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Apple Silicon Performance in Scientific Computing

Connor Kenyon, Collin Capano
Physics Department and the Center for Scientific Computing and Data Science Research at the University of Massachusetts Dartmouth, North Dartmouth, MA 02747
arXiv:2211.00720 [cs.DC], (1 Nov 2022)

@misc{https://doi.org/10.48550/arxiv.2211.00720,

   doi={10.48550/ARXIV.2211.00720},

   url={https://arxiv.org/abs/2211.00720},

   author={Kenyon, Connor and Capano, Collin},

   keywords={Distributed, Parallel, and Cluster Computing (cs.DC), Computational Physics (physics.comp-ph), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Physical sciences, FOS: Physical sciences},

   title={Apple Silicon Performance in Scientific Computing},

   publisher={arXiv},

   year={2022},

   copyright={arXiv.org perpetual, non-exclusive license}

}

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With the release of the Apple Silicon System-on-a-Chip processors, and the impressive performance shown in general use by both the M1 and M1 Ultra, the potential use for Apple Silicon processors in scientific computing is explored. Both the M1 and M1 Ultra are compared to current state-of-the-art data-center GPUs, including an NVIDIA V100 with PCIe, an NVIDIA V100 with NVLink, and an NVIDIA A100 with PCIe. The scientific performance is measured using the Scalable Heterogeneous Computing (SHOC) benchmark suite using OpenCL benchmarks. We find that both M1 processors outperform the GPUs in all benchmarks.
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