Performance Analysis of Hybrid CPU/GPU Environments

Smith, Michael Shawn

doi:10.15760/etd.300

Cited by 1 publication

(1 citation statement)

References 33 publications

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“…Few works gave attention to modeling overheads such as data communication costs [15]. Performance models for GPUs [24], [25] and heterogeneous framework [26], [27] recently have been proposed. These architectural performance models can be very useful to accurately predict the computational time of jobs in a broad set of GPUs.…”

Section: Performance Modelsmentioning

confidence: 99%

Scheduling in Heterogeneous Computing Environments for Proximity Queries

Kim

Lee

et al. 2013

IEEE Trans. Visual. Comput. Graphics

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Abstract-We present a novel, Linear Programming (LP) based scheduling algorithm that exploits heterogeneous multi-core architectures such as CPUs and GPUs to accelerate a wide variety of proximity queries. To represent complicated performance relationships between heterogeneous architectures and different computations of proximity queries, we propose a simple, yet accurate model that measures the expected running time of these computations. Based on this model, we formulate an optimization problem that minimizes the largest time spent on computing resources, and propose a novel, iterative LP-based scheduling algorithm. Since our method is general, we are able to apply our method into various proximity queries used in five different applications that have different characteristics. Our method achieves an order of magnitude performance improvement by using four different GPUs and two hexa-core CPUs over using a hexa-core CPU only. Unlike prior scheduling methods, our method continually improves the performance, as we add more computing resources. Also, our method achieves much higher performance improvement compared with prior methods as heterogeneity of computing resources is increased. Moreover, for one of tested applications, our method achieves even higher performance than a prior parallel method optimized manually for the application. We also show that our method provides results that are close (e.g., 75%) to the performance provided by a conservative upper bound of the ideal throughput. These results demonstrate the efficiency and robustness of our algorithm that have not been achieved by prior methods. In addition, we integrate one of our contributions with a work stealing method. Our version of the work stealing method achieves 18% performance improvement on average over the original work stealing method. This result shows wide applicability of our approach.

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