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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.02233 |
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| _version_ | 1866912410886995968 |
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| author | Mohamed, Mohamed Husain Noor Patil, Adarsh Ionkov, Latchesar Van Hensbergen, Eric |
| author_facet | Mohamed, Mohamed Husain Noor Patil, Adarsh Ionkov, Latchesar Van Hensbergen, Eric |
| contents | The wider adoption of tightly coupled core-adjacent accelerators, such as Arm Scalable Matrix Extension (SME), hinges on lowering software programming complexity. In this paper, we focus on enabling the use of SME architecture in Streaming Scalable Vector Extension (SSVE) mode for workloads written in C/C++. While current compilers optimize loops for all types of SIMD instructions, these techniques primarily target vector units within the core and falter when applied to disaggregated, core-adjacent SIMD accelerators. Our goal is to enable the compiler to automatically generate code for such accelerators only when profitable.
To this end, we investigate a path towards performant, precise, and repeatable computation offloading through two compiler ecosystems. We revisit LLVM compiler passes, MLIR transforms and their associated cost models, and heuristics. We hope that these insights can provide directions for evolving compiler capabilities towards automatic code generation for this next-generation vector processing paradigm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_02233 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improving compiler support for SIMD offload using Arm Streaming SVE Mohamed, Mohamed Husain Noor Patil, Adarsh Ionkov, Latchesar Van Hensbergen, Eric Programming Languages The wider adoption of tightly coupled core-adjacent accelerators, such as Arm Scalable Matrix Extension (SME), hinges on lowering software programming complexity. In this paper, we focus on enabling the use of SME architecture in Streaming Scalable Vector Extension (SSVE) mode for workloads written in C/C++. While current compilers optimize loops for all types of SIMD instructions, these techniques primarily target vector units within the core and falter when applied to disaggregated, core-adjacent SIMD accelerators. Our goal is to enable the compiler to automatically generate code for such accelerators only when profitable. To this end, we investigate a path towards performant, precise, and repeatable computation offloading through two compiler ecosystems. We revisit LLVM compiler passes, MLIR transforms and their associated cost models, and heuristics. We hope that these insights can provide directions for evolving compiler capabilities towards automatic code generation for this next-generation vector processing paradigm. |
| title | Improving compiler support for SIMD offload using Arm Streaming SVE |
| topic | Programming Languages |
| url | https://arxiv.org/abs/2506.02233 |