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Revm Audit/Fuzzing

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Revm reached its next big milestone with audit/fuzzing.

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EOF Benefits

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Talking about what EOF brings to Ethereum and addressing questions around EOF inclusion.

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Posted June 9, 2024 by draganrakita ‐ 6 min read

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Zombie nodes of Ethereum

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What data the node would need to follow the chain tip? Is 220gb execution node possible in 2023?

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Parallel EVM claim

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How to verify claim of parallel execution

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Posted July 10, 2023 by draganrakita ‐ 9 min read

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2D UI beginner guide. Learn to rotate/translate/scale

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Post about 2d transformations that i did in my previous work life.

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Posted December 20, 2019 by draganrakita ‐ 9 min read

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2D UI beginner guide. Learn to rotate/translate/scale

+ +

Posted December 20, 2019 by draganrakita ‐ 9 min read

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+

Post about 2d transformations that i did in my previous work life.

+

Recently I had pleasure to make UI for standard rotate/scale/translate controls on element with some bounding box. I told myself: great! this will be easy! Lets google it, make some matrices and this will be quickly finished... must say that it took me a bit longer. Internet didn't help me with comprehensive solution or guide, I needed to stitch stuff together, and at the end of my frustration I got inspired to write this.

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I will give small intro about rotation and translation because they can be easily implemented and will focus my attention to scaling that made me warm around my hearth (or maybe that was my frustration). At the end you can find TLDR section with aggregated functions that we call.

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Transformations

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Transformation for 2D is constituted of tree things: Rotation, Scaling and Translation and all three things can be represented with one 2x3 matrix (but because of conformity that matrix multiplication give us we add some zero padding and use 3x3). When thinking about matrix most of times I see only a black box, nothing more, I know what I can do with them, and avoid manually setting things. Most important things that we need to take care when matrixing is the order on how we do transformation, it matters, Mfirst*Msecond is not same as Msecond*Mfirst and it depends if we are using row or column major matrices.

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Some info on our setup. Our original element (before transformations), has some size original_size, it is rectangular shape (or his bounding box is), it lies in first quadrant with starting position at coordinate beginning. And we are using row major order matrices (This means order of multiplication of matrices is from left to right Mfirst*Msecond) additionally, we are using these points:

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  • ref_point: point from where transformation is started for rotation/scale this is corner that we selected, for translation this is point where user clicked and started to drag our element.
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  • current_point: current mouse point, it is point where we want to rotate/scale/translate
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  • M : transformation matrix that was already applied on our element.
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  • anchor_point: other side from ref_point, (opposite corner or side). Needed for scaling.
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  • center_point : ref_point-anchor_point
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  • original_size: Original size of our element (For some systems, element can be normalized to (1,1), but for this example I think it is better to show how we are effected if there is size).
    • +
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Okay, lets get started from easy to hard:

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Translation

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Translation is most simple of them all, it moves point by specified vector, and nothing more. It is used in tandem with rotation or scaling to center already moved/rotated element, but this will be explained in due time. For our UI you take point when mouse is clicked ref_point. And take current point where mouse moved current_point. get diff and create translation matrix as this M=M*translation(current_point-ref_point) and voila, we are done.

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Rotation

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Rotation is little bit more complex (it has little bit more to do) but in same rank as translation. We need reference point ref_point for selected element. Rotation is usually, not to say always, done around element center, for this we need center_point. And lastly we have current_point. As you can guest we need to find the angle between these two vectors x=ref_point-center_point and y=current_point-center_point. After consulting internet we get this equation:angle = atan2(norm(cross(x,y)), dot(x,y)). With angle found we can call function for creating matrix, something like R=rotation(angle). Appending R to transformation matrix M is done with this simple but very used and important trick: We create another matrix of translation from elements center T=translation(center_point), and it's inverseTinv = inverse(T). We get matrix that we can use to append transformation to already present points Ra = Tinv*R*T and final transformation is M=M*Ra. Basically (with Tinv we just nullify translation, we then rotate our element around center and apply T to put it back into old position).

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Scale

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And we come to scaling, it is best part of this post (it has pictures) :D. We will gradually introducing few things that needs to be done in scaling, we will see how we handle rotation, and shift controls (shift is usually used for aspect ration lock).

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Naive Scale

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Nice, lets start with basic example where our element is not rotated or translated and we just want to scale it. We will use ref_point (corner or side usually), and its anchor_point and of course we will need current_point to tell us where we want to scale to. We calculate diff=current_point-anchor_point, get scale as s=scale(diff/element_size) and we are done, we have scale matrix that can add to our transformation.

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Okay, lets now look on example where we want to take top left corner ref_point ( you can follow picture below), in that case our anchor_point is positioned at bottom and if we want to scale it properly, to top and left. First difference from previous example is that we will need to move our object so that anchor_point is in (0.0) coordinate! We still need diff and we are calculating it same as before, but because now our axis are flipped, this is second difference, we need to reverse sign of diff_new=Vector(-diff.x,-diff.y). Note, reversing y is needed for top side ref_point and reversing x for left side ref_point. We get scale as s=scale(diff_new/element_size) . And final third difference from previous example is that after all this we need to take translation of anchor T=translate(anchor_point), calculate inverse Tinv=inverse(T) and bind it all together (from left to right) S=T*s*Tin.

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Scale

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As you can see diff vector is oriented to negative in reference to our axis, this is reason why we need to flip it, if we didn't do flipping you would get small scale when moving away from top left corner.

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This will all work just fine if element is not in any way rotated (Yey rotation!), with rotation we are now in bind how to calculate our diff and extract scale information. But, don't despair, we can use same trick as we did with rotation in a way that we will take current_position and inverse of current transformation matrix Minv = inverse(M) and get relative_position=Minv*current_position. Relative position now presents point relative to our original element. We get corner of original element as: original_anchor_point=original_corners[handler_id] (take care to select correct corner, it is probably jumbled up with rotation, I had something like handler_id to help me with that) and do same as we did in our last example, calculate diff as diff=relative_position-original_corners[handler_id], and if needed invert its axis. Calculate scale as s=scale(diff_new/element_original_size) and now similarly as previous scale example we need to move our original element to anchor before we do scaling, bear in mind that that translation represent anchor when our element is not transformation. We get T=translate(original_anchor_point) and its inverse Tinv and we get S=T*s*Tinv.

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That's great, but how to append scale in current matrix, when scale is something that is done before rotation and translation? We could always prepend scale to M, and this is only way to properly add scale, mostly because we are using S*R*T order. But how to get matrix to apply directly on already transformed points? Hah, just take Minv = inverse(M) and get transformation that we can append on present points as Sa=Minv*S*M, and final matrix is M=M*Sa.

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Shift scale

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Shift scale is scaling where aspect ration is not changed. This means that scale on both axis is equal and we need to choose which axis orientation we will take as primary. We could make it simple and depending on which corner_id is selected that take modulo of two and chose x or y scale, this will work but will be unintuitive. For better solution where depending on position of mouse relative to diagonal of element we will get smother transition between x and y orientation. See picture below:

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Naive Scale

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With transparent colors we can see zones where we want to take only x ( blue color) or take only y (marked with red). As noticeable our object is in original position that means our original_points is calculated same as in example with rotated object. Slope of diagonals that make these zones are calculated from original_size with equation line_slope = original_size.y/original_size.x . for second diagonal it is enough to just flip sign and we will get second slope. what we want to check is if point is in blue or red space and we can do that following if statement: (for abbreviate: op is original_point , ls is line_slope ): (op.y < ls**op.x && op.y > -ls**op.x) || (op.y > op.x**ls && op.y < -ls**op.x), and if this if statement is true do scale.y=scale.x if it is false do opposite. And lastly don't forget that when you are overriding one scale to not override its sign, in example from picture we are taking y scale and overriding x scale but we need to preserve x sign to properly scale our element x=sign(x)*abs(y).

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TLDR

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Summary of functions that were called throughout the text:

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Translation:

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M = M*translation(current_point-ref_point)
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+

Rotation:

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crp  =ref_point - center_point
+ccp = current_point - center_point
+angle = atan2(norm(cross(crp,ccp)), dot(crp,ccp))
+R = rotate(angle)
+T = translation(center_point)
+Tinv = inverse(T)
+Ra = Tinv*R*T	//diff that can be used to apply to already transformed points
+M = M*Ra
+
+

Scale:

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Minv = inverse(M)
+relative_position = Minv * current_position
+original_anchor_point = original_corners[handler_id]
+diff = relative_position - original_anchor_point
+s = scale(diff/element_original_size)
+T = translate(original_anchor_point)
+Tinv = inverse(T)
+S = T*s*Tinv
+Sa = Minv*S*M	//diff that can be used to apply to already transformed points
+M=M*Sa
+
+

Shift scale:

+
scale = (x,y)
+line_slope = original_size.y/original_size.x
+if (op.y < ls**op.x && op.y > -ls**op.x) || (op.y > op.x**ls && op.y < -ls**op.x) {
+	scale.y = x=sign(y)*abs(x)
+} else {
+	scale.x x=sign(x)*abs(y)
+}
+
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+ + + + + + + + + + + + + + + + + + + + +Parallel EVM claim | draganrakita + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Parallel EVM claim

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Posted July 10, 2023 by draganrakita ‐ 9 min read

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This post is not what you would expect, it is not about how to find the order and dependencies of transaction execution, as there are already a few approaches to this, first can be done with access lists (UTXO, Solana) and the main paper for the second approach is to brute force it with probabilistic execution aka Block-STM pioneered by Nova/Aptos, and some EVM type blockchain emulated this and gained good performance boost (Polygon PoS, Binance Chain both got similar performance).

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The idea is for the builder to (somehow) find the transactions that can be done in parallel (the great thing about this is that this can be considered as a black box and can evolve on its own) and share that claim in a form of the transaction DAG to other peers/validators, the builder will be rewarded for doing that correctly. And verifier needs to execute those transactions in parallel following that DAG and verify the integrity of that claim. We will talk about how to verify this claim (Split of builder and verifier in imho is a very powerful idea that is a little bit undervalued allows us cleaner system modelling).

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Until now I didn't find anything related to this and the topic seems a lot more interesting to explore. You don't need to increase your transaction size with an access list and you don't need to do expensive probabilistic execution (at least not for a lot of nodes), so verifiers have smaller work that they need to do but still fully consistently verify execution. And there is an additional benefit for archive sync that I will talk about later.

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Parallel claim verification creates a potential path to introduce parallel execution inside Ethereum as the focus would be not on finding parallel tx but just on making sure that there are no inconsistencies when given tx are run in parallel. This path is long and requires more research to fully comprehend the change. As this topic is complex I will introduce a few simple examples and slowly build it up to encompass a working solution. But even with that, there are still a lot of pending topics that need to be addressed for this to become integrated inside protocol (parallel gas aka multidimensional gas accounting for example).

