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SCALING THE “MEMORY WALL” FOR MULTI-DIMENSIONAL SEISMIC PROCESSING WITH
ALGEBRAIC COMPRESSION ON CEREBRAS CS-2 SYSTEMS

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Name:
2023_gb_seismic.pdf
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3.701Mb
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Beschreibung:
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TYPE

Conference Paper

AUTHORS

Ltaief, Hatem
Hong, Yuxi
Wilson, Leighton
Jacquelin, Mathias
Ravasi, Matteo
Keyes, David E.

KAUST DEPARTMENT

Extreme Computing Research Center
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Computer Science Program
Earth Science and Engineering Program
Physical Science and Engineering (PSE) Division
Applied Mathematics and Computational Science Program
Office of the President

DATE

2023-09-11

PERMANENT LINK TO THIS RECORD

http://hdl.handle.net/10754/694388


METADATA

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ABSTRACT

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.

SPONSORS

For computer time, this research used the resources of the Ibex NVIDIA GPU
cluster of the Supercomputing Laboratory (KSL) at King Abdullah University of
Science and Technology (KAUST) in Thuwal, Saudi Arabia and the Condor Galaxy-1
CS-2 cluster provided by G42.

PUBLISHER

ACM/IEEE

CONFERENCE/EVENT NAME

ACM/IEEE International Conference for High Performance Computing, Networking,
Storage, and Analysis (SC'23)

COLLECTIONS

Conference Papers; Applied Mathematics and Computational Science Program;
Physical Science and Engineering (PSE) Division; Extreme Computing Research
Center; Computer Science Program; Earth Science and Engineering Program;
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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