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Optimizing Performance of GPU Applications with SM Activity Divergence Minimization

GPUs are nowadays major parts of high performance computing systems. They provide high rates of computation capabilities and are ideal for number crunching applications. Providing methods that will further boost GPU throughput can have a distinctive impact in computing and accelerate many operations. At the same time, continuous usage of computing systems raises the demand for hardware designs that will ensure system reliability and potentially improve performance during a system's lifetime. This paper presents an allocation method for GPU processing units that improves applications' throughput, even by 33%, without sacrificing usage homogeneity among the computing resources of the GPU, and minimizes reliability issues.