Lapidary: creating beautiful gem5 simulations.

If you use Lapidary in your research, please cite:

Ofir Weisse, Ian Neal, Kevin Loughlin, Thomas F. Wenisch, and Baris Kasikci. 2019. NDA: Preventing Speculative Execution Attacks at Their Source. In Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO '52). Association for Computing Machinery, New York, NY, USA, 572–586. DOI: https://doi.org/10.1145/3352460.3358306

Lapidary is a tool we have built to enable more efficient gem5 simulations.

Lapidary works by creating gem5 checkpoints on bare-metal to avoid the weeks of simulation required to create viable checkpoints. This is done by taking core dumps of the program through gdb (along with gathering some other miscellaneous process state information) and transforming the output into a gem5-compatible checkpoint. Lapidary can then perform short simulations over many checkpoints (in accordance with the SMARTS sampling methodology) in order to produce statistically significant performance measurements.

For more information about Lapidary and its inception, please refer to our blog post.

Installation

To install Lapidary:

# Clone repository
$ git clone https://github.com/efeslab/lapidary.git ./lapidary_repo
# Setup virtual environment
$ python3 -m venv virt_env
$ source virt_env/bin/activate
# install
$ pip3 install ./lapidary_repo

Note: due to some limitations, it might be necessary to run Lapidary in a VM with certain processor features disabled. See the limitations section for details.

Usage

There are two steps when running Lapidary: (1) creating checkpoints and (2) simulating checkpoints.

Configuration

All configurations must comply with the configuration schema. The file is assumed to be ./.lapidary.yaml, but can be specified explicitly by the -c <PATH> option available for all verbs.

Examples

1. Display the schema format and exit:

$ python3 -m lapidary --config-help

2. A basic configuration (all you need to run basic simulations):

./.lapidary.yaml:

gem5_path: path/to/gem5/relative/to/config/file/location

3. Creating a FlagConfigure custom class for enabling custom features during simulations. If you gate custom microarchitectural features behind debug flags, you can enable them inside of FlagConfigure class, either before simulation starts (before_init) or after warmup but before performance is measured (after_warmup). The default config is empty, which enables no flags.

./.lapidary.yaml:

...
gem5_flag_config_plugin: ./example_configurations.py
...

./example_configurations.py

from lapidary.config import FlagConfigure

class TestConfig(FlagConfigure):
    @staticmethod
    def before_init(system):
        ...
        
    @staticmethod
    def after_warmup():
        import m5
        m5.debug.flags["..."].enable()
        ...

A full example configuration file is available in our testing directory.

Checkpoint Creation

The create verb is used to create checkpoints. This can either be done in an automated fashion (every N seconds or every M instructions) or interactively through a shell interface.

Examples

1. Print help:

$ python3 -m lapidary create --help
# If ./.lapidary.yaml doesn't exist
$ python3 -m lapidary -c ./lapidary_repo/.lapidary.yaml create --help

2. Create checkpoints for an arbitrary binary every second:

$ python3 -m lapidary create --cmd "..." --interval 1

3. Create checkpoints for SPEC-CPU 2017 benchmarks (requires valid configuration path) and save them to /mnt/storage rather than the current working directory:

$ python3 -m lapidary create --bench mcf --interval 5 --directory /mnt/storage

4. Create checkpoints interactively:

$ python3 -m lapidary create --cmd "..." --breakpoints ...

At each breakpoint, the user will be allowed to interact with the gdb process.

User-defined breakpoint reached. Type "help" for help from this shell, or "gdb help" for traditional help from gdb.
(py-gdb) help

Documented commands (type help <topic>):
========================================
checkpoint  exit  gdb  help  quit

(py-gdb) 

Single Simulation

The simulate verb is used to simulate a single checkpoint that was previously created from the create command. This command is useful for debugging issues with custom modifications to the gem5 simulator.

Examples

1. Simulate a single checkpoint from an arbitrary binary:

$ python3 -m lapidary simulate --start-checkpoint \
    test_gdb_checkpoints/0_check.cpt --binary ./test/bin/test

2. Simulate a single checkpoint from an arbitrary command (with arguments):

$ python3 -m lapidary simulate --start-checkpoint \
    test_gdb_checkpoints/0_check.cpt --binary ./test/bin/test --args ... 

3. Debug gem5 on a particular checkpoint:

$ python3 -m lapidary simulate --start-checkpoint \
    test_gdb_checkpoints/0_check.cpt --binary ./test/bin/test --args ... \
    --debug-mode

--debug-mode will not only use gem5.debug instead of gem5.opt, but it will also runs gem5 through gdb.

