Design and evaluation of the GeMTC framework for GPU-enabled many task computing

We present the design and first performance and usability evaluation of GeMTC, a novel execution model and run-time system that enables accelerators to be programmed with many concurrent and independent tasks of potentially short or variable duration. With GeMTC, a broad class of such “many-task ” applications can leverage the increas-ing number of accelerated and hybrid high-end computing systems. GeMTC overcomes the obstacles to using GPUs in a many-task manner by scheduling and launching inde-pendent tasks on hardware designed for SIMD-style vector processing. We demonstrate the use of a high-level MTC programming model (the Swift parallel dataflow language) to run tasks on many accelerators and thus provide a high-productivity programming model for the growing number of supercomputers that are accelerator-enabled. While still in an experimental stage, GeMTC can already support tasks of fine (subsecond) granularity and execute concurrent hetero-geneous tasks on 86,000 independent GPU warps spanning 2.7M GPU threads on the Blue Waters supercomputer