Publication | Open Access
Accelerating Scientific Applications With SambaNova Reconfigurable Dataflow Architecture
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2021
Year
Artificial IntelligenceCluster ComputingEngineeringAdvanced ComputingComputer ArchitectureHigh Performance ComputingScience InsightsData ScienceParallel ComputingHigh-throughput ComputingNext Generation ComputingComputer EngineeringComputer ScienceSambaflow Software StackData-intensive ComputingWorkflow ExecutionScientific ApplicationsScientific Workflow SystemScience User FacilityCloud ComputingParallel ProgrammingSystem SoftwareBig Data
Our exploratory work finds that the SambaNova Reconfigurable Dataflow Architecture (RDA) along with the SambaFlow software stack provides for an attractive system and solution to accelerate AI for science workloads. We have observed the efficacy of using the system with a diverse set of science applications and reasoned their suitability for performance gains over traditional hardware. As the Data-Scale system provides for a very large memory capacity, the system can be used to train models that typically do not fit in a GPU. The architecture also provides for deeper integration with upcoming supercomputers at the Argonne Leadership Computing Facility (ALCF), a US Department of Energy Office of Science user facility, to help advance science insights.