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Algorithm explained

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All examples start from the point that we received a DAG of transaction and the builder claims that transaction can be done in parallel. We want to execute those transactions in parallel and be sure that the claim is correct and that there are no inconsistencies (data races) that can happen.

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Example 1: simple two parallel transactions

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We have two transactions that read/write to the same state (there is only one state that all of them share) and the update to that state is atomic. The example here is very simple but it allows us to set up some groundwork and initial ideas of what is checked.

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Graph

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For the sake of explaining we are simplifying state and seeing it as a list of accounts, these "accounts" can be a balance/nonce/code hash(code)/storage slot, it is just easier to reason and think about in simpler form.

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Additionally, we should consider both reads and writes of accounts as the same thing. This can be explored as a follow-up but for the first iteration, it is easier to omit this distinction. So this means that the transaction touched state consists of both reads and writes that this transaction did. And with this, having an account read from two different parallel transactions is considered invalid.

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Now, the idea here is that on every access of an account (read or write) to mark that account in the state as accessed by that transaction. This means that if account 0x01 is accessed by tx1 it will be marked as such and if tx2 tries to access account 0x1 we will notice that account is already marked and see that there is inconsistency and data race in place.

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So every "account" had additional information that represent the transaction that last touched it.

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Running transactions in parallel is more implementation detail and will depend on the programming language.

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Example 2: Chains, transactions dependencies.

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The second example is having a third transaction that depends on the first one.

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Graph

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This is the first example of dependent transactions andtx3 can access only accounts that are in the original state or touched by tx1, if both tx3 and tx2 access the same account this would make the parallelism claim invalid.

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This example show's us that marking of state can be done by chain ids that this tx belongs to and we would get the same outcome. Without this tx4 would need to check if the account state is original or marked by tx1 or marked by tx3 and that wouldn't be efficient. I will use the terms chain and transaction interchangeably.

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Example 3: Chain forks and joins

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Modelling dependency can be tricky but in parallel execution, there are only two synchronizations that can happen. And those are forks and joins and both of them can be seen in the picture.

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Graph

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There is one fork here, and can be seen in the example of tx1 that forks its state to chains of tx5 and tx3. This means that there is a dependency between tx5 and tx1, tx3 and tx1 but there are no dependencies on tx3 and tx5 and they can be run in parallel.

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The mechanism of marking the state works the same as in the first example. tx5 can now access the account of the original or tx1 or tx2 accounts if it accessed the state of tx3 or tx3 this would make parallel claim invalid.

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Example 4: Diamond pattern

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This is a good example that tests our initial mechanism of marking of accessed state.

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Graph

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All previous statements should be valid here.

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For example tx7 can only touch original state or tx1, tx2, tx3, tx4, tx5 but not tx6, and same with tx6 it can't touch state of tx7

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How to check marks

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Every transaction could have a list of previous dependent transactions, and when checking the mark inside the database we compare it if it is found inside that list.

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This list can be sorted so finding particular values can be done by binary search. The list size depends on the number of dependent transactions.

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miner fee

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The problem with the current setup is that transaction pays the fee of execution to the miner after a transaction is finished, this would mean that every transaction depends on its predecessor miner balance to update it with this transaction fee. A simple solution for this is to just move the fee balance increment of the miner at the end of the block after all transactions are executed. This is a small consensus change without a lot of general impact, but as said it is a "consensus change" for us to parallelize transactions with the DAG hints we need a different solution.

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This solution requires a lot of small things we need to make it consistent:

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  • we should mark those transactions in DAG that need miner information and make them dependent on all previous transactions.
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  • We need to have an additional atomic vector that is the size of the number of transactions and it will contain an increment of the miner balance, and it is updated async when transaction execution finishes.
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  • We need a hint when miner balance information is needed, this is only possible in a few situations. This hint or a flag should be checked against the flag set inside DAG to see that this information is done correctly: +
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    • Opcode BALANCE is called for a miner
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    • When the miner account is the contract and it transfers funds or calls SELFBALANCE opcode. We can be a little loose and say when the miner as a contract is called.
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    • If the miner account is an ordinary account and there is a transaction with the miner as a sender.
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  • After all transactions are executed apply the rest of the transaction fee to the miner.
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The solution requires a little bit of hoop jumping but it is possible to make and have verifiable parallel execution.

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Usage

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When live syncing, builders could potentially get more rewards if they find transactions that can be done in parallel. This would mean more throughput without state increase.

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On history sync, we can obtain DAGs from centralized sources, and if we have verifiable parallel execution we don't need to trust that DAG and can do verification of parallel execution on our own, if received DAG is not correct we can just fall back to serial execution. This can potentially speed up initial archive sync by X factor.

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Further works

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Split of reads and writes:

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This could make transactions ever more parallel.

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The main idea behind this is that chain that writes an account should be the only one that can read that account. And few more checks need to be done

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  • If the account is read by two chains but written in one it is considered a potential data race.
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  • For every account read we should append the transaction number, and on every write, we should check if those reads connect to the same chain and mark that account as written and clear read list. And if there is a transaction from a different chain/predecessor that has written an account before us this is making the claim invalid. This means that every account now has the last transactions that wrote it and the list of transactions that have read it.
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  • If we want to read the account we should first check if it is written by the predecessor, if it is not, this makes the claim invalid.
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This is optimization for this architecture.

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Gas calculation

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One of the pending things that need to be defined when parallel transactions are considered for inclusion. This probably can be done by some calculation on the DAG and its weight (gas).

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Separation of current gas accounting on CPU gas and disk io gas firstly specified in multidimentional EIP1559 is probably desirable, but not required. Gas calculation is always a sensitive topic as it can be abused if not done correctly.

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And as CPU cores are limited, we can have limitations on transaction DAG format.

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    Zombie nodes of Ethereum

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    Posted August 5, 2023 by draganrakita ‐ 9 min read

    + +

    + +

    Let us begin with a question that made me start this whole blog post:

    +

    What data the node would need to follow the chain tip?

    +

    And subquestion:

    +

    How big of the disk would you need for that data?

    +

    And the name of this node will be the Brain node (muhaaa)! Joking aside let's dive in:

    +

    Data

    +

    To answer the first question you would need

    +
      +
    • a plain state for executing new blocks
      • +
    • Merkle tree to verify state root
      • +
    • Little bit of history for the last 64blocks that are still not finalized, for block reorganization
      • +
    • Logs related to validator deposits to feed it to CL (Consensus Layer) node.
      • +
    +

    With this data, we would make a lean node and still be able to fully verify incoming blocks and sync with the CL node.

    +

    Disk size

    +

    Here is the data on the full archive mainnet node on block #17694321 on 14.07.2023, gained by executing ./reth db stats:

    +

    +

    The sizes of needed data are shown below:

    +
      +
    • 96.4gb Plain state: +
        +
      • 12.4gb PlainAccountState
        • +
      • 84gb PlainStorageState
        • +
      +
      • +
    • 118gb Merkle tree. +
        +
      • 16.1gb HashedAccount table
        • +
      • 75.4gb HashedStorage table
        • +
      • 22.6gb StorageTrie
        • +
      • 3.9gb AccountsTree
        • +
      +
      • +
    • I don't have the history size number for the last N blocks but it is in the range of a few gb. Will put it at 2gb.
      • +
    • For deposit events I don't have an exact number but for 856k deposit logs it is around a few gigs, let's say it is 2gb.
      • +
    +

    This would make the Brain node size around 218.4gb (what? just 220gb?)

    +

    Then why are current nodes from 700gb to 15tb?

    +

    It is complicated.

    +

    If you want to sync your node from the genesis (zero block) you would need to fetch blocks from somewhere and this is currently embedded inside the Ethereum p2p protocol this means most nodes have all past blocks and this increases the size so our first type of zombie nodes is Block Zombie node, but I am going too far, let's go over how do we get from 15tb to 220gb and various types of nodes found in present:

    +
      +
    • Type1 ( old archive node): It contains everything that Ethereum has, those nodes are called archive nodes and from Etherscan OpenEthereum and Geth archive are around 15TB for the current tip. This kind of archive node became unusable, they need months to sync and are just big.
      • +
    • Type2 (Erigon/Reth archive node): Formating state a little bit differently and omitting history Merkle tries (present trie is still there) you shave a lot of data and you get Erigon/Reth type archive node of ~2tb.
      • +
    • Type3 (full node): If you remove history state from the node you get full node, this is currently what Geth/Nethermind/Besu defaults to, size goes to 700gb-1tb
      • +
    • Type4 (brain node): If you shave history blocks and receipts you are getting that lean Brain node of 200-300gb. +
        +
      • There is even an intermediate between type3 and type4 where only blocks after 11M are saved as this is the time when CL deposit logs start to appear.
        • +
      +
      • +
    +

    Just to note here if all nodes switch to type4 then type2 nodes would need to get blocks from somewhere else as in essence history blocks would be unavailable over p2p. Infrastructure for this is still not being made but some of the Ethereum nodes by default don't save it. When infra gets made I assume in the future the main type of the node would be Type4.

    +

    The Horde

    +

    The question that zombie nodes want to solve is why would you need 1/2tb NVMe SSD when you can get 500gb and use a public repository for blocks and other data. It seems more logical. You can put your blocks to aws bucket or external disk, or if you need more resources you could clone parts of data to multiple servers, either way, it is more flexible.

    +

    This would make running a node more approachable to new people and would allow Providers/Researchers to run better suited types of the zombie node.

    +

    Or maybe the user care only for a particular event of a particular contract and want to have the ability to filter it, or maybe you want to run a lot of transactions on the newest state (Researcher use case), or you want to move the history state to a different place (provider use case) as you want to clone it or not want to be exposed to bandwidth expensive transaction broadcast. There are a lot of small use cases like this as every group of users focuses on something different.

    +

    Split have already happened with Consensus and Execution clients and imo I think the execution layer side needs to be split even more and this path seems unexplored (at least in the public).

    +

    Zombie types

    +

    The brain would be the main driver that does consensus checks and would push state changes to the zombies, zombies would consume brain data by being subscribed to it and would update its internal state.

    +

    Zombies consuming Brains (data) :)

    +

    I am assuming some kind of streaming from the brain to other zombies is needed, so the brain executes blocks that are received from CL and streams new blocks and state changes to subscribed zombies.

    +

    The problems here are the same as with CL and EL nodes, syncing and recovery become troublesome and they can get out of sync. For some of the zombie's nodes recovery is possible and data can be requested again, but for others, it is not and we need to execute blocks again to get them. So zombie nodes if they want to recover data need other zombie nodes, and if out of sync some of them would require braind to resend all history outputs of execution.

