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Stencil calculations comprise an important class of kernels in many scienti?c computing applications ranging from simple PDE solvers to constituent kernels in multigrid methods as well as image processing applications. In such types of solvers, stencil kernels are often the dominant part of the computation, and an ef?cient parallel implementation of the kernel is therefore crucial in order to reduce the time to solution. However, in the current complex hardware microarchitectures, meticulous architecture-speci?c tuning is required to elicit the machine’s full compute power. We present a code generation and auto-tuning framework PATUS for stencil computations targeted at multi- and manycore processors, such as multicore CPUs and graphics processing units, which makes it possible to generate compute kernels from a speci?cation of the stencil operation and a parallelization and optimization strategy, and leverages the autotuning methodology to optimize strategydependent parameters for the given hardware architecture.

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