Fast Stencil-Code Computation on a Wafer-Scale Processor
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Abstract (arXiv)
The performance of CPU-based and GPU-based systems is often low for PDE codes, where large, sparse, and often structured systems of linear equations must be solved. Iterative solvers are limited by data movement, both between caches and memory and between nodes. Here we describe the solution of such systems of equations on the Cerebras Systems CS-1, a wafer-scale processor that has the memory bandwidth and communication latency to perform well. We achieve 0.86 PFLOPS on a single wafer-scale system for the solution by BiCGStab of a linear system arising from a 7-point finite difference stencil on a 600 X 595 X 1536 mesh, achieving about one third of the machine's peak performance. We explain the system, its architecture and programming, and its performance on this problem and related problems. We discuss issues of memory capacity and floating point precision. We outline plans to extend this work towards full applications.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Oct 2020.
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Citing papers per year: 2021: 4; 2022: 3; 2023: 2; 2024: 1.
10 papers.
- 2022Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challengescited by 12
- 2024Demystifying AI Platform Design for Distributed Inference of Next-Generation LLM modelscited by 12
- 2023VEGETA: Vertically-Integrated Extensions for Sparse/Dense GEMM Tile Acceleration on CPUscited by 8
- 2021Bridging Data Center AI Systems with Edge Computing for Actionable Information Retrievalcited by 7
- 2022A Tale of Two Models: Constructing Evasive Attacks on Edge Modelscited by 6
- 2021Applications of physics-informed scientific machine learning in subsurface science: A surveycited by 2
- 2021Evaluating Spatial Accelerator Architectures with Tiled Matrix-Matrix Multiplicationcited by 2
- 2022An Experimental Evaluation of Machine Learning Training on a Real Processing-in-Memory Systemcited by 2
- 2021Implementation of a Binary Neural Network on a Passive Array of Magnetic Tunnel Junctionscited by 1
- 2023LoopTune: Optimizing Tensor Computations with Reinforcement Learningcited by 1
- 2024Demystifying AI Platform Design for Distributed Inference of Next-Generation LLM models3 Jun 2024
- 2023LoopTune: Optimizing Tensor Computations with Reinforcement Learning4 Sep 2023
- 2023VEGETA: Vertically-Integrated Extensions for Sparse/Dense GEMM Tile Acceleration on CPUs17 Feb 2023
- 2022An Experimental Evaluation of Machine Learning Training on a Real Processing-in-Memory System16 Jul 2022
- 2022A Tale of Two Models: Constructing Evasive Attacks on Edge Models22 Apr 2022
- 2022Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges23 Mar 2022
- 2021Implementation of a Binary Neural Network on a Passive Array of Magnetic Tunnel Junctions16 Dec 2021
- 2021Evaluating Spatial Accelerator Architectures with Tiled Matrix-Matrix Multiplication19 Jun 2021
- 2021Bridging Data Center AI Systems with Edge Computing for Actionable Information Retrieval28 May 2021
- 2021Applications of physics-informed scientific machine learning in subsurface science: A survey10 Apr 2021