    +

    Block zombie node

    +

    This is the first zombie that I am mentioning, it contains blocks (headers, bodies and receipts) and all of those data are checkable with just a hash.

    +

    It will contain multiple things, ~500gb of blocks, ~200gb of receipts and around ~200gb for senders and tx hashes index. If blocks/receipts are available from Brain it will allow Brain to still respond to requests over the p2p network and maintain the same protocol that is presently used. The otherwise empty brain can use those blocks to do initial sync, it should be faster and more reliable.

    +

    This node could have slow disks as it just needs to save blocks of data and it becomes a lot easier to cache things as we can perceive blocks as a blob of data that we need to save.

    +

    The good thing is there could be one Block node and multiple Brain nodes, this would allow faster fetching of the blocks if the brain needs to initially sync. All brain nodes could push blocks to the Block zombie and the zombie would save them all (or the main chain that gets extended)

    +

    History state zombie node

    +

    history state ~900gb allows you to access the past state of Ethereum. At every block, you would be able to fetch the balance/nonce/code or storage of the account.

    +

    The good thing about this is that we can add multiple indexes to the data and even create an index zombie node from it as we could map every change of the account.

    +

    if you have a Block zombie node to get transactions you could use it to execute it and get transaction traces. It can be used to inspect past state both of accounts and storage.

    +

    State zombie node

    +

    Needed for MEV searchers and block builders that do a lot of simulations on the tip they emulate transactions to find the best block rewards or possible MEV, this is already done by a lot of people/groups in space behind the doors and it should be fun to bring that more to the public.

    +

    This node would need just the newest state that is around ~100gb

    +

    The use case of MEV and builders is different but they operate on the newest state so I just wanted to mention this type of node. Builders for example would need to state root from the Merkle trie but they can delegate that to the Brain node.

    +

    Some Future/Experimental zombie nodes

    +
      +
    • Logs Zombie +If you want to prune a lot of logs and save only ones that are of interest to you. Or have a custom indexer for it.
      • +
    • State proof zombie +With state expire already mentioned few times and something that is probably going to happen in future (Hard to tell when) this will be additional type of nodes that would allow transitioning accounts from expired to present state
      • +
    • Zombie aggregator +Aggregate all streamable data from the brain (or multiple brains) that would allow populating other zombie nodes.
      • +
    +

    And I assume there will be an even more variant.

    +

    Conclusion

    +

    As the Ethereum state grows over time it becomes unusable, we need to mitigate this to slow down the growth until proper mechanisms are in place. Dropping of intermediate history Merkle trie's was the first mitigation that was naturally done and the second wave that we see is the dropping of history blocks.

    +

    In the end, presenting this as a zombie/brain/horde way was fun :) but the main ideas behind this post are half educational half exploratory, it is defining the execution node types that we already have (it is just about what data they possess), and that we can get even smaller execution nodes, and exploring the notion that having additional nodes/zombies that bind to execution node can give us different interactions/benefits in the ecosystem as not all data are made equal.

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    EOF Benefits

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    Posted June 9, 2024 by draganrakita ‐ 6 min read

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    EOF (EVM Object Format) is a group of small EIPs that improve EVM. It introduced a new format of the bytecode and prepares EVM for the future.

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    Benefits

    +

    The value of EOF turns out to be hard to explain as it is not one thing, and with its history of being delayed on multiple forks and development/research that spaned for years and different versions across history, does not help with clarity.

    +

    The intention of this post is to summarize the list of benefits and explain them in one sentence:

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    Benefits:

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      +
    • EOF allows opcode gas pricing changes. +
        +
      • If gas pricing changes, legacy bytecode could act differently.
        • +
      • Removal of gas observability, means removal of the GAS opcode and removal of gas limit from CALL/DELEGATECALL/STATICCALL.
        • +
      • Allows l2 to change gas depending on their use case. Example of hash being overpriced in zk l2. EIP-7667: Raise gas costs of hash functions https://eips.ethereum.org/EIPS/eip-7667
        • +
      • If present EOF was enabled before berlin, this EIP would not be needed and access list could be removed. https://eips.ethereum.org/EIPS/eip-2930
        • +
      +
      • +
    • Bytecode are smaller, and it reduces the gas usage +
        +
      • Early numbers show reduction in both the code/initcode size and reduction in gas usage: https://notes.ethereum.org/@ipsilon/solidity_eof_poc
        • +
      • Uniswap-v3 deployment 6.5% smaller initcode and 6.5% smaller deployed code
        • +
      • deploy UniswapV3Factory take: ~14% less gas and call runTest : ~9% less gas.
        • +
      • ENS DNSRegistrar deployment ~6% smaller initcode and ~1.5% smaller deployed code
        • +
      • ENS call proveAndClaim: takes ~10% less gas for EOF.
        • +
      +
      • +
    • EOF allows bytecode transformations and upgradability. +
        +
      • Removal of code observability means removal of PC, CREATE/CREATE2, EXTCODEHASH, EXTCODESIZE, EXTCODECOPY, CODESIZE, and CODECOPY opcodes.
        • +
      • Legacy contracts would break if code is changed.
        • +
      • This would allow us to mold EOF bytecode in any form when verkle comes.
        • +
      +
      • +
    • EOF enables opcode immediates +
        +
      • opens possibility of SWAPN, DUPN, and EXCHANGE opcodes
        • +
      • This allows solidity more freedom on stack size. It removed stack too deep problem in solidity.
        • +
      +
      • +
    • EOF removes expensive jumpdest analysis +
        +
      • While in Reth, the analysis is saved with a bytecode, this is not case for other clients. The removal of jumpdest analysis before contract execution increases speed.
        • +
      • With analysis removed we can in future increment max bytecode size.
        • +
      +
      • +
    • Static analysis becomes easier +
        +
      • Subroutines enforce more structured control flow. This makes fuzzing more effective and linear-time static analysis possible.
        • +
      • Data and code are separated and easier to reason about.
        • +
      • EOF bytecode can be compiled to faster bytecode.
        • +
      • EOF bytecode can be compiled to machine code.
        • +
      +
      • +
    • Future proofing EVM +
        +
      • The version and structure of bytecode allow its extensibility. This is especially useful for L2 and standardization.
        • +
      • One example of this is EIP-7701: Native Account Abstraction with EOF that adds new header section.
        • +
      +
      • +
    • Address space expansion. +
        +
      • New EXT*CALL opcodes prepare Ethereum for future address expansion by requiring for address field to be padded with zeros. When Ethereum do decide to extend address space EOF will have prepared those changes.
        • +
      +
      • +
    +

    Integration with current EVM

    +

    An important question for developers is the effort needed to implement the change, the cost of testing, and the cost of maintaining those opcodes.

    +

    New opcodes do not clash with legacy opcodes, and EOF cause of verification does not touch deprecated opcodes.

    +

    Encoding and decoding of EOF can be fuzzed. Validation is something new, around ~500 loc in Revm but has a lot of edge cases that need to be covered and need focus to do it right across the implementations.

    +

    Creation TX is used as a carrier of EOF bytecode, it acts similar to EOFCREATE but requests validation before execution.

    +

    Most opcodes are very simple:

    +
      +
    • EXTCALL (0xf8), EXTDELEGATECALL (0xf9), EXTSTATICCALL (0xfb) +
        +
      • Have same blueprint as deprecated CALLs but with gas_limit and memory output fields removed.
        • +
      • Before RETURNDATALOAD (introduced in a very early fork), the memory output of a CALL had to be set before doing a CALL opcode. This did not allow for dynamic outputs.
        • +
      +
      • +
    • EOFCREATE and RETURNCONTRACT +
        +
      • are new for EOF and require a special cases to be handled.
        • +
      • EOFCREATE introduces new creation call.
        • +
      +
      • +
    • EXCHANGE (0xe8), SWAPN (0xe7), DUPN (0xe6), DATACOPY (0xd3), DATASIZE (0xd2), DATALOADN (0xd1), DATALOAD (0xd0), RJUMP (0xe0), RJUMPI (0xe1), RJUMPV (0xe2), RETURNDATALOAD +
        +
      • Have simple logic and mostly require 10–20 lines per opcode to implement. Does not have a lot of edge cases that need to be covered.
        • +
      +
      • +
    • CALLF (0xe3), RETF (0xe4), and JUMPF (0xe5) +
        +
      • Require subroutine stack, and stack validation, those would require 20-30 loc as measure of complexity.
        • +
      +
      • +
    +

    They require around 2/3 months for one developer. Work on testing has already been started. There are currently around ~2000 handwritten tests for validation and work is beeing done on statetests.

    +

    Changes are localized to the EVM, so integration with the rest of the client depends on the client architecture and where you would save bytecode.

    +

    EXTCODESIZE and EXTCODEHASH need to be aware if the account is EOF or not and return a predefined value (size and hash of 0xEF00), this could slightly change how integration for the client can be done. One of ideas is to save is_eof flag inside plain accounts table to skip loading of the bytecode when any of EXTCODE type of opcodes are called.

    +

    Effect on L2

    +

    Biggest question that is asked is why don’t L2 implement this change? Should we close the EVM improvements on Ethereum L1?

    +

    The reality is that L2 are not ready, and not just that, they do not have a platform that would help with integration of those innovations. Versioning of bytecode helps with that as it helps build that platform that L2 can use, removal of code observability helps mitigating upgredability issues, and gas changes help ZK L2 to remove the ddos vector of gas bombs (for example, a hash in zk is worth a lot more).

    +

    More importantly, EOF is not just a format, it requires languages (solidity/vyper/huff) to be on board, and it requires tooling to support it so it can be usable. It requires an ecosystem to use it, and this format gives L2 more stability to innovate on top of it.

    +

    Downside, legacy bytecode still exists

    +

    This is a question that is often asked. Legacy bytecode will always be here, if we don’t offer a alternative, we are going to be stuck with it. With alternative formated bytecodes, there is a future where we could transition and remove legacy bytecodes when state expire happens.

    +

    In the end

    +

    EOF is not the next big shiny thing, it is the maintenance EIP that fixes problems left from the initial EVM version and there is no other way to solve those other than breaking it. It is necessary for further development and future proofing of EVM.

    +
    +

    Post was originally shared in hackmd form with Ethereum core devs. This was done a day before Execution Layer Meeting 189. On that ACD call, clients officially included EOF in Prague hardfork.

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    Revm Audit/Fuzzing

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    Posted June 25, 2024 by draganrakita ‐ 4 min read

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    I am thrilled that this milestone has been reached!

    +

    Revm is one of the leading EVMs in the Ethereum ecosystem. Many major projects and companies depend on it, and the number is growing, especially since the development of Reth. Revm has solidified its position as a foundational component of the Ethereum ecosystem, and its significance continues to rise.

