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iGPU: Exception Support and Speculative Execution on GPUs

机译:iGPU:GPU上的异常支持和推测执行

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Since the introduction of fully programmable vertex shader hardware, GPU computing has made tremendous advances. Exception support and speculative execution are the next steps to expand the scope and improve the usability of GPUs. However, traditional mechanisms to support exceptions and speculative execution are highly intrusive to GPU hardware design. This paper builds on two related insights to provide a unified lightweight mechanism for supporting exceptions and speculation on GPUs. First, we observe that GPU programs can be broken into code regions that contain little or no live register state at their entry point. We then also recognize that it is simple to generate these regions in such a way that they are idempo-tent, allowing their entry points to function as program recovery points and enabling support for exception handling, fast context switches, and speculation, all with very low overhead. We call the architecture of GPUs executing these idempotent regions the iGPU architecture. The hardware extensions required are minimal and the construction of idempotent code regions is fully transparent under the typical dynamic compilation framework of GPUs. We demonstrate how iGPU exception support enables virtual memory paging with very low overhead (1% to 4%), and how speculation support enables circuit-speculation techniques that can provide over 25% reduction in energy.
机译:自从引入完全可编程的顶点着色器硬件以来,GPU计算取得了长足的进步。异常支持和推测性执行是扩展范围和提高GPU可用性的下一步。但是,支持异常和推测执行的传统机制对GPU硬件设计具有很高的干扰性。本文基于两个相关的见解,以提供统一的轻量级机制来支持GPU上的异常和推测。首先,我们观察到GPU程序可以被划分为在入口点几乎不包含或不包含实时寄存器状态的代码区域。然后,我们还认识到,以这些区域为代表的方式来生成这些区域很简单,允许它们的入口点充当程序恢复点,并支持异常处理,快速上下文切换和推测,所有这些都非常低开销。我们将执行这些幂等区域的GPU架构称为iGPU架构。在典型的GPU动态编译框架下,所需的硬件扩展最少,并且幂等代码区域的构造是完全透明的。我们演示了iGPU异常支持如何以非常低的开销(1%到4%)启用虚拟内存分页,以及推测支持如何启用可以将能耗降低25%以上的电路推测技术。

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