We exploit the high memory bandwidth of AIcustomized Cerebras CS-2 systems for seismic processing. By leveraging low-rank matrix approximation, we fit memoryhungry seismic applications onto memory-austere SRAM waferscale hardware, thus addressing a challenge arising in many wave-equation-based algorithms that rely on Multi-Dimensional Convolution (MDC) operators. Exploiting sparsity inherent in seismic data in the frequency domain, we implement embarrassingly parallel tile low-rank matrix-vector multiplications (TLRMVM), which account for most of the elapsed time in MDC operations, to successfully 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 small local memories of Cerebras processing elements. Deploying TLR-MVM execution onto 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 AIcustomized architectures to enable a new generation of seismic algorithms that will empower multiple technologies of our lowcarbon future.
|Original language||English (US)|
|Title of host publication||ACM/IEEE International Conference for High Performance Computing, Networking, Storage, and Analysis (SC'23)|
|State||Published - Sep 11 2023|