SHF: Medium: Programmability, Portability, Performance and Energy Efficiency for Heterogeneous Systems

SHF：中：异构系统的可编程性、可移植性、性能和能源效率

• 批准号: 1302641
• 负责人: Vikram Adve
• 金额: $ 89.98万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1302641&HistoricalAwards=false
• 关键词: SHF; Medium; Programmability; Portability; Performance

To maximize energy efficiency, future mobile devices will include a diverse range of hardware, such as large and small general-purpose processor cores, vector units, graphics processing units (GPUs), digital signal processors (DSPs), and semi-custom and custom accelerator cores. This "heterogeneity" could power a new wave of innovation in mobile computing but is blocked by several fundamental challenges. Some of the biggest challenges are that such heterogeneous systems are highly challenging to program; that it is very difficult for software applications that use the diverse hardware to be portable across different mobile devices; that the memory systems in these devices are inflexible and inefficient; and that the semi-custom and custom accelerators are poorly integrated with the rest of the memory system and the programming environments.A key insight behind this project is that a carefully designed hardware abstraction layer --- a "Virtual Instruction Set" --- that abstracts away the differences in parallelism and memory subsystems across the different compute units can provide a framework in which all of the above interrelated problems can be solved extremely effectively. The project is developing a framework called Virtual Instruction Set Computing that uses this approach to address the above challenges. The framework uses just two or three models of parallelism and a uniform, rich model of communication to capture the full spectrum of heterogeneous hardware. The hardware memory architecture supports specialized memory sub-systems and novel memory optimizations customized for those sub-systems, while compilers partition the memory used by applications to make use of these partitions; together, these specialization techniques will provide an order of magnitude improvement in memory efficiency. Semi-custom accelerators for the key domain of Machine Learning are driving new programming and memory system design techniques to integrate and use semi-custom accelerators in such systems. The overall research builds on the widely used LLVM virtual instruction set and compiler infrastructure (previously developed by members of this research team), which are already widely used in industry, enhancing the potential for technology transfer from this work. If this project is successful, it can enable far more powerful mobile phones, tablets, and other such devices, and far more advanced software applications that can make full use of the rich capabilities of these devices.

为了最大限度地提高能效，未来的移动的设备将包括各种硬件，例如大型和小型通用处理器内核、矢量单元、图形处理单元（GPU）、数字信号处理器（DSP）以及半定制和定制加速器内核。 这种“异质性”可能会推动移动的计算领域的新一轮创新，但却受到几个基本挑战的阻碍。一些最大的挑战是，这种异构系统对编程是高度挑战性的;使用不同硬件的软件应用程序很难在不同的移动的设备上是可移植的;这些设备中的存储器系统是不灵活的和低效的;而半-自定义和自定义加速器与内存系统和编程环境的其余部分集成得很差。设计的硬件抽象层---“虚拟指令集”---抽象出不同计算单元之间的并行性和存储器子系统的差异，可以提供一个框架，在这个框架中，所有上述相互关联的问题都可以非常有效地解决。该项目正在开发一个名为虚拟指令集计算的框架，该框架使用这种方法来解决上述挑战。该框架仅使用两到三种并行模型和一个统一的、丰富的通信模型来捕获异构硬件的全部频谱。 硬件内存架构支持专门的内存子系统和为这些子系统定制的新颖内存优化，而编译器则对应用程序使用的内存进行分区以利用这些分区;这些专门化技术将一起提供内存效率的数量级改进。 机器学习关键领域的半定制加速器正在推动新的编程和内存系统设计技术，以在此类系统中集成和使用半定制加速器。 整个研究建立在广泛使用的LLVM虚拟指令集和编译器基础设施（以前由该研究团队成员开发）的基础上，这些基础设施已经在工业中广泛使用，从而增强了这项工作的技术转移潜力。如果这个项目成功，它可以使更强大的移动的手机，平板电脑和其他此类设备，以及更先进的软件应用程序，可以充分利用这些设备的丰富功能。