    +

    It is used by clients, block builders, tooling, and many searchers and hobbyists. Revm's stability has been stellar and its execution consistent, leading to heavy reliance on it. Its speed and flexibility make it highly desirable for integration. With no_std support, it fits nicely in the zkVM world, where Rust is predominantly used. There are many requirements that Revm fulfills.

    +

    There was already a lot of confidence in its consensus correctness, and bugs were squashed, which helped develop the client Reth on top of it. We wanted to increase that confidence even further and have more assurance. The next step for this was an audit.

    +

    Guido Vranken, a top Ethereum bug hunter, was hired to stress test Revm. With his expertise in fuzzing and finding consensus bugs, it has been a pleasure to have him on board.

    +

    Despite its already extensive usage, Guido managed to find a few undiscovered consensus issues and created a tool for fuzzing that we can reuse.

    +

    When we discussed how to conduct the audit, the idea of involving others arose: "If there are multiple parties who rely on Revm, then it may make sense to have them co-fund it."" So we did exactly that and made this audit community-driven and sponsored by six companies that use Revm in various ways.

    +
      +
    • +

      Paradigm +Foundry, which Paradigm created, was started in the same month as Revm. After some time, the Foundry team switched to Revm, making it the first big project to use it. +They were the first to see the potential and are a major reason for Revm's popularity today. +More than a year later, an idea was born to build Reth as there was already a working EVM. This is when I finally joined Paradigm, as I enjoyed working on client code. +I am really grateful to Georgios and Paradigm for their support and for later hiring me to work on Reth and continue working on Revm. The Reth team is amazing, and it is my privilege to work with them!

      +
      • +
    • +

      Ethereum Foundation +We wouldn’t be here without Ethereum, so it made sense to include the Ethereum Foundation. After the audit of Revm and Reth is finished, Reth is scheduled to be included in the EF Bug Bounty along with its dependencies, so any bugs found within Revm that has an impact on the Ethereum network will be receiving rewards through the Ethereum Foundation Bug Bounty Program.

      +
      • +
    • +

      Optimism +The Optimism stack and their team are amazing! Revm has support for Optimism changes, which made me think about the future of Revm and support for other EVM chains. The RetroPGF mechanism is a great way to incentivize builders to build even more.

      +
      • +
    • +

      Nomic Foundation +he builders of Hardhat recently enabled their EDR (Ethereum Development Runtime) framework, which uses Revm at its core. They recognized Revm's potential and started transitioning to it years ago.

      +
      • +
    • +

      Risc0 +Risc0 launched Zeth, their zkEVM system, which is leveraging Revm for execution. It is an awesome company that uses RISC-V for universal zk execution.

      +
      • +
    • +

      Taiko +At one of the conferences, they approached me about Revm audit efforts and offered to help, so when the list was made, they were included. They care about the lower stack and are one of the first projects to make the 1% Pledge for Protocol Guild. I appreciate their support.

      +
      • +
    +

    There are many more projects that depend on Revm, and I am glad that this is the case, as it means the library is doing something good.

    +

    This is a huge milestone for Revm and me. When I started the project, I didn't imagine it would have such an impact on Ethereum. I appreciate the community, as without it, this would not be possible. I am committed to fulfilling the expectations and obligations of the users who depend on it.

    +

    The work continues. The future is primarily focused on two things:

    +
      +
    1. The next hard fork that includes EOF (EVM Object Format)
      2. +
    2. Revm endgame, but that is a topic for another post.
      3. +
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    Post was originally shared in hackmd and twitted here.

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    Revm Audit/Fuzzing

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    Revm reached its next big milestone with audit/fuzzing.

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    Posted June 25, 2024 by draganrakita ‐ 4 min read

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    EOF Benefits

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    Talking about what EOF brings to Ethereum and addressing questions around EOF inclusion.

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    Posted June 9, 2024 by draganrakita ‐ 6 min read

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    Zombie nodes of Ethereum

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    What data the node would need to follow the chain tip? Is 220gb execution node possible in 2023?

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    Posted August 5, 2023 by draganrakita ‐ 9 min read

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    Parallel EVM claim

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    How to verify claim of parallel execution

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    Posted July 10, 2023 by draganrakita ‐ 9 min read

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    2D UI beginner guide. Learn to rotate/translate/scale

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    Post about 2d transformations that i did in my previous work life.

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    Posted December 20, 2019 by draganrakita ‐ 9 min read

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a/public/elasticlunr.min.js b/public/elasticlunr.min.js new file mode 100644 index 0000000..79dad65 --- /dev/null +++ b/public/elasticlunr.min.js @@ -0,0 +1,10 @@ +/** + * elasticlunr - http://weixsong.github.io + * Lightweight full-text search engine in Javascript for browser search and offline search. - 0.9.6 + * + * Copyright (C) 2017 Oliver Nightingale + * Copyright (C) 2017 Wei Song + * MIT Licensed + * @license + */ +!function(){function e(e){if(null===e||"object"!=typeof e)return e;var t=e.constructor();for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n]);return t}var t=function(e){var n=new t.Index;return n.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),e&&e.call(n,n),n};t.version="0.9.5",lunr=t,t.utils={},t.utils.warn=function(e){return function(t){e.console&&console.warn&&console.warn(t)}}(this),t.utils.toString=function(e){return void 0===e||null===e?"":e.toString()},t.EventEmitter=function(){this.events={}},t.EventEmitter.prototype.addListener=function(){var 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zs9$;O-s;gyf=6%l>9SS<=@@SjuEZ(n1`>@$U+tvAIy7l!$a~I#5f4Xsbgzi5MY>*L zK386{;w>qCN7d`0AdyGQbb^5PQbr6}+309IKp-m*$9P9hSV=Kp%FrEV(WoijmE#~J i40u#QdEOH1>H~8NYSyM1 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +draganrakita | draganrakita + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +     + + + +
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    +

    Welcome to my blog!

    +
    +
    +

    I write about core blockchain (mostly Ethereum) and rust.

