EAGER: Efficient Utilization of FPGAs in HPC Centers and the Cloud: A Software/Hardware Approach

EAGER：在 HPC 中心和云中高效利用 FPGA：软件/硬件方法

• 批准号: 1821691
• 负责人: Lina Sawalha
• 金额: $ 17.54万
• 依托单位: Western Michigan University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-05-01 至 2022-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1821691&HistoricalAwards=false
• 关键词: EAGER; Efficient; Utilization; FPGAs; HPC

Cloud and high-performance computing (HPC) are essential for advancing science and education. Field programmable gate arrays (FPGAs) are being integrated into data centers and cloud computing due to their power and performance benefits. This proposal focuses on filling the gap between the current use of FPGAs and the use of such computing infrastructures for mainstream computing. Exact solution for hardware/software partitioning is known to be a computationally difficult problem which is often solved based on static analysis without taking program runtime behavior into account. This work aims to take full advantage of FPGAs in cloud computing via automated partitioning to speed up HPC while minimizing energy consumption.The project integrates techniques from computer architecture, compilers, machine learning and computer systems to allow for an efficient automated task partitioning and tuning. The cross-layer techniques will exploit CPU-FPGA systems and will be based on hardware-software cooperation with new innovative architectures. This work could result in solutions for adaptable, scalable, energy-efficient and high-performance use of heterogeneous architectures for cluster and cloud computing. In addition, the research ideas developed in this project are expected to significantly speed the pace of discovery in many computing domains from cloud computing and cyber physical systems to mobile computing and embedded systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

云计算和高性能计算（HPC）对于推进科学和教育至关重要。 现场可编程门阵列（FPGA）因其功率和性能优势而被集成到数据中心和云计算中。 本提案的重点是填补FPGA当前使用与主流计算使用此类计算基础设施之间的差距。 硬件/软件划分的精确解是一个计算困难的问题，通常基于静态分析而不考虑程序运行时行为来解决。 该项目旨在通过自动分区充分利用FPGA在云计算中的优势，在最大限度地降低能耗的同时加快HPC的速度。该项目集成了计算机架构，编译器，机器学习和计算机系统的技术，以实现高效的自动任务分区和调优。跨层技术将利用CPU-FPGA系统，并将基于新的创新架构的硬件-软件合作。 这项工作可能会导致解决方案的适应性，可扩展性，能源效率和高性能使用异构架构的集群和云计算。 此外，该项目中开发的研究理念有望大大加快从云计算和网络物理系统到移动的计算和嵌入式系统等众多计算领域的发现步伐。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Accurate Performance and Power Prediction for FPGAs Using Machine Learning

使用机器学习准确预测 FPGA 的性能和功耗

• 发表时间: 2022
• 期刊: The 30th IEEE International Symposium On Field-Programmable Custom Computing Machines
• 作者: Sawalha, Lina

An OpenCL-Based Acceleration for Canny Algorithm Using a Heterogeneous CPU-FPGA Platform

• DOI: 10.1109/fccm.2019.00063
• 发表时间: 2019-04
• 期刊: 2019 IEEE 27th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM)
• 作者: Samah Rahamneh;L. Sawalha

Efficient OpenCL Accelerators for Canny Edge Detection Algorithm on a CPU-FPGA Platform

• DOI: 10.1109/reconfig48160.2019.8994769
• 发表时间: 2019-12
• 期刊: 2019 International Conference on ReConFigurable Computing and FPGAs (ReConFig)
• 作者: Samah Rahamneh;L. Sawalha

A Comparison of Different Optimization Algorithms for HW/SW Partitioning Using a High-Performance Cluster

使用高性能集群进行软硬件分区的不同优化算法的比较

• DOI: 10.1109/aiccsa53542.2021.9686929
• 发表时间: 2021
• 期刊: IEEE/ACS 18th International Conference on Computer Systems and Applications (AICCSA
• 作者: Rahamneh, Samah;Fong, Alvis;Sawalha, Lina
• 通讯作者: Sawalha, Lina