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Apple Silicon Performance in Scientific Computing

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arxiv 2211.00720 v1 pith:POJ3H3GS submitted 2022-11-01 cs.DC physics.comp-ph

Connor Kenyon , Collin Capano This is my paper · ORCID
classification cs.DCphysics.comp-ph
keywords applecomputingnvidiaperformanceprocessorsscientificsiliconbenchmarks
verification ladder T0 review T1 audit T2 compute T3 formal
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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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  1. Apple vs. Oranges: Evaluating the Apple Silicon M-Series SoCs for HPC Performance and Efficiency

    cs.AR 2025-02 conditional novelty 5.0 of 10

    Apple Silicon M-Series chips achieve up to 2.9 FP32 TFLOPS and over 200 GFLOPS per watt, making them energy-efficient but low-absolute-performance HPC options.

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