    + Blog posts +

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    + + + + + + + + + + + + + + + + + diff --git a/public/index.js b/public/index.js new file mode 100644 index 0000000..8c87d5f --- /dev/null +++ b/public/index.js @@ -0,0 +1,146 @@ +var suggestions = document.getElementById('suggestions'); +var userinput = document.getElementById('userinput'); + +document.addEventListener('keydown', inputFocus); + +function inputFocus(e) { + + if (e.keyCode === 191 ) { + e.preventDefault(); + userinput.focus(); + } + + if (e.keyCode === 27 ) { + userinput.blur(); + suggestions.classList.add('d-none'); + } + +} + +document.addEventListener('click', function(event) { + + var isClickInsideElement = suggestions.contains(event.target); + + if (!isClickInsideElement) { + suggestions.classList.add('d-none'); + } + +}); + +/* +Source: + - https://dev.to/shubhamprakash/trap-focus-using-javascript-6a3 +*/ + +document.addEventListener('keydown',suggestionFocus); + +function suggestionFocus(e){ + + const focusableSuggestions= suggestions.querySelectorAll('a'); + const focusable= [...focusableSuggestions]; + const index = focusable.indexOf(document.activeElement); + + let nextIndex = 0; + + if (e.keyCode === 38) { + e.preventDefault(); + nextIndex= index > 0 ? index-1 : 0; + focusableSuggestions[nextIndex].focus(); + } + else if (e.keyCode === 40) { + e.preventDefault(); + nextIndex= index+1 < focusable.length ? index+1 : index; + focusableSuggestions[nextIndex].focus(); + } + +} + + +/* +Source: + - https://github.com/nextapps-de/flexsearch#index-documents-field-search + - https://raw.githack.com/nextapps-de/flexsearch/master/demo/autocomplete.html +*/ + +(function(){ + + var index = new FlexSearch({ + preset: 'score', + cache: true, + doc: { + id: 'id', + field: [ + 'title', + 'description', + 'content', + ], + store: [ + 'href', + 'title', + 'description', + ], + }, + }); + + var docs = [ + {{ range $index, $page := (where .Site.Pages "Section" "docs") -}} + { + id: {{ $index }}, + href: "{{ .RelPermalink | relURL }}", + title: {{ .Title | jsonify }}, + description: {{ .Params.description | jsonify }}, + content: {{ .Content | jsonify }} + }, + {{ end -}} + ]; + + index.add(docs); + + userinput.addEventListener('input', show_results, true); + suggestions.addEventListener('click', accept_suggestion, true); + + function show_results(){ + + var value = this.value; + var results = index.search(value, 5); + var entry, childs = suggestions.childNodes; + var i = 0, len = results.length; + + suggestions.classList.remove('d-none'); + + results.forEach(function(page) { + + entry = document.createElement('div'); + + entry.innerHTML = ''; + + a = entry.querySelector('a'), + t = entry.querySelector('span:first-child'), + d = entry.querySelector('span:nth-child(2)'); + + a.href = page.href; + t.textContent = page.title; + d.textContent = page.description; + + suggestions.appendChild(entry); + + }); + + while(childs.length > len){ + + suggestions.removeChild(childs[i]) + } + + } + + function accept_suggestion(){ + + while(suggestions.lastChild){ + + suggestions.removeChild(suggestions.lastChild); + } + + return false; + } + +}()); diff --git a/public/js/main.js b/public/js/main.js new file mode 100644 index 0000000..2fbc228 --- /dev/null +++ b/public/js/main.js @@ -0,0 +1,14 @@ +// Set darkmode +document.getElementById('mode').addEventListener('click', () => { + + document.body.classList.toggle('dark'); + localStorage.setItem('theme', document.body.classList.contains('dark') ? 'dark' : 'light'); + +}); + +// enforce local storage setting but also fallback to user-agent preferences +if (localStorage.getItem('theme') === 'dark' || (!localStorage.getItem('theme') && window.matchMedia("(prefers-color-scheme: dark)").matches)) { + + document.body.classList.add('dark'); + +} diff --git a/public/js/search.js b/public/js/search.js new file mode 100644 index 0000000..7c5c745 --- /dev/null +++ b/public/js/search.js @@ -0,0 +1,317 @@ +var suggestions = document.getElementById('suggestions'); +var userinput = document.getElementById('userinput'); + +document.addEventListener('keydown', inputFocus); + +function inputFocus(e) { + + if (e.keyCode === 191 + && document.activeElement.tagName !== "INPUT" + && document.activeElement.tagName !== "TEXTAREA") { + e.preventDefault(); + userinput.focus(); + } + + if (e.keyCode === 27 ) { + userinput.blur(); + suggestions.classList.add('d-none'); + } + +} + +document.addEventListener('click', function(event) { + + var isClickInsideElement = suggestions.contains(event.target); + + if (!isClickInsideElement) { + suggestions.classList.add('d-none'); + } + +}); + +/* +Source: + - https://dev.to/shubhamprakash/trap-focus-using-javascript-6a3 +*/ + +document.addEventListener('keydown',suggestionFocus); + +function suggestionFocus(e){ + const focusableSuggestions= suggestions.querySelectorAll('a'); + if (suggestions.classList.contains('d-none') + || focusableSuggestions.length === 0) { + return; + } + const focusable= [...focusableSuggestions]; + const index = focusable.indexOf(document.activeElement); + + let nextIndex = 0; + + if (e.keyCode === 38) { + e.preventDefault(); + nextIndex= index > 0 ? index-1 : 0; + focusableSuggestions[nextIndex].focus(); + } + else if (e.keyCode === 40) { + e.preventDefault(); + nextIndex= index+1 < focusable.length ? index+1 : index; + focusableSuggestions[nextIndex].focus(); + } + +} + +/* +Source: + - https://github.com/nextapps-de/flexsearch#index-documents-field-search + - https://raw.githack.com/nextapps-de/flexsearch/master/demo/autocomplete.html + - http://elasticlunr.com/ + - https://github.com/getzola/zola/blob/master/docs/static/search.js +*/ +(function(){ + var index = elasticlunr.Index.load(window.searchIndex); + userinput.addEventListener('input', show_results, true); + suggestions.addEventListener('click', accept_suggestion, true); + + function show_results(){ + var value = this.value.trim(); + var options = { + bool: "OR", + fields: { + title: {boost: 2}, + body: {boost: 1}, + } + }; + var results = index.search(value, options); + + var entry, childs = suggestions.childNodes; + var i = 0, len = results.length; + var items = value.split(/\s+/); + suggestions.classList.remove('d-none'); + + results.forEach(function(page) { + if (page.doc.body !== '') { + entry = document.createElement('div'); + + entry.innerHTML = ''; + + a = entry.querySelector('a'), + t = entry.querySelector('span:first-child'), + d = entry.querySelector('span:nth-child(2)'); + a.href = page.ref; + t.textContent = page.doc.title; + d.innerHTML = makeTeaser(page.doc.body, items); + + suggestions.appendChild(entry); + } + }); + + while(childs.length > len){ + suggestions.removeChild(childs[i]) + } + + } + + function accept_suggestion(){ + + while(suggestions.lastChild){ + + suggestions.removeChild(suggestions.lastChild); + } + + return false; + } + + // Taken from mdbook + // The strategy is as follows: + // First, assign a value to each word in the document: + // Words that correspond to search terms (stemmer aware): 40 + // Normal words: 2 + // First word in a sentence: 8 + // Then use a sliding window with a constant number of words and count the + // sum of the values of the words within the window. Then use the window that got the + // maximum sum. If there are multiple maximas, then get the last one. + // Enclose the terms in . + function makeTeaser(body, terms) { + var TERM_WEIGHT = 40; + var NORMAL_WORD_WEIGHT = 2; + var FIRST_WORD_WEIGHT = 8; + var TEASER_MAX_WORDS = 30; + + var stemmedTerms = terms.map(function (w) { + return elasticlunr.stemmer(w.toLowerCase()); + }); + var termFound = false; + var index = 0; + var weighted = []; // contains elements of ["word", weight, index_in_document] + + // split in sentences, then words + var sentences = body.toLowerCase().split(". "); + for (var i in sentences) { + var words = sentences[i].split(/[\s\n]/); + var value = FIRST_WORD_WEIGHT; + for (var j in words) { + + var word = words[j]; + + if (word.length > 0) { + for (var k in stemmedTerms) { + if (elasticlunr.stemmer(word).startsWith(stemmedTerms[k])) { + value = TERM_WEIGHT; + termFound = true; + } + } + weighted.push([word, value, index]); + value = NORMAL_WORD_WEIGHT; + } + + index += word.length; + index += 1; // ' ' or '.' if last word in sentence + } + + index += 1; // because we split at a two-char boundary '. ' + } + + if (weighted.length === 0) { + if (body.length !== undefined && body.length > TEASER_MAX_WORDS * 10) { + return body.substring(0, TEASER_MAX_WORDS * 10) + '...'; + } else { + return body; + } + } + + var windowWeights = []; + var windowSize = Math.min(weighted.length, TEASER_MAX_WORDS); + // We add a window with all the weights first + var curSum = 0; + for (var i = 0; i < windowSize; i++) { + curSum += weighted[i][1]; + } + windowWeights.push(curSum); + + for (var i = 0; i < weighted.length - windowSize; i++) { + curSum -= weighted[i][1]; + curSum += weighted[i + windowSize][1]; + windowWeights.push(curSum); + } + + // If we didn't find the term, just pick the first window + var maxSumIndex = 0; + if (termFound) { + var maxFound = 0; + // backwards + for (var i = windowWeights.length - 1; i >= 0; i--) { + if (windowWeights[i] > maxFound) { + maxFound = windowWeights[i]; + maxSumIndex = i; + } + } + } + + var teaser = []; + var startIndex = weighted[maxSumIndex][2]; + for (var i = maxSumIndex; i < maxSumIndex + windowSize; i++) { + var word = weighted[i]; + if (startIndex < word[2]) { + // missing text from index to start of `word` + teaser.push(body.substring(startIndex, word[2])); + startIndex = word[2]; + } + + // add around search terms + if (word[1] === TERM_WEIGHT) { + teaser.push(""); + } + + startIndex = word[2] + word[0].length; + // Check the string is ascii characters or not + var re = /^[\x00-\xff]+$/ + if (word[1] !== TERM_WEIGHT && word[0].length >= 12 && !re.test(word[0])) { + // If the string's length is too long, it maybe a Chinese/Japance/Korean article + // if using substring method directly, it may occur error codes on emoji chars + var strBefor = body.substring(word[2], startIndex); + var strAfter = substringByByte(strBefor, 12); + teaser.push(strAfter); + } else { + teaser.push(body.substring(word[2], startIndex)); + } + + if (word[1] === TERM_WEIGHT) { + teaser.push(""); + } + } + teaser.push("…"); + return teaser.join(""); + } +}()); + + +// Get substring by bytes +// If using JavaScript inline substring method, it will return error codes +// Source: https://www.52pojie.cn/thread-1059814-1-1.html +function substringByByte(str, maxLength) { + var result = ""; + var flag = false; + var len = 0; + var length = 0; + var length2 = 0; + for (var i = 0; i < str.length; i++) { + var code = str.codePointAt(i).toString(16); + if (code.length > 4) { + i++; + if ((i + 1) < str.length) { + flag = str.codePointAt(i + 1).toString(16) == "200d"; + } + } + if (flag) { + len += getByteByHex(code); + if (i == str.length - 1) { + length += len; + if (length <= maxLength) { + result += str.substr(length2, i - length2 + 1); + } else { + break + } + } + } else { + if (len != 0) { + length += len; + length += getByteByHex(code); + if (length <= maxLength) { + result += str.substr(length2, i - length2 + 1); + length2 = i + 1; + } else { + break + } + len = 0; + continue; + } + length += getByteByHex(code); + if (length <= maxLength) 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I told myself: great! this will be easy! Lets google it, make some matrices and this will be quickly finished... must say that it took me a bit longer. Internet didn't help me with comprehensive solution or guide, I needed to stitch stuff together, and at the end of my frustration I got inspired to write this.\nI will give small intro about rotation and translation because they can be easily implemented and will focus my attention to scaling that made me warm around my hearth (or maybe that was my frustration). At the end you can find TLDR section with aggregated functions that we call.\nTransformations\nTransformation for 2D is constituted of tree things: Rotation, Scaling and Translation and all three things can be represented with one 2x3 matrix (but because of conformity that matrix multiplication give us we add some zero padding and use 3x3). When thinking about matrix most of times I see only a black box, nothing more, I know what I can do with them, and avoid manually setting things. Most important things that we need to take care when matrixing is the order on how we do transformation, it matters, Mfirst*Msecond is not same as Msecond*Mfirst and it depends if we are using row or column major matrices.\nSome info on our setup. Our original element (before transformations), has some size original_size, it is rectangular shape (or his bounding box is), it lies in first quadrant with starting position at coordinate beginning. And we are using row major order matrices (This means order of multiplication of matrices is from left to right Mfirst*Msecond) additionally, we are using these points:\n\nref_point: point from where transformation is started for rotation/scale this is corner that we selected, for translation this is point where user clicked and started to drag our element.\ncurrent_point: current mouse point, it is point where we want to rotate/scale/translate\nM : transformation matrix that was already applied on our element.\nanchor_point: other side from ref_point, (opposite corner or side). Needed for scaling.