Orchestrating Multiple Data-Parallel Kernels on Multiple Devices

Janghaeng Lee, Mehrzad Samadi, Scott Mahlke
University of Michigan, Ann Arbor
24th International Conference on Parallel Architectures and Compilation Techniques (PACT), 2015


   title={Orchestrating Multiple Data-Parallel Kernels on Multiple Devices},

   author={Lee, Janghaeng and Samadi, Mehrzad and Mahlke, Scott},



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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. 52% of this speedup comes from the coarse-grained scheduling and the other 48% is the result of the fine-grained partitioning.
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