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Application aware Scalable Architecture for GPGPU

机译：用于GPGPU的应用意识可扩展架构

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摘要

Modern General Purpose Graphic Processing Units (GPGPU) offer high throughput for parallel applications with their hundreds of integrated cores. However, there are applications that experience performance saturation and even degradation with increasing number of cores. At present the scheduler in the GPU hardware allocates all the available resources to maximize their utilization. We observed that applications have preference towards specific set of resources. The utilization of other redundant resources can reduce the throughput of the applications. To overcome this problem, in this paper we first classify the applications into two types; type-I that dominantly require processing cores and type-II that rely on the performance of the memory-system. We propose an Application aware Scalable Architecture (ApSA) for GPGPU based on classified applications which performs run-time tailoring of the GPU resources to present an optimal set of resources to the running application. The results are analyzed and compared in terms of instructions per cycle, bandwidth utilization and branch divergence. We found that if the application is identified to be of type-I with the proposed technique the average profiling overhead is 1.6%. Type-II applications experience average profiling overhead of 1.15%. The average power saved by clock-gating redundant resources in the case of type-II applications is 20.08%.
机译：现代通用图形处理单元（GPGPU）为具有数百个集成核心的并行应用提供高吞吐量。但是，存在在越来越多的核心越来越低劣的应用程序。目前GPU硬件中的调度程序分配所有可用资源以最大限度地提高其利用率。我们观察到申请偏好了对特定的资源集。其他冗余资源的利用可以降低应用程序的吞吐量。为了克服这个问题，在本文中，我们首先将应用程序分为两种类型; Type-i主导地要求处理核心和类型-II依赖于内存系统的性能。我们为GPGPU提出了一种基于类别的应用程序的应用程序意识到可扩展架构（APSA），该应用程序执行GPU资源的运行时间剪裁，以向正在运行的应用程序呈现最佳的资源集。在每个周期，带宽利用率和分支发散的指令方面进行分析和比较结果。我们发现，如果申请被识别为类型-i，则具有所提出的技术，平均分析开销是1.6％。 II型应用程序体验平均分析开销1.15％。在II型应用程序的情况下时钟门控冗余资源保存的平均功率为20.08％。

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