Reinventing the tuning and debugging tools for multi-thousand cores computer systems

重新发明数千核​​计算机系统的调优和调试工具

• 批准号: RGPIN-2017-05634
• 负责人: Dagenais, Michel
• 金额: $ 2.04万
• 依托单位: École Polytechnique de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=710162
• 关键词: Reinventing; tuning; debugging; tools; multi

The top supercomputers in recent years owe much of their power to the number-crunching ability of Graphical Processing Units (GPU) used for General Purpose computation (GPGPU). However, GPGPUs are also frequently used in smaller clusters and individual workstations for demanding tasks. Even in mobile phones and embedded systems, GPGPUs are an efficient medium to accomplish specialised tasks such as image processing. Furthermore, GPGPUs are at the forefront of a wider trend towards heterogeneous system architecture (HSA), where specialised co-procesors are used for a number of tasks beyond graphics and video, such as signal processing and network packet processing. The internal architecture of these complex GPGPU devices, with over 4000 cores and arithmetic units, was not well documented until recently. The architecture evolved significantly in recent years with a much tighter integration with main processing CPU cores, offering shared virtual memory and user-level queues. The problem is that the programming tools for debugging and tuning these systems are severely lacking. The objective of the proposed research is to devise new more efficient algorithms and software architectures for the debugging, tracing and profiling tools, such that they can adequately cope with hardware architectures boasting thousands of cores and putting a strong pressure on the available memory and bandwidth. The challenges are at several levels: insuring that the tracing and profiling tools impose a minimal overhead on the system studied to avoid affecting its behaviour, efficiently exploiting all the available tracing and profiling hardware assistance, and efficiently interfacing the debugging, tracing and profiling tools to the operating system, device driver and application to be monitored. The significance and novelty of this work is in addressing a serious challenge currently faced by all users of such systems: having suitable tools to efficiently debug and tune their applications on these systems. It is widely recognised that the extremely rapid advances in heterogeneous parallel processing hardware availability has not been followed by an equivalent progress in the software development tools. As a result, a large number of users are not achieving the level of performance that would be attainable with proper tools, or are simply not taking advantage of such hardware, because of the difficulty in efficiently programming, debugging and tuning these devices. The proposed research program will thus have a significant impact, helping all users of sophisticated computing devices in better exploiting the available hardware.

近年来的顶级超级计算机在很大程度上归功于用于通用计算（GPGPU）的图形处理单元（GPU）的数字运算能力。然而，GPGPU也经常用于较小的集群和单个工作站，以完成要求苛刻的任务。即使在移动的电话和嵌入式系统中，GPGPU也是完成图像处理等专门任务的有效媒介。此外，GPGPU处于异构系统架构（HSA）更广泛趋势的最前沿，其中专用协处理器用于图形和视频之外的许多任务，例如信号处理和网络数据包处理。 这些复杂的GPGPU设备的内部架构，拥有超过4000个核心和算术单元，直到最近才有很好的记录。该架构近年来有了显著的发展，与主处理CPU内核的集成更加紧密，提供了共享虚拟内存和用户级队列。问题是，用于调试和调优这些系统的编程工具严重缺乏。所提出的研究的目标是为调试，跟踪和分析工具设计新的更有效的算法和软件架构，使它们能够充分科普拥有数千个核心的硬件架构，并对可用内存和带宽施加强大的压力。挑战是在几个层面上：确保跟踪和分析工具施加最小的开销上的系统研究，以避免影响其行为，有效地利用所有可用的跟踪和分析硬件的援助，并有效地对接调试，跟踪和分析工具的操作系统，设备驱动程序和应用程序被监视。 这项工作的意义和新奇是在解决这些系统的所有用户目前面临的一个严重的挑战：有合适的工具来有效地调试和调整这些系统上的应用程序。人们普遍认识到，在异构并行处理硬件的可用性非常迅速的进步，并没有随之而来的软件开发工具的同等进展。结果，大量用户没有达到用适当的工具可以达到的性能水平，或者只是没有利用这样的硬件，因为难以有效地编程、调试和调整这些设备。因此，拟议的研究计划将产生重大影响，帮助所有复杂计算设备的用户更好地利用可用的硬件。