PARTANS: An autotuning framework for stencil computation on multi-GPU systems
ACM Transactions on Architecture and Code Optimization (TACO), 2013•dl.acm.org
GPGPUs are a powerful and energy-efficient solution for many problems. For higher
performance or larger problems, it is necessary to distribute the problem across multiple
GPUs, increasing the already high programming complexity. In this article, we focus on
abstracting the complexity of multi-GPU programming for stencil computation. We show that
the best strategy depends not only on the stencil operator, problem size, and GPU, but also
on the PCI express layout. This adds nonuniform characteristics to a seemingly …
performance or larger problems, it is necessary to distribute the problem across multiple
GPUs, increasing the already high programming complexity. In this article, we focus on
abstracting the complexity of multi-GPU programming for stencil computation. We show that
the best strategy depends not only on the stencil operator, problem size, and GPU, but also
on the PCI express layout. This adds nonuniform characteristics to a seemingly …
GPGPUs are a powerful and energy-efficient solution for many problems. For higher performance or larger problems, it is necessary to distribute the problem across multiple GPUs, increasing the already high programming complexity.
In this article, we focus on abstracting the complexity of multi-GPU programming for stencil computation. We show that the best strategy depends not only on the stencil operator, problem size, and GPU, but also on the PCI express layout. This adds nonuniform characteristics to a seemingly homogeneous setup, causing up to 23% performance loss. We address this issue with an autotuner that optimizes the distribution across multiple GPUs.
ACM Digital Library