CSR: Small: Real-Time Computing Using GPUs

随着多核芯片的出现，计算行业最近经历了CPU体系结构的重大转变。这种转变需要采用新的编程模型、算法和分析方法，以充分利用多核芯片设计中固有的并行性。虽然这些领域的进展正在顺利进行，但工业已经开始了另一次向具有不同处理元素的系统的体系结构转变。异构性带来了新的挑战，因为不同类型的处理元素的可用性意味着在将硬件资源分配给软件组件时必须做出重要的选择。当今异构性最成功的应用之一是在将强大的图形处理单元(GPU)与通用CPU一起使用的架构中。尽管GPU最初打算用作特殊用途的图形加速器，但现在它被广泛用于许多应用领域中的非图形处理，包括其中存在实时限制(例如，截止日期要求)的许多领域。例如，设想的自动化汽车系统将需要实时传感和跟踪功能，而GPU可以加速这些功能。该项目的目标是确定哪些资源分配方法最有利于在可能具有多个CPU和GPU的异类平台上支持此类实时应用程序。通过对影响在支持GPU的多核平台上实施的实时应用程序的部署和分析的问题进行广泛研究，正在实现这一目标。更广泛的影响包括与业界同行进行联合研究，以及开发可供其他机构用于研究和教学目的的公开开放源码软件。

The computing industry recently experienced a major shift in CPU architectures with the advent of multicore chips. This shift has necessitated the adoption of new programming models, algorithms, and analysis methods to fully exploit the parallelism inherent in multicore chip designs. While advances in these areas are well underway, industry has already begun yet another architectural shift towards systems with heterogeneous processing elements. Heterogeneity creates new challenges because the availability of different types of processing elements means that nontrivial choices must be made when allocating hardware resources to software components. One of the most successful applications of heterogeneity today is in architectures in which powerful graphics processing units (GPUs) are used alongside general-purpose CPUs. Though originally intended as special-purpose graphics accelerators, GPUs are now being widely used for non-graphics processing in numerous application domains, including many domains in which real-time constraints (e.g., deadline requirements) exist. For example, envisioned automated automotive systems will require real-time sensing and tracking features that GPUs can accelerate. The goal of this project is to determine which resource allocation methods best facilitate the support of such real-time applications on heterogeneous platforms that may have multiple CPUs and GPUs. This goal is being met by undertaking a broad study of issues affecting the deployment and analysis of real-time applications implemented on GPU-enabled multicore platforms. Broader impacts include joint research with industry colleagues, and the development of publicly-available open-source software that can be used by other institutions for research and teaching purposes.