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

SHF：中：通过硬件软件协同设计拥抱架构异构性

• 批准号: 1763681
• 负责人: Chitaranjan Das
• 金额: $ 100万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1763681&HistoricalAwards=false
• 关键词: SHF; Medium; Embracing; Architectural; Heterogeneity

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.

过去十年见证了从高端数据中心到低成本嵌入式系统的不同应用领域的异构性激增，因为与同质多核架构相比，它们能够提供更好的性能和能效。这些系统通常包括 CPU、GPU、FPGA 和 ASIC 的子集作为计算引擎，因此带来了独特的编程/资源管理挑战。然而，缺乏所需的编译器和运行时支持，为异构系统的广泛采用带来了障碍。此外，在给定面积/功率预算下，为了满足各种应用需求，在各种计算引擎、内存子系统和互连的数量和布局方面，底层异构架构的设计尚未得到充分探索。因此，必须以一种紧密结合的方式研究整个系统堆栈，涵盖应用程序、系统软件和底层硬件，以便为高效的应用程序执行提供所需的支持。 因此，该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。该项目的成果将改变程序员和用户感知异质性并与其交互的方式。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人才，并有女研究生和本科生（荣誉）学生的积极参与。 该项目包括四项任务。任务 I 旨在对各种应用领域（包括深度学习、云计算和高性能计算）在不同硬件平台上进行基于配置文件的工作负载表征，以了解其性能/功耗效用。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。 Task-II 旨在探索编译器/编程支持，将应用程序代码转换为合适的与设备无关的“codelet”，作为跨不同硬件单元无缝调度和执行的粒度。任务 III 研究运行时支持，以优化调度和跨硬件单元无缝移动小代码，从而提高系统性能。最后，Task-IV 通过分析异构程度、芯片上和跨芯片的各种计算引擎的放置和集成、底层通信支持等问题来探索异构平台的设计。该奖项反映了 NSF 的法定使命，被认为是值得的。通过使用基金会的智力优势和更广泛的影响审查标准进行评估来提供支持。

项目成果

EdgePC: Efficient Deep Learning Analytics for Point Clouds on Edge Devices

EdgePC：边缘设备上点云的高效深度学习分析

• DOI: 10.1145/3579371.3589113
• 发表时间: 2023-06-17
• 期刊: Proceedings of the 50th Annual International Symposium on Computer Architecture
• 作者: Ziyu Ying;S;eepa Bhuyan;eepa;Yan Kang;Yingtian Zhang;M. K;emir;emir;C. Das
• 通讯作者: C. Das

Kube-Knots: Resource Harvesting through Dynamic Container Orchestration in GPU-based Datacenters

Kube-Knots：通过基于 GPU 的数据中心中的动态容器编排进行资源收获

• DOI: 10.1109/cluster.2019.8891040
• 发表时间: 2019-09-01
• 期刊: 2019 IEEE International Conference on Cluster Computing (CLUSTER)
• 作者: P. Thinakaran;Jashwant Raj Gunasekaran;Bikash Sharma;M. K;emir;emir;C. Das
• 通讯作者: C. Das

HoloAR: On-the-fly Optimization of 3D Holographic Processing for Augmented Reality

HoloAR：增强现实 3D 全息处理的动态优化

• DOI: 10.1145/3466752.3480056
• 发表时间: 2021-10-17
• 期刊: MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture
• 作者: Shulin Zhao;Haibo Zhang;Cyan Subhra Mishra;S;eepa Bhuyan;eepa;Ziyu Ying;M. K;emir;emir;A. Sivasubramaniam;C. Das
• 通讯作者: C. Das

Opportunistic computing in GPU architectures

GPU 架构中的机会计算

• DOI: 10.1145/3307650.3322212
• 发表时间: 2019-06
• 期刊: ISCA '19: Proceedings of the 46th International Symposium on Computer Architecture
• 作者: Pattnaik, Ashutosh;Tang, Xulong;Kayiran, Onur;Jog, Adwait;Mishra, Asit;Kandemir, Mahmut T.;Sivasubramaniam, Anand;Das, Chita R.
• 通讯作者: Das, Chita R.

Getting more performance with polymorphism from emerging memory technologies

通过新兴内存技术的多态性获得更高的性能

• DOI: 10.1145/3319647.3325826
• 发表时间: 2019-05
• 期刊: 12th ACM International Conference on Systems and Storage
• 作者: Narayanan, Iyswarya;Ganesan, Aishwarya;Badam, Anirudh;Govindan, Sriram;Sharma, Bikash;Sivasubramaniam, Anand
• 通讯作者: Sivasubramaniam, Anand