Improving Predication Efficiency through Compaction/Restoration of SIMD Instructions Article Swipe
Adrián Barredo
,
Juan M. Cebrián
,
Miquel Moretó
,
Marc Casas
,
Mateo Valero
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/hpca47549.2020.00064
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/hpca47549.2020.00064
Vector processors offer a wide range of unexplored opportunities to improve performance and energy efficiency. However, despite its potential, vector code generation and execution have significant challenges, the most relevant ones being control flow divergence. Most modern processors including SIMD extensions (such as AVX) rely on predication to support divergence control. In predicated codes, performance and energy consumption are usually insensitive to the number of true values in a predicated mask. This implies that the system efficiency becomes sub-optimal as vector length increases. In this paper we focus on SIMD extensions and propose a novel approach to improve execution efficiency in predicated SIMD instructions, the Compaction/Restoration (CR) technique. CR delays predicated SIMD instructions with inactive elements and compacts them with instances of the same instruction from different loop iterations to form an equivalent dense vector instruction, where, in the best case, all the elements are active. After executing such dense instructions, their results are restored to the original instructions. Our evaluation shows that CR improves performance by up to 25% and reduces dynamic energy consumption by up to 43% on real unmodified applications with predicated execution. Moreover, CR allows executing unmodified legacy code with short vector instructions (AVX-2) on newer architectures with wider vectors (AVX-512), achieving up to 56% performance benefits.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/hpca47549.2020.00064
- OA Status
- green
- Cited By
- 7
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3016723581
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3016723581Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/hpca47549.2020.00064Digital Object Identifier
- Title
-
Improving Predication Efficiency through Compaction/Restoration of SIMD InstructionsWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2020Year of publication
- Publication date
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2020-02-01Full publication date if available
- Authors
-
Adrián Barredo, Juan M. Cebrián, Miquel Moretó, Marc Casas, Mateo ValeroList of authors in order
- Landing page
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https://doi.org/10.1109/hpca47549.2020.00064Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://hdl.handle.net/2117/341626Direct OA link when available
- Concepts
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SIMD, Computer science, Parallel computing, Control flow, Code (set theory), Energy consumption, Instruction set, Code generation, Performance improvement, Programming language, Operating system, Electrical engineering, Key (lock), Economics, Operations management, Set (abstract data type), EngineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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7Total citation count in OpenAlex
- Citations by year (recent)
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2023: 1, 2022: 3, 2021: 3Per-year citation counts (last 5 years)
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36Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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