4 research outputs found
LoopTune: Optimizing Tensor Computations with Reinforcement Learning
Advanced compiler technology is crucial for enabling machine learning
applications to run on novel hardware, but traditional compilers fail to
deliver performance, popular auto-tuners have long search times and
expert-optimized libraries introduce unsustainable costs. To address this, we
developed LoopTune, a deep reinforcement learning compiler that optimizes
tensor computations in deep learning models for the CPU. LoopTune optimizes
tensor traversal order while using the ultra-fast lightweight code generator
LoopNest to perform hardware-specific optimizations. With a novel graph-based
representation and action space, LoopTune speeds up LoopNest by 3.2x,
generating an order of magnitude faster code than TVM, 2.8x faster than
MetaSchedule, and 1.08x faster than AutoTVM, consistently performing at the
level of the hand-tuned library Numpy. Moreover, LoopTune tunes code in order
of seconds