xkaapi中数据流程序调度：一种新的异构体系结构下基于亲和度的调度算法
Scheduling data flow program in xkaapi: A new affinity based Algorithm for Heterogeneous Architectures
摘要：
在异构混合体系结构上高效实现并行应用需要在计算和与加速器设备的通信之间进行仔细的平衡。即使大部分的通信时间可以被计算重叠，减少通信数据的总量也是至关重要的。因此，文献中有大量的ad-hoc方法来达到这种平衡，但这些方法依赖于体系结构和应用程序。我们在这里提出了一个通用的机制来自动优化CPU和GPU之间的调度，并比较两种策略在此机制：经典的异构最早完成时间（HEFT）算法和我们的新的，参数化的，分布式亲和对偶近似算法（DADA），它包括在分组的任务之前，运行一个快速的对偶近似。我们在一个异构并行机上进行了实验，该并行机具有六个CPU内核和八个NVIDIA Fermi GPU。来自PLASMA库的三个标准密集线性代数内核已经移植到Xkaapi运行时之上。我们报道他们的表演。结果表明，HEFT和DADA在各种实验条件下表现良好，但DADA在较大的系统和GPU数量上表现更好，并且在大多数情况下，产生的数据传输比HEFT低得多，以实现相同的性能。
英文摘要：
Efficient implementations of parallel applications on heterogeneous hybrid architectures require a careful balance between computations and communications with accelerator devices. Even if most of the communication time can be overlapped by computations, it is essential to reduce the total volume of communicated data. The literature therefore abounds with ad-hoc methods to reach that balance, but that are architecture and application dependent. We propose here a generic mechanism to automatically optimize the scheduling between CPUs and GPUs, and compare two strategies within this mechanism: the classical Heterogeneous Earliest Finish Time (HEFT) algorithm and our new, parametrized, Distributed Affinity Dual Approximation algorithm (DADA), which consists in grouping the tasks by affinity before running a fast dual approximation. We ran experiments on a heterogeneous parallel machine with six CPU cores and eight NVIDIA Fermi GPUs. Three standard dense linear algebra kernels from the PLASMA library have been ported on top of the Xkaapi runtime. We report their performances. It results that HEFT and DADA perform well for various experimental conditions, but that DADA performs better for larger systems and number of GPUs, and, in most cases, generates much lower data transfers than HEFT to achieve the same performance.
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。
