CMS-PAS-MLG-23-001

CMS-PAS-MLG-23-001
Portable Acceleration of CMS Production Workflow with Coprocessors as a Service
CMS Collaboration
2 October 2023

Abstract: Computing demands for large scientific experiments, such as the CMS experiment at CERN, will increase dramatically in the next decades. To complement the future performance increases of software running on CPUs, explorations of coprocessor usage in data processing hold great potential and interest. We explore the approach of Services for Optimized Network Inference on Coprocessors (SONIC) and study the deployment of this as-a-service approach in large-scale data processing. In the studies, we take a data processing workflow of the CMS experiment as an example, and run the main workflow on CPUs, while offloading several machine learning (ML) inference tasks onto either remote or local coprocessors, such as GPUs. With experiments performed at Google Cloud, the Purdue Tier-2 computing center, and combinations of the two, we demonstrate the acceleration of these ML algorithms individually on coprocessors and the corresponding throughput improvement for the entire workflow. This approach can be easily generalized to different types of coprocessors, and deployed on local CPUs without throughput performance decrease. We emphasize that SONIC enables high coprocessor usage and enables the portability to run workflows on different types of coprocessors.
Links: CDS record (PDF) ; CADI line (restricted) ; These preliminary results are superseded in this paper, Submitted to CSBS. The superseded preliminary plots can be found here.

CMS Publications