SCHEDULE: NOV 16-22, 2013

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AUGEM: Automatically Generate High Performance Dense Linear Algebra Kernels on x86 CPUs

SESSION: Optimizing Numerical Code

EVENT TYPE: Papers

TIME: 3:30PM - 4:00PM

SESSION CHAIR: Naoya Maruyama

AUTHOR(S):Qian Wang, Xianyi Zhang, Yunquan Zhang, Qing Yi

ROOM:401/402/403

ABSTRACT:
Basic liner algebra subprograms (BLAS) is a fundamental library in scientific computing. In this paper, we present a template-based optimization framework, AUGEM, which can automatically generate fully optimized assembly code for several dense linear algebra (DLA) kernels, such as GEMM, GEMV, AXPY and DOT, on varying multi-core CPUs without requiring any manual interference from developers. In particular, based on domain-specific knowledge about algorithms of the DLA kernels, we use a collection of parameterized code templates to formulate a number of commonly occurring instruction sequences within the optimized low-level C code of these DLA kernels. Then, our framework uses a specialized low-level C optimizer to identify instruction sequences that match the predefined code templates and thereby translates them into extremely efficient SSE/AVX instructions. The DLA kernels generated by our template-based approach surpass the implementations of Intel MKL and AMD ACML BLAS libraries, on both Intel Sandy Bridge and AMD Piledriver processors.

Chair/Author Details:

Naoya Maruyama (Chair) - RIKEN Advanced Institute for Computational Science

Qian Wang - Institute of Software, Chinese Academy of Science

Xianyi Zhang - Institute of Software, Chinese Academy of Science

Yunquan Zhang - Institute of Software, Chinese Academy of Science

Qing Yi - University of Colorado Colorado Springs

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The full paper can be found in the ACM Digital Library