IEEE Trans Vis Comput Graph - Scheduling in Heterogeneous Computing Environments for Proximity Queries.

Tópicos

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Resumo

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 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. We also show that our method provides results that are close 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.

Resumo Limpo

present novel linear program lp base schedul algorithm exploit heterogen multicor architectur cpus gpus acceler wide varieti proxim queri repres complic perform relationship heterogen architectur differ comput proxim queri propos simpl yet accur model measur expect run time comput base model formul optim problem minim largest time spent comput resourc propos novel iter lpbase schedul algorithm sinc method general abl appli method various proxim queri differ characterist method achiev order magnitud perform improv use four differ gpus two hexacor cpus use hexacor cpu unlik prior schedul method method continu improv perform add comput resourc also method achiev much higher perform improv compar prior method heterogen comput resourc increas also show method provid result close perform provid conserv upper bound ideal throughput result demonstr effici robust algorithm achiev prior method

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