资源受限虚拟机的GPU实现
GPU implementation of a resource-constrained virtual machine
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中文总结 AI 辅助
本文研究将资源受限虚拟机Uxn部署到GPU上，为Uxntal设计OpenMP风格并行API，经评估该方法可使Stencil基准性能提升19倍、Bunnymark帧率提升7倍，证明通用GPU可高效执行节俭型工作负载。
中文摘要 AI 辅助
计算硬件过时的主要原因之一是软件膨胀：软件产品每迭代一次，资源需求就会增加。资源受限虚拟机（VM）是应对软件膨胀的一种方式，因为它们对资源设置了严格限制，从而迫使程序员保持节俭。在本文中，我们探索将一种此类资源受限虚拟机Uxn部署到GPU上。我们表明，要获得有竞争力的性能，利用GPU数据并行性至关重要。我们为Uxn平台基于栈的汇编式语言Uxntal提供了一种OpenMP风格的并行API。我们证明，使用我们API的示例代码即使在集成GPU上也能达到相当的性能。具体而言，我们的评估结果显示，这种方法使计算密集型的Stencil基准测试性能提升了19倍，使图形密集型的Bunnymark基准测试帧率提升了7倍。实际上，所有笔记本电脑、台式机甚至移动设备都配备了GPU，我们的工作表明这些GPU可用于高效执行节俭型工作负载。
英文摘要
One of the main reasons compute hardware becomes obsolete is software bloat: resource requirements increase for every iteration of a software product. Resource constrained VMs are one way to combat software bloat as they post a hard limit on the resources and so force the programmer to be frugal. In this paper we explore the deployment of one such resource constrained VM, Uxn, on GPU. We show that for competitive performance it is essential to make use of the GPU data parallelism. We present an OpenMP-style parallelism API for Uxntal, the stack-based assembly-style language for the Uxn platform. We demonstrate that exemplar code using our API can run at comparable performance even on an integrated GPU. Specifically, our evaluation results show that using this approach improves performance on the compute-intensive Stencil benchmark with 19x and frame rate on the graphics-intensive Bunnymark benchmark with 7x. In practice, all laptops and desktops and even mobile devices have a GPU and our work shows that they can be used to execute frugal workloads effectively.
