Abstract
Modern parallel computing hardware demands increasingly specialized attention to the details of scheduling and load balancing across heterogeneous execution resources that may include GPU and cloud environments, in addition to traditional CPUs. Many existing solutions address the challenges of particular resources, but do so in isolation, and in general do not compose within larger systems. We propose a general, composable abstraction for execution resources, along with a continuation-based meta-scheduler that harnesses those resources in the context of a deterministic parallel programming library for Haskell. We demonstrate performance benefits of combined CPU/GPU scheduling over either alone, and of combined multithreaded/distributed scheduling over existing distributed programming approaches for Haskell.
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