CAREER: Fast, Energy Efficient Irregular Kernels via Neural Accerlation

高性能计算遭受浪费计算时间、金钱和能量的性能瓶颈，因为多核系统上的处理核心处于空闲状态，等待来自不规则内核的存储器访问。这些不规则的内核通常完成很少的计算工作，尽管访问内存的成本很高。这些代价高昂的瓶颈必须通过一种新的高性能计算方法来弥补。但是，与此同时，计算也在不断发展，越来越不依赖于导致这些瓶颈的低级编程语言，而越来越依赖于神经网络等学习算法来获得必要的效率。该项目通过将不规则内核替换为在针对神经网络优化的加速器上运行的神经网络，为加速不规则内核奠定了基础。这些神经网络提供更好的性能和能耗。此外，这些网络是在高级编程语言（例如，Python），对于新手用户来说更容易学习。这使得更多的计算机科学家能够帮助科学和高性能计算社区。该项目还建立了新的课程，如增加神经加速器和扩展神经网络算法材料到传统的本科课程。该项目在研究和教育方面都显著减少了高性能计算的开发时间和成本，同时减少了性能瓶颈。此外，该项目将支持研究生和本科生进行跨学科参与，以匹配科学建模和大数据分析社区的准确性和性能限制，这些社区目前在气候建模、大规模电路设计和传染病药物分析等领域依赖不规则内核。该项目的目标和范围是建立一个框架，允许使用神经加速技术优化不规则内核的性能和能量使用，即，被表示和执行为神经网络。用于满足项目目标和范围的方法包括以下内容：1）开发一个近似约束特性，该特性量化和限定所开发的神经网络沿着性能和能量要求的可接受误差条;（二）为常用的不规则核开发初始神经网络，可用作更复杂的不规则核的起始网络，并可供个人使用调整它们的不规则内核（这将由研究者创建和维护的公共数据库提供，以支持该领域的研究）;以及3）构建工具链，以帮助识别代码中的不规则内核，基于用户输入构建神经网络，以及决定如何调度神经网络。可交付工具链支持TensorFlow等流行库，并将通过开源存储库传播。该项目的努力的变革性影响为不规则内核和基本工具集（即，一个公共数据库和调度工具链），这将促进未来将神经加速用于各种代码的进步，并导致科学和工程的重大进步。因此，这种新的优化选项可能会在后摩尔时代激发一种新的计算模型，为紧急的政府政策提供及时的科学数据，例如气候变化和外交事务。该项目由CAREER软件和硬件基金会HPC计划以及刺激竞争研究的既定计划（EPSCoR）联合资助该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

High-performance computing suffers from a performance bottleneck that wastes computation time, money, and energy, as processing cores on multicore systems sit idle waiting for memory accesses from irregular kernels. These irregular kernels normally accomplish little computational work despite the high cost of accessing memory. These costly bottlenecks must be remedied by a new approach to high-performance computing. But, at the same time, computing is evolving and is becoming less dependent on the low-level programming languages that cause these bottlenecks and more dependent on learning algorithms such as neural networks to attain the necessary efficiency. This project builds the foundation for accelerating irregular kernels by replacing them with neural networks that run on accelerators optimized for neural networks. These neural networks offer better performance and energy consumption. Additionally, these networks are tuned in high-level programming languages (e.g., Python) that are easier for novice users to learn. This allows more computer scientists to aid the scientific and high-performance computing communities. This project also builds a new curriculum such as adding neural accelerators and expanding neural network algorithm materials into traditional undergraduate courses. This project, in both its research and educational aspects, significantly reduces the development time and costs of high-performance computing while simultaneously reducing performance bottlenecks. Furthermore, this project will support graduate and undergraduate students as they engage in cross-disciplinary involvement to match accuracy and performance constraints from the scientific-modeling and big-data-analysis communities that currently depend on irregular kernels for areas such as climate modeling, large scale circuit design, and drug analysis on infectious diseases. The goals and scope of this project are to build a framework that allows irregular kernels to be optimized in terms of both their performance and energy usage using the technique of neural acceleration, i.e., being represented and executed as a neural network. The methods used to meet the project’s goals and scope include the following: 1) The development of an approximation-bound characteristic that quantifies and qualifies acceptable error bars on the developed neural networks along with performance and energy requirements; 2) The development of initial neural networks for commonly used irregular kernels that can be used as starting networks for more complex irregular kernels and be used by individuals tuning their irregular kernels (which will be made available by a public database that is created and maintained by the investigator to support research in this area); and 3) The construction of a toolchain to aid in identifying irregular kernels in code, constructing neural networks based on user input, and deciding how the neural networks should be scheduled. The deliverable toolchain has support for popular libraries like TensorFlow and will be disseminated via an open-source repository. The transformative impact of this project’s effort generates a completely new optimization option for irregular kernels and a base set of tools (i.e., a public database and scheduling toolchain) that will foster future advances into using neural acceleration for various codes and lead to significant advancements in science and engineering. As such, this new optimization option may inspire a new computational model in a post-Moore era that provides timely scientific data for urgent government policy, such as climate change and foreign affairs.This project is jointly funded by CAREER Software and Hardware Foundations HPC program and the Established Program to Stimulate Competitive Research (EPSCoR).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.