Abstract
Graphics Processing Units (GPUs) have been used in general purpose computing for several years. The newly introduced Dynamic Parallelism feature of Nvidia's Kepler GPUs allows launching kernels from the GPU directly. However, the naive use of this feature can cause a high number of nested kernel launches, each performing limited work, leading to GPU underutilization and poor performance. We propose workload consolidation mechanisms at different granularities to maximize the work performed by nested kernels and reduce their overhead. Our end goal is to design automatic code transformation techniques for applications with irregular nested loops.
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