\ncenter_point : ref_point-anchor_point\noriginal_size: Original size of our element (For some systems, element can be normalized to (1,1), but for this example I think it is better to show how we are effected if there is size).\n\nOkay, lets get started from easy to hard:\nTranslation\nTranslation is most simple of them all, it moves point by specified vector, and nothing more. It is used in tandem with rotation or scaling to center already moved/rotated element, but this will be explained in due time. For our UI you take point when mouse is clicked ref_point. And take current point where mouse moved current_point. get diff and create translation matrix as this M=M*translation(current_point-ref_point) and voila, we are done.\nRotation\nRotation is little bit more complex (it has little bit more to do) but in same rank as translation. We need reference point ref_point for selected element. Rotation is usually, not to say always, done around element center, for this we need center_point. And lastly we have current_point. As you can guest we need to find the angle between these two vectors x=ref_point-center_point and y=current_point-center_point. After consulting internet we get this equation:angle = atan2(norm(cross(x,y)), dot(x,y)). With angle found we can call function for creating matrix, something like R=rotation(angle). Appending R to transformation matrix M is done with this simple but very used and important trick: We create another matrix of translation from elements center T=translation(center_point), and it's inverseTinv = inverse(T). We get matrix that we can use to append transformation to already present points Ra = Tinv*R*T and final transformation is M=M*Ra. Basically (with Tinv we just nullify translation, we then rotate our element around center and apply T to put it back into old position).\nScale\nAnd we come to scaling, it is best part of this post (it has pictures) :D. We will gradually introducing few things that needs to be done in scaling, we will see how we handle rotation, and shift controls (shift is usually used for aspect ration lock).\n\nNice, lets start with basic example where our element is not rotated or translated and we just want to scale it. We will use ref_point (corner or side usually), and its anchor_point and of course we will need current_point to tell us where we want to scale to. We calculate diff=current_point-anchor_point, get scale as s=scale(diff/element_size) and we are done, we have scale matrix that can add to our transformation.\nOkay, lets now look on example where we want to take top left corner ref_point ( you can follow picture below), in that case our anchor_point is positioned at bottom and if we want to scale it properly, to top and left. First difference from previous example is that we will need to move our object so that anchor_point is in (0.0) coordinate! We still need diff and we are calculating it same as before, but because now our axis are flipped, this is second difference, we need to reverse sign of diff_new=Vector(-diff.x,-diff.y). Note, reversing y is needed for top side ref_point and reversing x for left side ref_point. We get scale as s=scale(diff_new/element_size) . And final third difference from previous example is that after all this we need to take translation of anchor T=translate(anchor_point), calculate inverse Tinv=inverse(T) and bind it all together (from left to right) S=T*s*Tin.\n\nAs you can see diff vector is oriented to negative in reference to our axis, this is reason why we need to flip it, if we didn't do flipping you would get small scale when moving away from top left corner.\nThis will all work just fine if element is not in any way rotated (Yey rotation!), with rotation we are now in bind how to calculate our diff and extract scale information. But, don't despair, we can use same trick as we did with rotation in a way that we will take current_position and inverse of current transformation matrix Minv = inverse(M) and get relative_position=Minv*current_position. Relative position now presents point relative to our original element. We get corner of original element as: original_anchor_point=original_corners[handler_id] (take care to select correct corner, it is probably jumbled up with rotation, I had something like handler_id to help me with that) and do same as we did in our last example, calculate diff as diff=relative_position-original_corners[handler_id], and if needed invert its axis. Calculate scale as s=scale(diff_new/element_original_size) and now similarly as previous scale example we need to move our original element to anchor before we do scaling, bear in mind that that translation represent anchor when our element is not transformation. We get T=translate(original_anchor_point) and its inverse Tinv and we get S=T*s*Tinv.\nThat's great, but how to append scale in current matrix, when scale is something that is done before rotation and translation? We could always prepend scale to M, and this is only way to properly add scale, mostly because we are using S*R*T order. But how to get matrix to apply directly on already transformed points? Hah, just take Minv = inverse(M) and get transformation that we can append on present points as Sa=Minv*S*M, and final matrix is M=M*Sa.\nShift scale\nShift scale is scaling where aspect ration is not changed. This means that scale on both axis is equal and we need to choose which axis orientation we will take as primary. We could make it simple and depending on which corner_id is selected that take modulo of two and chose x or y scale, this will work but will be unintuitive. For better solution where depending on position of mouse relative to diagonal of element we will get smother transition between x and y orientation. See picture below:\n\nWith transparent colors we can see zones where we want to take only x ( blue color) or take only y (marked with red). As noticeable our object is in original position that means our original_points is calculated same as in example with rotated object. Slope of diagonals that make these zones are calculated from original_size with equation line_slope = original_size.y/original_size.x . for second diagonal it is enough to just flip sign and we will get second slope. what we want to check is if point is in blue or red space and we can do that following if statement: (for abbreviate: op is original_point , ls is line_slope ): (op.y < ls**op.x && op.y > -ls**op.x) || (op.y > op.x**ls && op.y < -ls**op.x), and if this if statement is true do scale.y=scale.x if it is false do opposite. And lastly don't forget that when you are overriding one scale to not override its sign, in example from picture we are taking y scale and overriding x scale but we need to preserve x sign to properly scale our element x=sign(x)*abs(y).\nTLDR\nSummary of functions that were called throughout the text:\nTranslation:\n\nRotation:\n\nScale:\n\nShift scale:\n\n","id":"https://rakita.github.io/blog/blog/001-2d-transformations/","title":"2D UI beginner guide. Learn to rotate/translate/scale"},"https://rakita.github.io/blog/blog/002-parallel-evm-claim/":{"body":"This post is not what you would expect, it is not about how to find the order and dependencies of transaction execution, as there are already a few approaches to this, first can be done with access lists (UTXO, Solana) and the main paper for the second approach is to brute force it with probabilistic execution aka Block-STM pioneered by Nova/Aptos, and some EVM type blockchain emulated this and gained good performance boost (Polygon PoS, Binance Chain both got similar performance).\nThe idea is for the builder to (somehow) find the transactions that can be done in parallel (the great thing about this is that this can be considered as a black box and can evolve on its own) and share that claim in a form of the transaction DAG to other peers/validators, the builder will be rewarded for doing that correctly. And verifier needs to execute those transactions in parallel following that DAG and verify the integrity of that claim. We will talk about how to verify this claim (Split of builder and verifier in imho is a very powerful idea that is a little bit undervalued allows us cleaner system modelling).\nUntil now I didn't find anything related to this and the topic seems a lot more interesting to explore. You don't need to increase your transaction size with an access list and you don't need to do expensive probabilistic execution (at least not for a lot of nodes), so verifiers have smaller work that they need to do but still fully consistently verify execution. And there is an additional benefit for archive sync that I will talk about later.\nParallel claim verification creates a potential path to introduce parallel execution inside Ethereum as the focus would be not on finding parallel tx but just on making sure that there are no inconsistencies when given tx are run in parallel. This path is long and requires more research to fully comprehend the change. As this topic is complex I will introduce a few simple examples and slowly build it up to encompass a working solution. But even with that, there are still a lot of pending topics that need to be addressed for this to become integrated inside protocol (parallel gas aka multidimensional gas accounting for example).\nAlgorithm explained\nAll examples start from the point that we received a DAG of transaction and the builder claims that transaction can be done in parallel. We want to execute those transactions in parallel and be sure that the claim is correct and that there are no inconsistencies (data races) that can happen.\nExample 1: simple two parallel transactions\nWe have two transactions that read/write to the same state (there is only one state that all of them share) and the update to that state is atomic. The example here is very simple but it allows us to set up some groundwork and initial ideas of what is checked.\n\nGraph\nFor the sake of explaining we are simplifying state and seeing it as a list of accounts, these \"accounts\" can be a balance/nonce/code hash(code)/storage slot, it is just easier to reason and think about in simpler form.\nAdditionally, we should consider both reads and writes of accounts as the same thing. This can be explored as a follow-up but for the first iteration, it is easier to omit this distinction. So this means that the transaction touched state consists of both reads and writes that this transaction did. And with this, having an account read from two different parallel transactions is considered invalid.\nNow, the idea here is that on every access of an account (read or write) to mark that account in the state as accessed by that transaction. This means that if account 0x01 is accessed by tx1 it will be marked as such and if tx2 tries to access account 0x1 we will notice that account is already marked and see that there is inconsistency and data race in place.\nSo every \"account\" had additional information that represent the transaction that last touched it.\nRunning transactions in parallel is more implementation detail and will depend on the programming language.\nExample 2: Chains, transactions dependencies.\nThe second example is having a third transaction that depends on the first one.\n\nGraph\nThis is the first example of dependent transactions andtx3 can access only accounts that are in the original state or touched by tx1, if both tx3 and tx2 access the same account this would make the parallelism claim invalid.\nThis example show's us that marking of state can be done by chain ids that this tx belongs to and we would get the same outcome. Without this tx4 would need to check if the account state is original or marked by tx1 or marked by tx3 and that wouldn't be efficient. I will use the terms chain and transaction interchangeably.\nExample 3: Chain forks and joins\nModelling dependency can be tricky but in parallel execution, there are only two synchronizations that can happen. And those are forks and joins and both of them can be seen in the picture.\n\nGraph\nThere is one fork here, and can be seen in the example of tx1 that forks its state to chains of tx5 and tx3. This means that there is a dependency between tx5 and tx1, tx3 and tx1 but there are no dependencies on tx3 and tx5 and they can be run in parallel.\nThe mechanism of marking the state works the same as in the first example. tx5 can now access the account of the original or tx1 or tx2 accounts if it accessed the state of tx3 or tx3 this would make parallel claim invalid.\nExample 4: Diamond pattern\nThis is a good example that tests our initial mechanism of marking of accessed state.\n\nGraph\nAll previous statements should be valid here.\nFor example tx7 can only touch original state or tx1, tx2, tx3, tx4, tx5 but not tx6, and same with tx6 it can't touch state of tx7\nHow to check marks\nEvery transaction could have a list of previous dependent transactions, and when checking the mark inside the database we compare it if it is found inside that list.\nThis list can be sorted so finding particular values can be done by binary search. The list size depends on the number of dependent transactions.\nminer fee\nThe problem with the current setup is that transaction pays the fee of execution to the miner after a transaction is finished, this would mean that every transaction depends on its predecessor miner balance to update it with this transaction fee. A simple solution for this is to just move the fee balance increment of the miner at the end of the block after all transactions are executed. This is a small consensus change without a lot of general impact, but as said it is a \"consensus change\" for us to parallelize transactions with the DAG hints we need a different solution.