基于AI规划启发式和机器学习的异构系统优化：一种性能和能量感知方法
Optimization of Heterogeneous Systems with AI Planning Heuristics and Machine Learning: A Performance and Energy Aware Approach
摘要：
异构计算系统提供高性能和高能效。然而，为了最佳地利用这些系统，需要跨主机CPU和加速设备分配工作的解决方案。在本文中，我们提出了一种性能和能量感知方法，该方法将用于参数空间探索的AI规划算法与用于性能和能量评估的机器学习模型相结合，以确定接近最优的系统配置。对于数据并行应用程序，我们的方法确定了一个接近最佳的主机设备分配的工作，所需的处理单元的数量和相应的调度策略。我们评估了我们的方法，用于使用GPU或英特尔至强融核加速的各种异构系统。实验结果表明，我们的方法找到一个接近最优的系统配置，通过评估只有约7%的合理配置。此外，使用我们的机器学习模型的系统配置的每焦耳性能估计比通过程序执行的系统评估快1000倍以上。
英文摘要：
Heterogeneous computing systems provide high performance and energy efficiency. However, to optimally utilize such systems, solutions that distribute the work across host CPUs and accelerating devices are needed. In this paper, we present a performance and energy aware approach that combines AI planning heuristics for parameter space exploration with a machine learning model for performance and energy evaluation to determine a near-optimal system configuration. For data-parallel applications our approach determines a near-optimal host-device distribution of work, number of processing units required and the corresponding scheduling strategy. We evaluate our approach for various heterogeneous systems accelerated with GPU or the Intel Xeon Phi. The experimental results demonstrate that our approach finds a near-optimal system configuration by evaluating only about 7% of reasonable configurations. Furthermore, the performance per Joule estimation of system configurations using our machine learning model is more than 1000x faster compared to the system evaluation by program execution.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
