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The computational power and the physical memory size of a single GPU device are often insufficient for large-scale problems. Using CUDA, the user must explicitly partition such problems into several tasks repeating the data transfer and kernel execution. To use multiple GPUs, explicit device switching is also needed. Furthermore, low-level hand optimizations such as load balancing and determining...
Graphics processing units (GPUs) have emerged as important players in the transition of the computing industry from sequential to multi- and many-core computing. We propose a software framework for execution of domain-specific parallel templates on GPUs, which simultaneously raises the abstraction level of GPU programming and ensures efficient execution with forward scalability to large data sizes...
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