The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Authors: David Keyes, Hatem Ltaief, and Yuxi Hong (King Abdullah University of Science and Technology (KAUST)); Leighton Wilson and Mathias Jacquelin (Cerebras Systems, Inc.); and Matteo Ravasi (King Abdullah University of Science and Technology (KAUST))
Abstract: We exploit the high memory bandwidth of AI-customized Cerebras CS-2 systems for seismic processing. Through low-rank matrix approximation, memory hungry seismic applications fit onto memory-austere SRAM waferscale hardware, addressing a challenge arising in many wave-equation-based algorithms that rely on multi-dimensional convolution operators. Exploiting sparsity inherent in seismic data in the frequency domain, we implement embarrassingly parallel tile low-rank matrix-vector multiplications (TLR-MVM), which account for most of the elapsed time in MDC operations, to solve the Multi-Dimensional Deconvolution (MDD) inverse problem. By reducing memory footprint along with arithmetic complexity, we fit a standard seismic benchmark dataset into the local memories of Cerebras processing elements. TLR-MVM on 48 CS-2 systems in support of MDD gives a sustained memory bandwidth of 92.58PB/s on 35,784,000 processing elements, a significant milestone that highlights the capabilities of AI-customized architectures to enable a new generation of seismic algorithms that will empower multiple technologies of our low-carbon future.
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