CoreTSAR: Adaptive Worksharing for Heterogeneous Systems

The popularity of heterogeneous computing continues to increase rapidly due to the high peak performance, favorable energy efficiency, and comparatively low cost of accelerators. However, heterogeneous programming models still lack the flexibility of their CPU-only counterparts. Accelerated OpenMP models, including OpenMP 4.0 and OpenACC, ease the migration of code from CPUs to GPUs but lack much of OpenMP’s flexibility: OpenMP applications can run on any number of CPUs without extra user effort, but GPU implementations do not offer similar adaptive worksharing across GPUs in a node, nor do they employ a mix of CPUs and GPUs. To address these shortcomings, we present CoreTSAR, our library for scheduling

core

s via a

t

ask-

s

ize

a

dapting

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untime system by supporting worksharing of loop nests across arbitrary heterogeneous resources. Beyond scheduling the computational load across devices, CoreTSAR includes a memory-management system that operates based on task association, enabling the runtime to dynamically manage memory movement and task granularity. Our evaluation shows that CoreTSAR can provide nearly linear scaling to four GPUs and all cores in a node

without

modifying the code within the parallel region. Furthermore, CoreTSAR provides portable performance across a variety of system configurations.