Orchestrating Multiple Data-Parallel Kernels on Multiple Devices

Abstract

Traditionally, programmers and software tools have focused on mapping a single data-parallel kernel onto a heterogeneous computing system consisting of multiple general-purpose processors (CPUS) and graphics processing units (GPUs). These methodologies break down as application complexity grows to contain multiple communicating data-parallel kernels. This paper introduces MKMD, an automatic system for mapping multiple kernels across multiple computing devices in a seamless manner. MKMD is a two phased approach that combines coarse grain scheduling of indivisible kernels followed by opportunistic fine-grained workgroup-level partitioning to exploit idle resources. During this process, MKMD considers kernel dependencies and the underlying systems along with the execution time model built with a few sets of profile data. With the scheduling decision, MKMD transparently manages the order of executions and data transfers for each device. On a real machine with one CPU and two different GPUs, MKMD achieves a mean speedup of 1.89x compared to the in-order execution on the fastest device for a set of applications with multiple kernels. 53% of this speedup comes from the coarse-grained scheduling and the other 47% is the result of the fine-grained partitioning.