research-article

Automatic Generation of Micro-kernels for Performance Portability of Matrix Multiplication on RISC-V Vector Processors

Authors:

Francisco Igual,

Sandra Catalán,

Héctor Martínez,

Adrián Castelló,

Enrique Quintana-OrtíAuthors Info & Claims

SC-W '23: Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis

Pages 1523 - 1532

https://doi.org/10.1145/3624062.3624229

Published: 12 November 2023 Publication History

Abstract

In this paper, we propose and evaluate several optimized implementations of the general matrix multiplication (gemm) on two different RISC-V architecture cores implementing the RISC-V vector extension (RVV): C906 and C910 from T-HEAD. Specifically, we address the performance portability problem across these processor cores by means of an automatic assembly code generator, written in Python, capable of emitting RVV code for high performance computing (HPC), with a variety of combinations of specific and general optimizations.

Our experimental results using a number of automatically-generated micro-kernels for gemm, on both RISC-V architectures, reveal different impact of each optimization, depending on the target architecture, and highlight the importance of automatically generating HPC RVV code to achieve performance portability while reducing the developers’ effort. In addition, these optimizations show important performance gains with resepcto to a state-of-the-art tuned BLAS library (OpenBLAS), reaching 3 × and 1.3 × speed-ups for the C910 and C906, respectively.

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Cited By

Martínez HCatalán SCastelló AQuintana-Ortí E(2024)Parallel GEMM-based convolutions for deep learning on multicore ARM and RISC-V architecturesJournal of Systems Architecture: the EUROMICRO Journal10.1016/j.sysarc.2024.103186153:COnline publication date: 8-Aug-2024
https://dl.acm.org/doi/10.1016/j.sysarc.2024.103186
Martínez HCatalán SGarcía CIgual FRodríguez-Sánchez RCastelló AQuintana-Ortí E(2024)Performance Analysis of BERT on RISC-V Processors with SIMD UnitsHigh Performance Computing. ISC High Performance 2024 International Workshops10.1007/978-3-031-73716-9_23(325-338)Online publication date: 14-Dec-2024
https://doi.org/10.1007/978-3-031-73716-9_23

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Automatic Generation of Micro-kernels for Performance Portability of Matrix Multiplication on RISC-V Vector Processors

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SC-W '23: Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis

November 2023

2180 pages

ISBN:9798400707858

DOI:10.1145/3624062

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 12 November 2023

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Ministerio de Ciencia e Innovación

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SC-W 2023

SC-W 2023: Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis

November 12 - 17, 2023

CO, Denver, USA

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Cited By

Martínez HCatalán SCastelló AQuintana-Ortí E(2024)Parallel GEMM-based convolutions for deep learning on multicore ARM and RISC-V architecturesJournal of Systems Architecture: the EUROMICRO Journal10.1016/j.sysarc.2024.103186153:COnline publication date: 8-Aug-2024
https://dl.acm.org/doi/10.1016/j.sysarc.2024.103186
Martínez HCatalán SGarcía CIgual FRodríguez-Sánchez RCastelló AQuintana-Ortí E(2024)Performance Analysis of BERT on RISC-V Processors with SIMD UnitsHigh Performance Computing. ISC High Performance 2024 International Workshops10.1007/978-3-031-73716-9_23(325-338)Online publication date: 14-Dec-2024
https://doi.org/10.1007/978-3-031-73716-9_23

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