CSR: Small: Predictable Real-Time Computing in GPU-enabled Systems

CSR：小型：支持 GPU 的系统中的可预测实时计算

• 批准号: 1527727
• 负责人: Cong Liu
• 金额: $ 35.41万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1527727&HistoricalAwards=false
• 关键词: CSR; Small; Predictable; Real; Time

Given the need to achieve higher performance without driving up energy consumption, most chip manufacturers have shifted to multicore architectures, especially heterogeneous ones. Among heterogeneous processing elements, graphic processing units (GPUs) have seen wide-spread use. GPUs have the power to enable orders of magnitude faster execution of many applications. Thus, they are becoming increasingly applicable for general-purpose systems. Unfortunately, it is not straightforward to reliably adopt GPUs in many safety-critical systems that require predictable real-time correctness, one of the most important tenets in certification required for such systems. A key example is the advanced automotive system where timeliness of computations is an essential requirement of correctness due to the interaction with the physical world. The goal of this project is to ensure predictable real-time correctness in current GPU-enabled systems, through (i) developing new real-time resource allocation methods that can be applied in GPU-enabled systems, where a number of difficult analysis issues due to the problem of co-scheduling CPU and GPU resources and several GPU-specific constraints will be addressed, and (ii) building an open-source ecosystem of predictably managing GPU resources in the operating system. These efforts will pave the way to utilizing GPUs in a predictable manner and benefit many applications and systems in which real-time constraints exist, such as automated automobiles and medical instrumentation. The successful completion of this work will enable powerful GPU computing capability. In safety-critical systems which usually require certification, this work will help enable such systems equipped with GPUs to be certifiable.

考虑到需要在不增加能耗的情况下实现更高的性能，大多数芯片制造商已转向多核架构，尤其是异构架构。在异构处理元件中，图形处理单元（GPU）得到了广泛的使用。 GPU 能够使许多应用程序的执行速度提高几个数量级。因此，它们越来越适用于通用系统。不幸的是，在许多需要可预测的实时正确性的安全关键系统中可靠地采用 GPU 并不容易，而这是此类系统所需认证的最重要原则之一。一个关键的例子是先进的汽车系统，其中由于与物理世界的交互，计算的及时性是正确性的基本要求。该项目的目标是通过（i）开发可应用于支持 GPU 的系统的新的实时资源分配方法，确保当前支持 GPU 的系统中可预测的实时正确性，其中许多困难的分析问题是由于共同调度 CPU 和 GPU 资源的问题以及一些 GPU 特定的约束将得到解决，并且 (ii) 构建一个可预测地管理操作系统中 GPU 资源的开源生态系统。这些努力将为以可预测的方式利用 GPU 铺平道路，并使许多存在实时限制的应用程序和系统受益，例如自动化汽车和医疗仪器。这项工作的顺利完成，将实现强大的GPU计算能力。在通常需要认证的安全关键系统中，这项工作将有助于使配备 GPU 的此类系统能够获得认证。