建模收集和分散与硬件性能计数器为Xeon Phi

James Lin, Akira Nukada, S. Matsuoka
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引用次数: 1

摘要

Intel初始多核指令(IMCI)为Xeon Phi引入了硬件实现的收集和分散(G/S)从/到非连续内存位置加载/存储SIMD寄存器的内容。然而,它们可能是Xeon Phi的关键性能瓶颈之一。建模G/S可以提供对Xeon Phi处理器性能的洞察,然而,现有的解决方案需要手工编写的汇编实现。因此,我们使用硬件性能计数器对G/S进行建模,这些计数器可以通过PAPI等工具进行分析。我们将Address Generation Interlock (AGI)事件描述为G/S的数量,使用VPU_DATA_READ估计G/S的平均延迟,并将它们结合起来建模G/S的总延迟。我们将我们的模型应用于3D 7点模板,结果显示G/S花费了近40%的总内核时间。我们还通过实现带有intrinsic的无G/S版本来验证该模型。这项工作的贡献是一个用硬件计数器构建的G/S性能模型。我们相信该模型也可以普遍适用于CPU。
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Modeling Gather and Scatter with Hardware Performance Counters for Xeon Phi
Intel Initial Many-Core Instructions (IMCI) for Xeon Phi introduces hardware-implemented Gather and Scatter (G/S) load/store contents of SIMD registers from/to non-contiguous memory locations. However, they can be one of key performance bottlenecks for Xeon Phi. Modelling G/S can provide insights to the performance on Xeon Phi, however, the existing solution needs a hand-written assembly implementation. Therefore, we modeled G/S with hardware performance counters which can be profiled by the tools like PAPI. We profiled Address Generation Interlock (AGI) events as the number of G/S, estimated the average latency of G/S with VPU_DATA_READ, and combined them to model the total latencies of G/S. We applied our model to the 3D 7-point stencil and the result showed G/S spent nearly 40% of total kernel time. We also validated the model by implementing a G/S- free version with intrinsics. The contribution of the work is a performance model for G/S built with hardware counters. We believe the model can be generally applicable to CPU as well.
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