Portable Performance on Heterogeneous Architectures

Phitchaya Mangpo Phothilimthana, Jason Ansel, Jonathan Ragan-Kelley, Saman Amarasinghe
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology
Eighteenth International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2013

   title={Portable Performance on Heterogeneous Architectures},

   author={Phothilimthana, P.M. and Ansel, J. and Ragan-Kelley, J. and Amarasinghe, S.},



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Trends in both consumer and high performance computing are bringing not only more cores, but also increased heterogeneity among the computational resources within a single machine. In many machines, one of the greatest computational resources is now their graphics coprocessors (GPUs), not just their primary CPUs. But GPU programming and memory models differ dramatically from conventional CPUs, and the relative performance characteristics of the different processors vary widely between machines. Different processors within a system often perform best with different algorithms and memory usage patterns, and achieving the best overall performance may require mapping portions of programs across all types of resources in the machine. To address the problem of efficiently programming machines with increasingly heterogeneous computational resources, we propose a programming model in which the best mapping of programs to processors and memories is determined empirically. Programs define choices in how their individual algorithms may work, and the compiler generates further choices in how they can map to CPU and GPU processors and memory systems. These choices are given to an empirical autotuning framework that allows the space of possible implementations to be searched at installation time. The rich choice space allows the autotuner to construct polyalgorithms that combine many different algorithmic techniques, using both the CPU and the GPU, to obtain better performance than any one technique alone. Experimental results show that algorithmic changes, and the varied use of both CPUs and GPUs, are necessary to obtain up to a 16.5x speedup over using a single program configuration for all architectures.
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