Reading list #123
Replies: 10 comments
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Inline Caching:
Hidden classes:
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Analyses of Python runtime overhead:
Interpreter dispatch:
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Producing Wrong Data Without Doing Anything Obviously Wrong! |
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"USING PYTHON FOR MODEL INFERENCE IN DEEP LEARNING", DeVito et al (Facebook AI Research) https://arxiv.org/abs/2104.00254 The most relevant aspects, for our project, are their coverage of a popular class of Python workloads (PyTorch) and their approach to CPU parallelism using CPython. Overall, they cover a lot of interesting details that extend to many parts of Python core development and to the Python packaging ecosystem. Most notably:
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A very detailed post on perf improvements for .NET 6: https://devblogs.microsoft.com/dotnet/performance-improvements-in-net-6/ |
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Copy-and-Patch Compilation: |
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From #213: https://www.sciencedirect.com/science/article/abs/pii/S0167642321001520 (paywalled). |
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Simple and Effective Type Check Removal through Lazy Basic Block Versioning |
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Writeup about the function inliner in Cinder JIT https://engineering.fb.com/2022/05/02/open-source/cinder-jits-instagram/ |
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Threaded Code Generation with a Meta-Tracing JIT Compiler |
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Please add your favorite references!
Two things on HHVM (2018, so fairly recent):
Mark Shannon writings:
Victor Stinner writings:
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