EAGER: Developing a Programming Environment for Heterogenous Multiprocessors

智力优势：这个EAGER项目解决了在异构计算体系结构上执行的系统的分布问题，也就是说，已经配置了图形处理单元（gpu）和现场可编程门阵列（fpga）的体系结构。与简单的多处理器体系结构相比，异构体系结构可以显著提高吞吐量。但是，为了在异构体系结构中实现最大的收益和资源利用率，应用程序？s的工作负载需要根据这些组件的计算能力在加速器和cpu之间分配。任务和数据在处理单元之间的最佳分配是这类平台关注的一个重要问题。动态分区可以更好地利用执行过程中的所有计算单元。然而，尽管在一些已发表的研究中解决了这种类型的分区问题，但尚未报告充分利用的收益。这个团队会尝试发展吗？附近的理想?分区方案需要：？为每一种加速器设计监测机制。将任务和数据量化为计算块，以及？在整个执行过程中评估性能。这些负载平衡方案将独立于应用程序和设备，并且完全自动化，而不需要在实际运行之前进行预先培训。目标是将这些方案与编程模型集成在一起，以无缝地暴露异构多处理器体系结构的近乎最佳利用率。更广泛的影响：在这项研究中开发的软件和硬件平台将作为开源公开发布，以便其他人可以试用它们。这项研究将与英特尔研究人员合作进行，以便将来将技术转移到工业中。本项目的资助将用于招募未被充分代表的少数族裔和女学生。此外，研究内容将整合到研究生课程中，为学生提供设计和编程未来异构系统的训练。

Intellectual Merit: This EAGER project addresses distribution issues for systems executing on heterogeneous computing architectures, that is, architectures that have been configured with graphics processing units (GPUs) and field-programmable gate array (FPGAs).Heterogeneous architectures may offer considerable increase in throughput when compared to simple multi-processor architectures. But, to achieve maximum gain and resource utilization in heterogeneous architectures, an application?s workload needs to be distributed among accelerators and CPUs according to the computational capabilities of those components. The optimal distribution of tasks and data among processing units is an important concern for such platforms. Dynamic partitioning can better utilize all computational units during execution. However, even though this type of partitioning is addressed in a few published studies, full utilization gains have not been reported. This team will attempt to develop ?near ideal? partitioning schemes which require: ? devising monitoring mechanisms for each type of accelerator, ? quantifying tasks and data as computational blocks, and ? estimating performance throughout execution. These load balancing schemes will be application and device independent and fully automated without the need for prior training runs before the actual run. The goal is to integrate these schemes with programming models to seamlessly expose near optimal utilization of heterogeneous multiprocessor architectures.Broader Impact: The software and hardware platforms developed in this research will be publicly released as open source so that others may experiment with them. The research will be conducted with cooperation of Intel researchers enabling possible future transfer of technology to industry. Funding of this project will provide support for recruiting underrepresented minority and female students for the project. In addition, the research content will be integrated to graduate courses to provide training to students for designing and programming future heterogeneous systems.