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Speculative Execution on Multi-GPU Systems

Gregory Diamos, Sudhakar Yalamanchili
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0250
IEEE International Symposium on Parallel & Distributed Processing (IPDPS), 2010

@conference{diamos2010speculative,

   title={Speculative execution on multi-GPU systems},

   author={Diamos, G. and Yalamanchili, S.},

   booktitle={Parallel & Distributed Processing (IPDPS), 2010 IEEE International Symposium on},

   pages={1–12},

   issn={1530-2075},

   year={2010},

   organization={IEEE}

}

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The lag of parallel programming models and languages behind the advance of heterogeneous many-core processors has left a gap between the computational capability of modern systems and the ability of applications to exploit them. Emerging programming models, such as CUDA and OpenCL, force developers to explicitly partition applications into components (kernels) and assign them to accelerators in order to utilize them effectively. An accelerator is a processor with a different ISA and micro-architecture than the main CPU. These static partitioning schemes are effective when targeting a system with only a single accelerator. However, they are not robust to changes in the number of accelerators or the performance characteristics of future generations of accelerators. In previous work, we presented the Harmony execution model for computing on heterogeneous systems with several CPUs and accelerators. In this paper, we extend Harmony to target systems with multiple accelerators using control speculation to expose parallelism. We refer to this technique as Kernel Level Speculation (KLS). We argue that dynamic parallelization techniques such as KLS are sufficient to scale applications across several accelerators based on the intuition that there will be fewer distinct accelerators than cores within each accelerator. In this paper, we use a complete prototype of the Harmony runtime that we developed to explore the design decisions and trade-offs in the implementation of KLS. We show that KLS improves parallelism to a sufficient degree while retaining a sequential programming model. We accomplish this by demonstrating good scaling of KLS on a highly heterogeneous system with three distinct accelerator types and ten processors.
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