Fine-grained accelerator partitioning for Machine Learning and Scientific Computing in Function as a Service Platform

Function-as-a-service (FaaS) is a promising execution environment for high-performance computing (HPC) and machine learning (ML) applications as it offers developers a simple way to write and deploy programs. Nowadays, GPUs and other accelerators are indispensable for HPC and ML workloads. These accelerators are expensive to acquire and operate; consequently, multiplexing them can increase their financial profitability. However, we have observed that state-of-the-art FaaS frameworks usually treat accelerator as a single device to run single workload and have little support for multiplexing accelerators.