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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >Workload-Aware Optimal Power Allocation on Single-Chip Heterogeneous Processors
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Workload-Aware Optimal Power Allocation on Single-Chip Heterogeneous Processors

机译：单芯片异构处理器上的可识别工作负载的最佳功率分配

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摘要

As technology scales below 32 nm, manufacturers began to integrate both CPU and GPU cores in a single chip, i.e., single-chip heterogeneous processor (SCHP), to improve the throughput of emerging applications. In SCHPs, the CPU and the GPU share the total chip power budget while satisfying their own power constraints, respectively. Consequently, to maximize the overall throughput and/or power efficiency, both power budget and workload should be judiciously allocated to the CPU and the GPU. In this paper, we first demonstrate that optimal allocation of power budget and workload to the CPU and the GPU can provide 13 percent higher throughput than the optimal allocation of workload alone for a single-program workload scenario. Second, we also demonstrate that asymmetric power allocation considering per-program characteristics for a multi-programmed workload scenario can provide 9 percent higher throughput or 24 percent higher power efficiency than the even power allocation per program depending on the optimization objective. Last, we propose effective runtime algorithms that can determine near-optimal or optimal combinations of workload and power budget partitioning for both single- and multi-programmed workload scenarios; the runtime algorithms can achieve 96 and 99 percent of the maximum achievable throughput within 5-8 and 3-5 kernel invocations for single- and multi-programmed workload cases, respectively.
机译：随着技术规模扩展到32纳米以下，制造商开始将CPU和GPU内核都集成在单个芯片（即单芯片异构处理器（SCHP））中，以提高新兴应用程序的吞吐量。在SCHP中，CPU和GPU共享总芯片功耗预算，同时分别满足其自身的功耗约束。因此，为了最大程度地提高整体吞吐量和/或电源效率，应该明智地将电源预算和工作负载分配给CPU和GPU。在本文中，我们首先证明，针对单程序工作负载方案，将功率预算和工作负载分配给CPU和GPU的最佳性能比单独分配工作负载的最佳性能高13％。其次，我们还证明，根据优化目标，考虑到多程序工作负载方案的每个程序特性的不对称功率分配可以提供比每个程序均匀功率分配高9％的吞吐量或24％的功率效率。最后，我们提出了有效的运行时算法，可以为单程序和多程序工作负载方案确定工作负载和电源预算划分的最佳组合。对于单程序和多程序负载情况，运行时算法在5-8和3-5个内核调用中分别可以达到最大可实现吞吐量的96％和99％。

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