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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:
a popular class of Python workloads (PyTorch)
embedding CPython
running multiple interpreters in the same process with CPU parallelism
Activity
lewurm commentedon Mar 3, 2021
Inline Caching:
Hidden classes:
superproperty access improvement in v8 [2021]: https://v8.dev/blog/fast-super[-]Things to read[/-][+]Reading list[/+]markshannon commentedon Mar 11, 2021
Analyses of Python runtime overhead:
Interpreter dispatch:
markshannon commentedon Aug 2, 2021
Producing Wrong Data Without Doing Anything Obviously Wrong!
ericsnowcurrently commentedon Aug 2, 2021
"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:
gvanrossum commentedon Aug 17, 2021
A very detailed post on perf improvements for .NET 6:
https://devblogs.microsoft.com/dotnet/performance-improvements-in-net-6/
gvanrossum commentedon Nov 16, 2021
Copy-and-Patch Compilation:
https://arxiv.org/pdf/2011.13127.pdf