CompilerGym Release v0.2.0 #439
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This release adds two new compiler optimization problems to CompilerGym: GCC command line flag optimization and CUDA loop nest optimization. - [GCC] A new `gcc-v0` environment, authored by @hughleat, exposes the command line flags of GCC as a reinforcement learning environment. GCC is a production-grade compiler for C and C++ used throughout industry. The environment provides several datasets and a large, high dimensional action space that works on several GCC versions. For further details check out the reference documentation: https://facebookresearch.github.io/CompilerGym/envs/gcc.html - [loop_tool] A new `loop_tool-v0` environment, authored by @bwasti, provides an experimental intermediate representation of *n*-dimensional data computation that can be lowered to both CPU and GPU backends. This provides a reinforcement learning environment for manipulating nests of loop computations to maximize throughput. For further details check out the reference documentation: https://facebookresearch.github.io/CompilerGym/envs/loop_tool.html Other highlights of this release include: - [Docker] Published a chriscummins/compiler_gym docker image that can be used to run CompilerGym services in standalone isolated containers. - [LLVM] Fixed a bug in the experimental `Runtime` observation space that caused observations to slow down over time. - [LLVM] Added a new utility module to compute observations from bitcodes. - Overhauled the continuous integration services to reduce computational requirements by 59.4% while increasing test coverage. - Improved error reporting if computing an observation fails. - Changed the return type of compiler_gym.random_search() to a CompilerEnv. - Numerous other bug fixes and improvements. Many thanks to code contributors: @thecoblack, @bwasti, @hughleat, and @sahirgomez1!
We will be running the full CI workflow on every push / PR, so repeating every test on the build artifacts seems wasteful. Instead just run the examples tests.
CompilerGym Release v0.2.0
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This release adds two new compiler optimization problems to CompilerGym: GCC command line flag optimization and CUDA loop nest optimization.
gcc-v0environment, authored by @hughleat, exposes the command line flags of GCC as a reinforcement learning environment. GCC is a production-grade compiler for C and C++ used throughout industry. The environment provides several datasets and a large, high dimensional action space that works on several GCC versions. For further details check out the reference documentation.loop_tool-v0environment, authored by @bwasti, provides an experimental intermediate representation of n-dimensional data computation that can be lowered to both CPU and GPU backends. This provides a reinforcement learning environment for manipulating nests of loop computations to maximize throughput. For further details check out the reference documentation.Other highlights of this release include:
Runtimeobservation space that caused observations to slow down over time (#398).compiler_gym.random_search()to aCompilerEnv(#387).Many thanks to code contributors: @thecoblack, @bwasti, @hughleat, and @sahirgomez1!