\nThis solution requires a lot of small things we need to make it consistent:\n\nwe should mark those transactions in DAG that need miner information and make them dependent on all previous transactions.\nWe need to have an additional atomic vector that is the size of the number of transactions and it will contain an increment of the miner balance, and it is updated async when transaction execution finishes.\nWe need a hint when miner balance information is needed, this is only possible in a few situations. This hint or a flag should be checked against the flag set inside DAG to see that this information is done correctly:\n\nOpcode BALANCE is called for a miner\nWhen the miner account is the contract and it transfers funds or calls SELFBALANCE opcode. We can be a little loose and say when the miner as a contract is called.\nIf the miner account is an ordinary account and there is a transaction with the miner as a sender.\n\n\nAfter all transactions are executed apply the rest of the transaction fee to the miner.\n\nThe solution requires a little bit of hoop jumping but it is possible to make and have verifiable parallel execution.\nUsage\nWhen live syncing, builders could potentially get more rewards if they find transactions that can be done in parallel. This would mean more throughput without state increase.\nOn history sync, we can obtain DAGs from centralized sources, and if we have verifiable parallel execution we don't need to trust that DAG and can do verification of parallel execution on our own, if received DAG is not correct we can just fall back to serial execution. This can potentially speed up initial archive sync by X factor.\nFurther works\nSplit of reads and writes:\nThis could make transactions ever more parallel.\nThe main idea behind this is that chain that writes an account should be the only one that can read that account. And few more checks need to be done\n\nIf the account is read by two chains but written in one it is considered a potential data race.\nFor every account read we should append the transaction number, and on every write, we should check if those reads connect to the same chain and mark that account as written and clear read list. And if there is a transaction from a different chain/predecessor that has written an account before us this is making the claim invalid. This means that every account now has the last transactions that wrote it and the list of transactions that have read it.\nIf we want to read the account we should first check if it is written by the predecessor, if it is not, this makes the claim invalid.\n\nThis is optimization for this architecture.\nGas calculation\nOne of the pending things that need to be defined when parallel transactions are considered for inclusion. This probably can be done by some calculation on the DAG and its weight (gas).\nSeparation of current gas accounting on CPU gas and disk io gas firstly specified in multidimentional EIP1559 is probably desirable, but not required. Gas calculation is always a sensitive topic as it can be abused if not done correctly.\nAnd as CPU cores are limited, we can have limitations on transaction DAG format.\n","id":"https://rakita.github.io/blog/blog/002-parallel-evm-claim/","title":"Parallel EVM claim"},"https://rakita.github.io/blog/blog/003-zombie-nodes/":{"body":"Let us begin with a question that made me start this whole blog post:\nWhat data the node would need to follow the chain tip?\nAnd subquestion:\nHow big of the disk would you need for that data?\nAnd the name of this node will be the Brain node (muhaaa)! Joking aside let's dive in:\nData\nTo answer the first question you would need\n\na plain state for executing new blocks\nMerkle tree to verify state root\nLittle bit of history for the last 64blocks that are still not finalized, for block reorganization\nLogs related to validator deposits to feed it to CL (Consensus Layer) node.\n\nWith this data, we would make a lean node and still be able to fully verify incoming blocks and sync with the CL node.\nDisk size\nHere is the data on the full archive mainnet node on block #17694321 on 14.07.2023, gained by executing ./reth db stats:\n\nThe sizes of needed data are shown below:\n\n96.4gb Plain state:\n\n12.4gb PlainAccountState\n84gb PlainStorageState\n\n\n118gb Merkle tree.\n\n16.1gb HashedAccount table\n75.4gb HashedStorage table\n22.6gb StorageTrie\n3.9gb AccountsTree\n\n\nI don't have the history size number for the last N blocks but it is in the range of a few gb. Will put it at 2gb.\nFor deposit events I don't have an exact number but for 856k deposit logs it is around a few gigs, let's say it is 2gb.\n\nThis would make the Brain node size around 218.4gb (what? just 220gb?)\nThen why are current nodes from 700gb to 15tb?\nIt is complicated.\nIf you want to sync your node from the genesis (zero block) you would need to fetch blocks from somewhere and this is currently embedded inside the Ethereum p2p protocol this means most nodes have all past blocks and this increases the size so our first type of zombie nodes is Block Zombie node, but I am going too far, let's go over how do we get from 15tb to 220gb and various types of nodes found in present:\n\nType1 ( old archive node): It contains everything that Ethereum has, those nodes are called archive nodes and from Etherscan OpenEthereum and Geth archive are around 15TB for the current tip. This kind of archive node became unusable, they need months to sync and are just big.\nType2 (Erigon/Reth archive node): Formating state a little bit differently and omitting history Merkle tries (present trie is still there) you shave a lot of data and you get Erigon/Reth type archive node of ~2tb.\nType3 (full node): If you remove history state from the node you get full node, this is currently what Geth/Nethermind/Besu defaults to, size goes to 700gb-1tb\nType4 (brain node): If you shave history blocks and receipts you are getting that lean Brain node of 200-300gb.\n\nThere is even an intermediate between type3 and type4 where only blocks after 11M are saved as this is the time when CL deposit logs start to appear.\n\n\n\nJust to note here if all nodes switch to type4 then type2 nodes would need to get blocks from somewhere else as in essence history blocks would be unavailable over p2p. Infrastructure for this is still not being made but some of the Ethereum nodes by default don't save it. When infra gets made I assume in the future the main type of the node would be Type4.\nThe Horde\nThe question that zombie nodes want to solve is why would you need 1/2tb NVMe SSD when you can get 500gb and use a public repository for blocks and other data. It seems more logical. You can put your blocks to aws bucket or external disk, or if you need more resources you could clone parts of data to multiple servers, either way, it is more flexible.\nThis would make running a node more approachable to new people and would allow Providers/Researchers to run better suited types of the zombie node.\nOr maybe the user care only for a particular event of a particular contract and want to have the ability to filter it, or maybe you want to run a lot of transactions on the newest state (Researcher use case), or you want to move the history state to a different place (provider use case) as you want to clone it or not want to be exposed to bandwidth expensive transaction broadcast. There are a lot of small use cases like this as every group of users focuses on something different.\nSplit have already happened with Consensus and Execution clients and imo I think the execution layer side needs to be split even more and this path seems unexplored (at least in the public).\nZombie types\nThe brain would be the main driver that does consensus checks and would push state changes to the zombies, zombies would consume brain data by being subscribed to it and would update its internal state.\nZombies consuming Brains (data) :)\nI am assuming some kind of streaming from the brain to other zombies is needed, so the brain executes blocks that are received from CL and streams new blocks and state changes to subscribed zombies.\nThe problems here are the same as with CL and EL nodes, syncing and recovery become troublesome and they can get out of sync. For some of the zombie's nodes recovery is possible and data can be requested again, but for others, it is not and we need to execute blocks again to get them. So zombie nodes if they want to recover data need other zombie nodes, and if out of sync some of them would require braind to resend all history outputs of execution.\nBlock zombie node\nThis is the first zombie that I am mentioning, it contains blocks (headers, bodies and receipts) and all of those data are checkable with just a hash.\nIt will contain multiple things, ~500gb of blocks, ~200gb of receipts and around ~200gb for senders and tx hashes index. If blocks/receipts are available from Brain it will allow Brain to still respond to requests over the p2p network and maintain the same protocol that is presently used. The otherwise empty brain can use those blocks to do initial sync, it should be faster and more reliable.\nThis node could have slow disks as it just needs to save blocks of data and it becomes a lot easier to cache things as we can perceive blocks as a blob of data that we need to save.\nThe good thing is there could be one Block node and multiple Brain nodes, this would allow faster fetching of the blocks if the brain needs to initially sync. All brain nodes could push blocks to the Block zombie and the zombie would save them all (or the main chain that gets extended)\nHistory state zombie node\nhistory state ~900gb allows you to access the past state of Ethereum. At every block, you would be able to fetch the balance/nonce/code or storage of the account.\nThe good thing about this is that we can add multiple indexes to the data and even create an index zombie node from it as we could map every change of the account.\nif you have a Block zombie node to get transactions you could use it to execute it and get transaction traces. It can be used to inspect past state both of accounts and storage.\nState zombie node\nNeeded for MEV searchers and block builders that do a lot of simulations on the tip they emulate transactions to find the best block rewards or possible MEV, this is already done by a lot of people/groups in space behind the doors and it should be fun to bring that more to the public.\nThis node would need just the newest state that is around ~100gb\nThe use case of MEV and builders is different but they operate on the newest state so I just wanted to mention this type of node. Builders for example would need to state root from the Merkle trie but they can delegate that to the Brain node.\nSome Future/Experimental zombie nodes\n\nLogs Zombie\nIf you want to prune a lot of logs and save only ones that are of interest to you. Or have a custom indexer for it.\nState proof zombie\nWith state expire already mentioned few times and something that is probably going to happen in future (Hard to tell when) this will be additional type of nodes that would allow transitioning accounts from expired to present state\nZombie aggregator\nAggregate all streamable data from the brain (or multiple brains) that would allow populating other zombie nodes.\n\nAnd I assume there will be an even more variant.\nConclusion\nAs the Ethereum state grows over time it becomes unusable, we need to mitigate this to slow down the growth until proper mechanisms are in place. Dropping of intermediate history Merkle trie's was the first mitigation that was naturally done and the second wave that we see is the dropping of history blocks.\nIn the end, presenting this as a zombie/brain/horde way was fun :) but the main ideas behind this post are half educational half exploratory, it is defining the execution node types that we already have (it is just about what data they possess), and that we can get even smaller execution nodes, and exploring the notion that having additional nodes/zombies that bind to execution node can give us different interactions/benefits in the ecosystem as not all data are made equal.\n","id":"https://rakita.github.io/blog/blog/003-zombie-nodes/","title":"Zombie nodes of Ethereum"},"https://rakita.github.io/blog/blog/004-eof-benefits/":{"body":"EOF (EVM Object Format) is a group of small EIPs that improve EVM. It introduced a new format of the bytecode and prepares EVM for the future.\nBenefits\nThe value of EOF turns out to be hard to explain as it is not one thing, and with its history of being delayed on multiple forks and development/research that spaned for years and different versions across history, does not help with clarity.\nThe intention of this post is to summarize the list of benefits and explain them in one sentence:\nBenefits:\n\nEOF allows opcode gas pricing changes.\n\nIf gas pricing changes, legacy bytecode could act differently.\nRemoval of gas observability, means removal of the GAS opcode and removal of gas limit from CALL/DELEGATECALL/STATICCALL.