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首页> 外文期刊>ACM Transactions on Architecture and Code Optimization >Buddy SM: Sharing Pipeline Front-End for Improved Energy Efficiency in GPGPUs
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Buddy SM: Sharing Pipeline Front-End for Improved Energy Efficiency in GPGPUs

机译：Buddy SM：共享管道前端以提高GPGPU的能效

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摘要

A modern general-purpose graphics processing unit (GPGPU) usually consists of multiple streaming multiprocessors (SMs), each having a pipeline that incorporates a group of threads executing a common instruction flow. Although SMs are designed to work independently, we observe that they tend to exhibit very similar behavior for many workloads. If multiple SMs can be grouped and work in the lock-step manner, it is possible to save energy by sharing the front-end units among multiple SMs, including the instruction fetch, decode, and schedule components. However, such sharing brings architectural challenges and sometime causes performance degradation. In this article, we show our design, implementation, and evaluation for such an architecture, which we call Buddy SM. Specifically, multiple SMs can be opportunistically grouped into a buddy cluster. One SM becomes the master, and the rest become the slaves. The front-end unit of the master works actively for itself as well as for the slaves, whereas the front-end logics of the slaves are power gated. For efficient flow control and program correctness, the proposed architecture can identify unfavorable conditions and ungroup the buddy cluster when necessary. We analyze various techniques to improve the performance and energy efficiency of Buddy SM. Detailed experiments manifest that 37.2% front-end and 7.5% total GPU energy reduction can be achieved.
机译：现代通用图形处理单元（GPGPU）通常由多个流式多处理器（SM）组成，每个流式多处理器（SM）都具有一个流水线，该流水线并入了一组执行共同指令流的线程。尽管SM被设计为独立工作，但是我们发现它们在许多工作负载下往往表现出非常相似的行为。如果可以将多个SM分组并以锁步方式工作，则可以通过在多个SM之间共享前端单元（包括指令提取，解码和调度组件）来节省能量。但是，这样的共享带来了架构挑战，并且有时会导致性能下降。在本文中，我们展示了我们对这种架构（称为Buddy SM）的设计，实现和评估。具体而言，可以将多个SM机会性地分组为伙伴群集。一个SM成为主机，其余SM成为从机。主机的前端单元既可以为自己也可以为从机工作，而从机的前端逻辑是电源门控。为了实现有效的流控制和程序正确性，所提出的体系结构可以识别不利条件，并在必要时取消对伙伴群集的分组。我们分析了各种技术来提高Buddy SM的性能和能效。详细的实验表明，可以实现37.2％的前端和7.5％的GPU总能耗降低。

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