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Creating high performance implementations of deep learning primitives on CPUsis a challenging task. Multiple considerations including multi-level cachehierarchy, and wide SIMD units of CPU platforms influence the choice of programtransformations to apply for performance optimization. In this paper, wepresent machine learning powered compiler techniques to optimize loop nests. Wetake a two-pronged approach to code optimization: We first apply high leveloptimizations to optimize the code to take optimal advantage of the cachememories. Then, we perform low level, target-specific optimizations toeffectively vectorize the code to run well on the SIMD units of the machine.For high level optimizations, we use polyhedral compilation techniques and deeplearning approaches. For low level optimization, we use a target specific codegenerator that generates code using vector intrinsics and ReinforcementLearning (RL) techniques to find the optimal parameters for the code generator.We perform experimental evaluation of the developed techniques on variousmatrix multiplications that occur in popular deep learning workloads. Theexperimental results show that the compiler techniques presented in the paperachieve 7.6X and 8.2X speed-ups over a baseline for sequential and parallelruns respectively.

AI Powered Compiler Techniques for DL Code Optimization