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SIMD²: a generalized matrix instruction set for accelerating tensor computation beyond GEMM

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Hung-Wei TsengAuthors Info & Claims

ISCA '22: Proceedings of the 49th Annual International Symposium on Computer Architecture

Pages 552 - 566

https://doi.org/10.1145/3470496.3527411

Published: 11 June 2022 Publication History

Abstract

Matrix-multiplication units (MXUs) are now prevalent in every computing platform. The key attribute that makes MXUs so successful is the semiring structure, which allows tiling for both parallelism and data reuse. Nonetheless, matrix-multiplication is not the only algorithm with such attributes. We find that many algorithms share the same structure and differ in only the core operation; for example, using add-minimum instead of multiply-add. Algorithms with a semiring-like structure therefore have potential to be accelerated by a general-purpose matrix operation architecture, instead of common MXUs.

In this paper, we propose SIMD², a new programming paradigm to support generalized matrix operations with a semiring-like structure. SIMD² instructions accelerate eight more types of matrix operations, in addition to matrix multiplications. Since SIMD² instructions resemble a matrix-multiplication instruction, we are able to build SIMD² architecture on top of any MXU architecture with minimal modifications. We developed a framework that emulates and validates SIMD² using NVIDIA GPUs with Tensor Cores. Across 8 applications, SIMD² provides up to 38.59× speedup and more than 6.94× on average over optimized CUDA programs, with only 5% of full-chip area overhead.

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Cited By

Hong CBhatia SHaan ADong SNikiforov DCheung AShao Y(2024)LLM-Aided Compilation for Tensor Accelerators2024 IEEE LLM Aided Design Workshop (LAD)10.1109/LAD62341.2024.10691748(1-14)Online publication date: 28-Jun-2024
https://doi.org/10.1109/LAD62341.2024.10691748
Hong CBhatia SHaan ADong SNikiforov DCheung AShaov Y(2024)LLM-Aided Compilation for Tensor Accelerators2024 IEEE LLM Aided Design Workshop (LAD)10.1109/LAD62341.2024.10691720(1-16)Online publication date: 28-Jun-2024
https://doi.org/10.1109/LAD62341.2024.10691720

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cover image ACM Conferences

ISCA '22: Proceedings of the 49th Annual International Symposium on Computer Architecture

June 2022

1097 pages

ISBN:9781450386104

DOI:10.1145/3470496

General Chairs:
Valentina Salapura
Google
,
Mohamed Zahran
New York University
,
Program Chairs:
Fred Chong
The University of Chicago
,
Lingjia Tang
The University of Michigan

Copyright © 2022 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Published: 11 June 2022

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ISCA '22: The 49th Annual International Symposium on Computer Architecture

June 18 - 22, 2022

New York, New York

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ISCA '22 Paper Acceptance Rate 67 of 400 submissions, 17%;

Overall Acceptance Rate 543 of 3,203 submissions, 17%

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Cited By

Hong CBhatia SHaan ADong SNikiforov DCheung AShao Y(2024)LLM-Aided Compilation for Tensor Accelerators2024 IEEE LLM Aided Design Workshop (LAD)10.1109/LAD62341.2024.10691748(1-14)Online publication date: 28-Jun-2024
https://doi.org/10.1109/LAD62341.2024.10691748
Hong CBhatia SHaan ADong SNikiforov DCheung AShaov Y(2024)LLM-Aided Compilation for Tensor Accelerators2024 IEEE LLM Aided Design Workshop (LAD)10.1109/LAD62341.2024.10691720(1-16)Online publication date: 28-Jun-2024
https://doi.org/10.1109/LAD62341.2024.10691720

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