5/17/2023 0 Comments Motrix multiplication![]() ![]() 4 is devoted to the experimental results. 3 explains how to generate gemm micro-kernels using vector intrinsics. 2 briefly reviews the family of BLIS algorithms for high performance matrix-matrix multiplication. The rest of the paper is structured as follows. ![]() We provide practical evidence of the advantages and caveats of this high-level approach, for deep learning (DL) applications, on two ARM-based multicore processors, equipped with 128-bit and 512-bit SIMD units. We integrate the intrinsics-based micro-kernels into the BLIS family of algorithms for matrix multiplication, experimentally exploring the performance of the family members. ![]() We describe how to develop an ample variety of multi-platform, high performance micro-kernels for matrix multiplication using high level vector intrinsics to exploit the SIMD (single instruction, multiple data) units in current general-purpose processors. This work thus makes the following specific contributions: This differs from current realizations of gemm in libraries such as GotoBLAS2, OpenBLAS and BLIS, which integrate a single micro-kernel per architecture, encoded in assembly. The key in this paper lies in the formulation of a common generic micro-kernel, which can then be easily instantiated into a collection architecture-specific realizations via a few macros and two configuration variables specifying the micro-kernel dimensions. In this paper, we continue our previous work in toward improving portability (and maintainability) of gemm. In , we investigated the members of this family, manually instantiating them on an NVIDIA Carmel, ARM-based processor, and conducting an evaluation for the type of operators that are encountered in convolutional neural networks , using a simple micro-kernel only. The ideas behind Goto and van de Geijn’s algorithm for gemm were eventually extended to formulate a rich family of algorithms for this operation that comprises six algorithms and three types of micro-kernels . ![]() There, the authors define the foundations for the realization of the high performance matrix multiplication ( gemm) that underlies most current linear algebra libraries, such as GotoBLAS , BLIS , OpenBLAS , AMD AOCL, and (possibly) Intel MKL and oneAPI. A significant leap forward toward combining portability with performance came later, in a seminal paper from Kazushige Goto and Robert A. Our work exposes the structure of the template-based micro-kernels for ARM Neon (128-bit SIMD), ARM SVE (variable-length SIMD) and Intel AVX512 (512-bit SIMD), showing considerable performance for an NVIDIA Carmel processor (ARM Neon), a Fujitsu A64FX processor (ARM SVE) and on an AMD EPYC 7282 processor (256-bit SIMD).Ī few decades ago, the basic linear algebra subprograms (BLAS) sparked portability in scientific computing by defining a standard interface that hardware vendors could instantiate into their products, and researchers could then leverage from their codes and libraries. These generic templates employ vector intrinsics to exploit the SIMD (single instruction, multiple data) units in current general-purpose processors and, for the particular type of gemm problems encountered in deep learning, deliver a floating-point throughput rate on par with or even higher than that obtained with conventional, carefully tuned implementations of gemm in current linear algebra libraries (e.g., BLIS, AMD AOCL, ARMPL). \right)x.We provide a practical demonstration that it is possible to systematically generate a variety of high-performance micro-kernels for the general matrix multiplication ( gemm) via generic templates which can be easily customized to different processor architectures and micro-kernel dimensions. ![]()
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