Auto-tuning GEMM kernels on the Intel KNL and Intel Skylake-SP processors

The general matrix–matrix multiplication is a core building block for implementing Basic Linear Algebra Subprograms. This paper presents a methodology for automatically producing the matrix–matrix multiplication kernels tuned for the Intel Xeon Phi Processor code-named Knights Landing and the Intel Skylake-SP processors with AVX-512 intrinsic functions. The architecture of the latest manycore processors has been complicated in the levels of parallelism and cache hierarchies; it is not easy to find the best combination of optimization techniques for a given application. Our approach produces matrix multiplication kernels through a process of heuristic auto-tuning based on generating multiple kernels and selecting the fastest ones through performance tests. The tuning parameters include the size of block matrices for registers and caches, prefetch distances, and loop unrolling depth. Parameters for multithreaded execution, such as identifying loops to parallelize and the optimal number of threads for such loops are also investigated. We also present a method to reduce the parameter search space based on our previous research results.