\nAllows l2 to change gas depending on their use case. Example of hash being overpriced in zk l2. EIP-7667: Raise gas costs of hash functions https://eips.ethereum.org/EIPS/eip-7667\nIf present EOF was enabled before berlin, this EIP would not be needed and access list could be removed. https://eips.ethereum.org/EIPS/eip-2930\n\n\nBytecode are smaller, and it reduces the gas usage\n\nEarly numbers show reduction in both the code/initcode size and reduction in gas usage: https://notes.ethereum.org/@ipsilon/solidity_eof_poc\nUniswap-v3 deployment 6.5% smaller initcode and 6.5% smaller deployed code\ndeploy UniswapV3Factory take: ~14% less gas and call runTest : ~9% less gas.\nENS DNSRegistrar deployment ~6% smaller initcode and ~1.5% smaller deployed code\nENS call proveAndClaim: takes ~10% less gas for EOF.\n\n\nEOF allows bytecode transformations and upgradability.\n\nRemoval of code observability means removal of PC, CREATE/CREATE2, EXTCODEHASH, EXTCODESIZE, EXTCODECOPY, CODESIZE, and CODECOPY opcodes.\nLegacy contracts would break if code is changed.\nThis would allow us to mold EOF bytecode in any form when verkle comes.\n\n\nEOF enables opcode immediates\n\nopens possibility of SWAPN, DUPN, and EXCHANGE opcodes\nThis allows solidity more freedom on stack size. It removed stack too deep problem in solidity.\n\n\nEOF removes expensive jumpdest analysis\n\nWhile in Reth, the analysis is saved with a bytecode, this is not case for other clients. The removal of jumpdest analysis before contract execution increases speed.\nWith analysis removed we can in future increment max bytecode size.\n\n\nStatic analysis becomes easier\n\nSubroutines enforce more structured control flow. This makes fuzzing more effective and linear-time static analysis possible.\nData and code are separated and easier to reason about.\nEOF bytecode can be compiled to faster bytecode.\nEOF bytecode can be compiled to machine code.\n\n\nFuture proofing EVM\n\nThe version and structure of bytecode allow its extensibility. This is especially useful for L2 and standardization.\nOne example of this is EIP-7701: Native Account Abstraction with EOF that adds new header section.\n\n\nAddress space expansion.\n\nNew EXT*CALL opcodes prepare Ethereum for future address expansion by requiring for address field to be padded with zeros. When Ethereum do decide to extend address space EOF will have prepared those changes.\n\n\n\nIntegration with current EVM\nAn important question for developers is the effort needed to implement the change, the cost of testing, and the cost of maintaining those opcodes.\nNew opcodes do not clash with legacy opcodes, and EOF cause of verification does not touch deprecated opcodes.\nEncoding and decoding of EOF can be fuzzed. Validation is something new, around ~500 loc in Revm but has a lot of edge cases that need to be covered and need focus to do it right across the implementations.\nCreation TX is used as a carrier of EOF bytecode, it acts similar to EOFCREATE but requests validation before execution.\nMost opcodes are very simple:\n\nEXTCALL (0xf8), EXTDELEGATECALL (0xf9), EXTSTATICCALL (0xfb)\n\nHave same blueprint as deprecated CALLs but with gas_limit and memory output fields removed.\nBefore RETURNDATALOAD (introduced in a very early fork), the memory output of a CALL had to be set before doing a CALL opcode. This did not allow for dynamic outputs.\n\n\nEOFCREATE and RETURNCONTRACT\n\nare new for EOF and require a special cases to be handled.\nEOFCREATE introduces new creation call.\n\n\nEXCHANGE (0xe8), SWAPN (0xe7), DUPN (0xe6), DATACOPY (0xd3), DATASIZE (0xd2), DATALOADN (0xd1), DATALOAD (0xd0), RJUMP (0xe0), RJUMPI (0xe1), RJUMPV (0xe2), RETURNDATALOAD\n\nHave simple logic and mostly require 10–20 lines per opcode to implement. Does not have a lot of edge cases that need to be covered.\n\n\nCALLF (0xe3), RETF (0xe4), and JUMPF (0xe5)\n\nRequire subroutine stack, and stack validation, those would require 20-30 loc as measure of complexity.\n\n\n\nThey require around 2/3 months for one developer. Work on testing has already been started. There are currently around ~2000 handwritten tests for validation and work is beeing done on statetests.\nChanges are localized to the EVM, so integration with the rest of the client depends on the client architecture and where you would save bytecode.\nEXTCODESIZE and EXTCODEHASH need to be aware if the account is EOF or not and return a predefined value (size and hash of 0xEF00), this could slightly change how integration for the client can be done. One of ideas is to save is_eof flag inside plain accounts table to skip loading of the bytecode when any of EXTCODE type of opcodes are called.\nEffect on L2\nBiggest question that is asked is why don’t L2 implement this change? Should we close the EVM improvements on Ethereum L1?\nThe reality is that L2 are not ready, and not just that, they do not have a platform that would help with integration of those innovations. Versioning of bytecode helps with that as it helps build that platform that L2 can use, removal of code observability helps mitigating upgredability issues, and gas changes help ZK L2 to remove the ddos vector of gas bombs (for example, a hash in zk is worth a lot more).\nMore importantly, EOF is not just a format, it requires languages (solidity/vyper/huff) to be on board, and it requires tooling to support it so it can be usable. It requires an ecosystem to use it, and this format gives L2 more stability to innovate on top of it.\nDownside, legacy bytecode still exists\nThis is a question that is often asked. Legacy bytecode will always be here, if we don’t offer a alternative, we are going to be stuck with it. With alternative formated bytecodes, there is a future where we could transition and remove legacy bytecodes when state expire happens.\nIn the end\nEOF is not the next big shiny thing, it is the maintenance EIP that fixes problems left from the initial EVM version and there is no other way to solve those other than breaking it. It is necessary for further development and future proofing of EVM.\n\nPost was originally shared in hackmd form with Ethereum core devs. This was done a day before Execution Layer Meeting 189. On that ACD call, clients officially included EOF in Prague hardfork.\n","id":"https://rakita.github.io/blog/blog/004-eof-benefits/","title":"EOF Benefits"},"https://rakita.github.io/blog/blog/005-revm-audit/":{"body":"I am thrilled that this milestone has been reached!\nRevm is one of the leading EVMs in the Ethereum ecosystem. Many major projects and companies depend on it, and the number is growing, especially since the development of Reth. Revm has solidified its position as a foundational component of the Ethereum ecosystem, and its significance continues to rise.\nIt is used by clients, block builders, tooling, and many searchers and hobbyists. Revm's stability has been stellar and its execution consistent, leading to heavy reliance on it. Its speed and flexibility make it highly desirable for integration. With no_std support, it fits nicely in the zkVM world, where Rust is predominantly used. There are many requirements that Revm fulfills.\nThere was already a lot of confidence in its consensus correctness, and bugs were squashed, which helped develop the client Reth on top of it. We wanted to increase that confidence even further and have more assurance. The next step for this was an audit.\nGuido Vranken, a top Ethereum bug hunter, was hired to stress test Revm. With his expertise in fuzzing and finding consensus bugs, it has been a pleasure to have him on board.\nDespite its already extensive usage, Guido managed to find a few undiscovered consensus issues and created a tool for fuzzing that we can reuse.\nWhen we discussed how to conduct the audit, the idea of involving others arose: \"If there are multiple parties who rely on Revm, then it may make sense to have them co-fund it.\"\" So we did exactly that and made this audit community-driven and sponsored by six companies that use Revm in various ways.\n\n\nParadigm\nFoundry, which Paradigm created, was started in the same month as Revm. After some time, the Foundry team switched to Revm, making it the first big project to use it.\nThey were the first to see the potential and are a major reason for Revm's popularity today.\nMore than a year later, an idea was born to build Reth as there was already a working EVM. This is when I finally joined Paradigm, as I enjoyed working on client code.\nI am really grateful to Georgios and Paradigm for their support and for later hiring me to work on Reth and continue working on Revm. The Reth team is amazing, and it is my privilege to work with them!\n\n\nEthereum Foundation\nWe wouldn’t be here without Ethereum, so it made sense to include the Ethereum Foundation. After the audit of Revm and Reth is finished, Reth is scheduled to be included in the EF Bug Bounty along with its dependencies, so any bugs found within Revm that has an impact on the Ethereum network will be receiving rewards through the Ethereum Foundation Bug Bounty Program.\n\n\nOptimism\nThe Optimism stack and their team are amazing! Revm has support for Optimism changes, which made me think about the future of Revm and support for other EVM chains. The RetroPGF mechanism is a great way to incentivize builders to build even more.\n\n\nNomic Foundation\nhe builders of Hardhat recently enabled their EDR (Ethereum Development Runtime) framework, which uses Revm at its core. They recognized Revm's potential and started transitioning to it years ago.\n\n\nRisc0\nRisc0 launched Zeth, their zkEVM system, which is leveraging Revm for execution. It is an awesome company that uses RISC-V for universal zk execution.\n\n\nTaiko\nAt one of the conferences, they approached me about Revm audit efforts and offered to help, so when the list was made, they were included. They care about the lower stack and are one of the first projects to make the 1% Pledge for Protocol Guild. I appreciate their support.\n\n\nThere are many more projects that depend on Revm, and I am glad that this is the case, as it means the library is doing something good.\nThis is a huge milestone for Revm and me. When I started the project, I didn't imagine it would have such an impact on Ethereum. I appreciate the community, as without it, this would not be possible. I am committed to fulfilling the expectations and obligations of the users who depend on it.\nThe work continues. The future is primarily focused on two things:\n\nThe next hard fork that includes EOF (EVM Object Format)\nRevm endgame, but that is a topic for another post.\n\n\nPost was originally shared in hackmd and twitted here.\n","id":"https://rakita.github.io/blog/blog/005-revm-audit/","title":"Revm Audit/Fuzzing"},"https://rakita.github.io/blog/privacy-policy/":{"body":"TLDR: We do not use cookies and we do not collect any personal data.\nWebsite visitors\n\nNo personal information is collected.\nNo information is stored in the browser.\nNo information is shared with, sent to or sold to third-parties.\nNo information is shared with advertising companies.\nNo information is mined and harvested for personal and behavioral trends.\nNo information is monetized.\n\nContact us\nContact us if you have any questions.\nEffective Date: 1st May 2021\n","id":"https://rakita.github.io/blog/privacy-policy/","title":"Privacy Policy"}},"docInfo":{"https://rakita.github.io/blog/":{"body":0,"title":2},"https://rakita.github.io/blog/authors/":{"body":3,"title":1},"https://rakita.github.io/blog/authors/draganrakita/":{"body":3,"title":1},"https://rakita.github.io/blog/blog/":{"body":0,"title":1},"https://rakita.github.io/blog/blog/001-2d-transformations/":{"body":795,"title":6},"https://rakita.github.io/blog/blog/002-parallel-evm-claim/":{"body":865,"title":3},"https://rakita.github.io/blog/blog/003-zombie-nodes/":{"body":850,"title":3},"https://rakita.github.io/blog/blog/004-eof-benefits/":{"body":694,"title":2},"https://rakita.github.io/blog/blog/005-revm-audit/":{"body":389,"title":2},"https://rakita.github.io/blog/privacy-policy/":{"body":39,"title":2}},"length":10},"lang":"English"} \ No newline at end of file diff --git a/public/site.webmanifest b/public/site.webmanifest new file mode 100644 index 0000000..4ce700f --- /dev/null +++ b/public/site.webmanifest @@ -0,0 +1 @@ +{"name":"Zola Theme AdiDoks","short_name":"AdiDoks","icons":[{"src":"/android-chrome-192x192.png","sizes":"192x192","type":"image/png"},{"src":"/android-chrome-512x512.png","sizes":"512x512","type":"image/png"}],"theme_color":"#fff","background_color":"#fff","display":"standalone"} \ No newline at end of file diff --git a/public/sitemap.xml b/public/sitemap.xml new file mode 100644 index 0000000..cd15f6e --- /dev/null +++ b/public/sitemap.xml @@ -0,0 +1,42 @@ + + + + https://rakita.github.io/blog/ + + + https://rakita.github.io/blog/authors/ + + + https://rakita.github.io/blog/authors/draganrakita/ + 2021-04-01T08:50:45+00:00 + + + https://rakita.github.io/blog/blog/ + + + https://rakita.github.io/blog/blog/001-2d-transformations/ + 2019-12-20T09:19:42+00:00 + + + https://rakita.github.io/blog/blog/002-parallel-evm-claim/ + 2023-07-09T22:20:00+00:00 + + + https://rakita.github.io/blog/blog/003-zombie-nodes/ + 2023-08-04T22:20:00+00:00 + + + https://rakita.github.io/blog/blog/004-eof-benefits/ + 2024-06-06T12:00:00+00:00 + + + https://rakita.github.io/blog/blog/005-revm-audit/ + 2024-06-25T12:00:00+00:00 + + + https://rakita.github.io/blog/blog/page/1/ + + + https://rakita.github.io/blog/privacy-policy/ + +
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