# Apple Silicon Performance in Scientific Computing

arXiv: https://arxiv.org/abs/2211.00720 · HTML: https://mlsanity.com/paper/2211.00720/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2022.

## Built on by (most cited first, 3 of 3)

- [DeepPCR: Parallelizing Sequential Operations in Neural Networks](https://mlsanity.com/paper/2309.16318/) (2023)
- [Out of the Box, into the Clinic? Evaluating State-of-the-Art ASR for Clinical Applications for Older Adults](https://mlsanity.com/paper/2508.08684/) (2025)
- [Benchmarking On-Device Machine Learning on Apple Silicon with MLX](https://mlsanity.com/paper/2510.18921/) (2025)

## Abstract (arXiv)

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.