Fractal
| Autor: | Victor A. Ying, Hyun Ryong Lee, Mark C. Jeffrey, Suvinay Subramanian, Maleen Abeydeera, Joel Emer, Daniel Sanchez |
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| Rok vydání: | 2017 |
| Předmět: |
010302 applied physics
Multi-core processor Fine grain parallelism Data parallelism Computer science Speculative execution Parallel algorithm Transactional memory Task parallelism Parallel computing 02 engineering and technology General Medicine Scalable parallelism 01 natural sciences 020202 computer hardware & architecture Memory-level parallelism 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Speculative multithreading Implicit parallelism Instruction-level parallelism Execution model |
| Zdroj: | ISCA MIT web domain |
| ISSN: | 0163-5964 |
| DOI: | 10.1145/3140659.3080218 |
| Popis: | Most systems that support speculative parallelization, like hardware transactional memory (HTM), do not support nested parallelism. This sacrifices substantial parallelism and precludes composing parallel algorithms. And the few HTMs that do support nested parallelism focus on parallelizing at the coarsest (shallowest) levels, incurring large overheads that squander most of their potential. We present FRACTAL, a new execution model that supports unordered and timestamp-ordered nested parallelism. FRACTAL lets programmers seamlessly compose speculative parallel algorithms, and lets the architecture exploit parallelism at all levels. FRACTAL can parallelize a broader range of applications than prior speculative execution models. We design a FRACTAL implementation that extends the Swarm architecture and focuses on parallelizing at the finest (deepest) levels. Our approach sidesteps the issues of nested parallel HTMs and uncovers abundant fine-grain parallelism. As a result, FRACTAL outperforms prior speculative architectures by up to 88x at 256 cores. |
| Databáze: | OpenAIRE |
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