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Portable Acceleration of CMS Computing Workflows with Coprocessors as a Service.

, A Hayrapetyan1, A Tumasyan1,2

  • 1Yerevan Physics Institute, Yerevan, Armenia.

Computing and Software for Big Science
|September 9, 2024
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Summary
This summary is machine-generated.

Services for Optimized Network Inference on Coprocessors (SONIC) accelerates machine learning inference for scientific experiments. This approach improves data processing throughput by offloading tasks to coprocessors like GPUs.

Keywords:
CMSMachine learningOffline and computing

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Area of Science:

  • High-energy physics computing
  • Machine learning infrastructure

Background:

  • Scientific experiments like the CMS experiment at CERN's LHC face escalating computing demands.
  • Central processing units (CPUs) alone may not meet future performance needs.
  • Coprocessors offer architectural advantages for specific computational tasks.

Purpose of the Study:

  • To explore the Services for Optimized Network Inference on Coprocessors (SONIC) approach for large-scale data processing.
  • To evaluate the deployment of SONIC as a service for scientific workflows.
  • To demonstrate the benefits of offloading machine learning inference tasks to coprocessors.

Main Methods:

  • Implemented a data processing workflow from the CMS experiment.
  • Executed the main workflow on CPUs and offloaded machine learning inference tasks to Graphics Processing Units (GPUs) as coprocessors.
  • Conducted experiments across Google Cloud and the Purdue Tier-2 computing center.

Main Results:

  • Achieved individual acceleration of machine learning algorithms on coprocessors.
  • Demonstrated significant throughput improvement for the entire data processing workflow.
  • Validated the SONIC approach's effectiveness in diverse cloud and local computing environments.

Conclusions:

  • The SONIC approach effectively accelerates machine learning inference in scientific data processing.
  • SONIC enables high coprocessor utilization and workflow portability across different coprocessor types.
  • This method can be generalized to various coprocessors and deployed on local CPUs without performance degradation.