Software Resource Disaggregation for HPC with Serverless Computing
DescriptionAggregated HPC resources have rigid allocation systems and programming models and struggle to adapt to diverse and changing workloads. Thus, HPC systems fail to efficiently use the large pools of unused memory and increase the utilization of idle computing resources. Prior work attempted to increase the throughput and efficiency of supercomputing systems through workload co-location and resource disaggregation. However, these methods fall short of providing a solution that can be applied to existing systems without major hardware modifications and performance losses.

In this project, we use the new cloud paradigm of serverless computing to improve the utilization of supercomputers. We show that the FaaS programming model satisfies the requirements of high-performance applications and how idle memory helps resolve cold startup issues. We demonstrate a software resource disaggregation approach where the co-location of functions allows idle cores and accelerators to be utilized while retaining near-native performance.
Event Type
ACM Student Research Competition: Graduate Poster
ACM Student Research Competition: Undergraduate Poster
TimeTuesday, 15 November 20228:30am - 5pm CST
Registration Categories
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