4. Display loaded FlagConfigurations and simulate using a custom configuration.

$ python3 -m lapidary simulate --list-configs

['empty', 'testconfig']

$ python3 -m lapidary simulate --start-checkpoint \
    test_gdb_checkpoints/0_check.cpt --bin ./test/bin/test \
    --flag-config testconfig

Parallel Simulation

The parallel-simulate verb is used to simulate a group of checkpoints from a single benchmark at once. This can be used to generate statistical performance measurements using the SMARTS methology.

This works by simulating each checkpoint for a small number of instructions in two different periods; there is a warmup period (default 5,000,000 instructions) which allows the caches to be filled, and a real simulation period (by default 100,000 instructions) which generates the reported statistics. For more details on the theory behind this sampling methodology, please refer to the ISCA publication.

Examples

1. Simulate all checkpoints taken from an arbitrary command (This will look for checkpoints within ./test_gdb_checkpoints/, e.g. ./test_gdb_checkpoints/0_check.cpt, ./test_gdb_checkpoints/1_check.cpt, etc.):

$ python3 -m lapidary parallel-simulate --binary ./test/bin/test --args ... 

2. Simulate all checkpoints taken from the MCF benchmark with a non-standard checkpoint directory:

$ python3 -m lapidary parallel-simulate --bench mcf --checkpoint-dir /mnt/storage

Reporting

The report verb is used to aggregate the results from multiple simulations into a single report file and display them in a variety of ways. The first step is always to generate the aggregated report (via the report process facility), then the data can be further processed into more digestable formats.

Currently we have two sub-commands:

• report process, to create the aggregated report.
• report filter, to create a more terse, easily readable report.

We plan on adding a built-in graphing facility (report graph) soon.

Examples

1. Create a report after simulating checkpoints in /mnt/storage:

$ python3 -m lapidary report process -d /mnt/storage

The output is in ./report.json, which looks like:

{
    "results": {
        "benchmark_name": {
            "configuration_1": {
                "sim_insts": {
                    "mean": 103165.07692307692,
                    "std": 2507.9945546971453,
                    "ci": 1363.3613701898664,
                    "count": 13.0
                },
                ...

• count is the number of checkpoints used to generate the statistic.
• mean is the arithmetic mean across the checkpoints.
• std is the standard deviation, and ci is the 95% confidence interval.

2. Create a summary report that contains only the cpi (cycles per instruction) and MLP (memory-level parallelism) statistic.

$ python3 -m lapidary report filter cpi -o cpi_only.yaml -i report.json

This reads the report from report.json and writes the output into cpi_only.yaml. The output type can be either yaml or json (use report filter --help for details).

cpi_only.yaml:

benchmark_1:
 configuration_1:
   cpi: 0.98
 configuration_2:
   cpi: 1.1
 ...

Current Limitations

1. Currently, gem5 does not support all Intel ISA extensions (such as AVX). Gem5 is able to disable use of these instructions by libc when it loads programs, however glibc seems to dynamically check which extensions are available at runtime and use those implementations. This causes a problem for some checkpoints, as they end up attempting to use AVX instructions in gem5, causing a crash since gem5 does not recognize these instructions.

   Our temporary workaround was to run our experiments in a VM, as it is easy to specify which extensions to disable via the -cpu flag in QEMU, e.g.

... -cpu host,-avx2,-bmi1,-bmi2 ...

We have provided some example scripts in the scripts directory.

2. Not all checkpoints run successfully. For example, checkpoints that attempt to invoke syscalls generally end up crashing.

3. As we generate a lot of checkpoints for our sampling methodology, Lapidary quickly occupies a lot of disk space (a few hundred GB is not uncommon).

4. Our sampling method does not support simulation over custom instructions. In other words, our sampling method only works when simulating existing ISAs (which can be run on bare metal) with potentially different back-end implementations.

Future Work

1. We are currently working on a feature for syscall tracing, i.e. replaying the call trace from the checkpointing process upon resuming simulation in gem5. This will solve many of the issues that currently cause some checkpoints to fail.

2. Add support for checkpoint "key-frames", i.e. storing small numbers of full checkpoints, then creating diffs from those for following checkpoints. This feature will need to be configurable, as it will increase the processing required for simulation startup.

3. Add support for custom instructions. This can be presented in several modes; either skip custom instructions during checkpoint creation, or emulate them at a high level when encountered. This will not catch all use cases, but I imagine it will catch many.

4. Add support for cloud deployments, i.e. distributed simulation, potentially using ansible to automate provisioning/setup as well.

Contributing

Please feel free to create forks, post issues, make pull requests, or email us directly! We appreciate any and all feedback and would like to make this tool as useful as possible.