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A ML-based Runtime System for Executing Dataflow Graphs on Heterogeneous Processors

This paper briefly describes the design a system that addresses the challenge of scheduling distributed data-analytics workloads on heterogeneous processing fabrics (which include CPUs, GPUs, FPGAs and ASICs) in cloud-based, dynamic, multi-tenant environments. To demonstrate the capabilities of the proposed system, we use a computational genomics workflow that addresses the growing need for rapid genomic analyses in hospital environments [6]. Accelerators such as GPUs, FPGAs and other ASICs have become an integral component of modern datacenters [5]. However, several key issues remain for extensive user adoption of these technologies: (1) identifying acceleration targets and designing accelerators for distributed applications; (2) composing these accelerators using data-flow graphs (DFGs) to build applications; and (3) optimally orchestrating, deploying and executing these DFGs in a dynamically