SHF: Medium: Embracing Architectural Heterogeneity through Hardware-Software Co-design

在过去的十年中，从高端嵌入式系统到低成本嵌入式系统，各种应用领域的异构性激增，因为与同构多核架构相比，它们能够实现更好的性能和能效。这些系统通常包括CPU、GPU、FPGA和ASIC的子集作为计算引擎，因此存在独特的编程/资源管理挑战。然而，缺乏所需的编译器和运行时支持，提出了广泛采用异构系统的障碍。此外，在给定面积/功率预算的各种计算引擎、存储器子系统和互连的数量和放置方面的底层异构体系结构的设计以满足各种应用需求没有被充分探索。因此，有必要以一种内聚的方式研究整个系统堆栈，包括应用程序、系统软件和底层硬件，以便为有效的应用程序执行提供所需的支持。 因此，本研究项目的主要目标是实现应用程序到不同计算引擎的动态映射，以提高性能/功率效率和系统利用率。该项目的成果有望改变程序员和用户对异质性的认知和互动方式。异构计算的研究将与宾夕法尼亚州立大学的教育活动和学生培训相结合，以培养未来的科学和工程劳动力，女性研究生和本科生（荣誉）学生的积极参与。 该项目包括四项任务。Task-I旨在为各种应用领域（包括深度学习、云计算和不同硬件平台上的高性能计算）进行基于配置文件的工作负载表征，以了解其性能/功率效用。这将用于开发基于机器学习（ML）的模型，用于将任务初始分配给不同的计算引擎。 任务II旨在探索编译器/编程支持，以将应用程序代码转换为合适的设备无关的“代码集”，作为跨不同硬件单元的无缝调度和执行的粒度。任务III研究运行时支持，以优化调度并在硬件单元之间无缝移动codelet，从而提高系统性能。最后，Task-IV通过分析异构程度、芯片上和芯片间各种计算引擎的放置和集成、底层通信支持等问题，探索异构平台的设计。该奖项反映了NSF的法定使命，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。

The last decade has witnessed a proliferation of heterogeneity across diverse application domains spanning from high-end datacenters to low-cost embedded systems, because they are capable of better performance and energy efficiency compared to homogeneous multicore architectures. These systems typically include a subset of CPUs, GPUs, FPGAs and ASICs as compute engines and hence, present unique programming/resource management challenges. However, the lack of required compiler and runtime support, present a barrier to the widespread adoption of heterogeneous systems. Furthermore, the design of the underlying heterogeneous architecture in terms of number and placement of various compute engines, memory subsystems and interconnects for a given area/power budget to satiate various application demands, is not fully explored. Therefore, it is imperative to investigate the entire system stack in a cohesive manner spanning applications, system software and underlying hardware for providing the required support for efficient application executions. Thus, the main goal of this research project is to enable dynamic mapping of an application to different computing engines for improving performance/power efficiency and system utilization. The outcomes of this project are poised to change the way the programmers and users perceive heterogeneity and interact with it. The research on heterogeneous computing will be integrated with the educational activities and student training at Penn State for nurturing the future workforce in science and engineering, with active participation of female graduate students and undergraduates (Honors) students. The project consists four tasks. Task-I aims at conducting a profile-based workload characterization for various application domains including deep learning, cloud computing and high-performance computing on diverse hardware platforms to understand their performance/power utility. This will be used to develop a machine-learning (ML) based model for initial assignment of tasks to different compute engines. Task-II is aimed at exploring compiler/programming support to transform application code into suitable device-agnostic 'codelets', that serve as the granularity for seamless scheduling and execution across different hardware units. Task-III investigates runtime support to optimally schedule and seamlessly move the codelets across the hardware units for improving system performance. Finally, Task-IV explores design of heterogeneous platforms by analyzing issues such as degree of heterogeneity, placement and integration of various computing engines on a chip and across chips, the underlying communication support.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.