SHF: Small: K-Way Speculation for Mapping Applications with Dependencies on Modern HPC Systems

SHF：小型：依赖现代 HPC 系统的地图应用程序的 K-Way 推测

• 批准号: 2334273
• 负责人: Gagan Agrawal
• 金额: $ 50万
• 依托单位: University of Georgia Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2334273&HistoricalAwards=false
• 关键词: SHF; Small; Way; Speculation; Mapping

Modern hardware provides parallelism in the form of a large number of threads. If an application involves independent work components, these components can be mapped to such hardware for parallel execution. However, when the work components are not independent, often such hardware is under-utilized. One technique used to overcome this problem is speculation – which involves guessing certain values or outcomes and assigning work to threads based on them. If the guess turns out to be incorrect, we need to re-execute the work. In current approaches one value or outcome is considered (or speculated): i.e., whether a specific value will be computed or loaded, or whether there will be dependence or not. With presence of massive number of threads, there is an opportunity to consider k-way speculation, e.g., speculating two or more likely values, or in parallel executing one thread that assumes dependence and another that assumes there is no dependence. This project is developing techniques enabling k-way speculation, using k guessed values to benefit from parallelism provided by modern hardware with multiple threads. The motivation arrives from the fact that modern hardware has a very high degree of parallelism, and it is better to have redundancy in using this parallelism, as compared to settling for a sequential execution. The class of applications this project targets span across scientific computing, data analytics, machine learning, and optimization, and thus this project impacts all of these areas. This project will also make contributions towards advancing curriculum and broadening participation in computing. This project builds on two demonstrated results relevant to k-way speculation. First, in certain cases, one might speculate k values, and while none of them may be correct, execution using them can be used to reconstruct the function. Second, for Finite State Machines (FSMs), it has been shown that k-way speculation is a better alternative to both (1-way) speculation and enumeration. Both approaches, k-way speculation and reconstruction and k-way speculation for FSMs, are not scalable to a very large number of cores because of the overhead of merging across different speculated executions. In addition, existing loop-transformation methods respect dependencies and cannot identify and exploit loops that can be parallelized despite dependencies. The following research directions are explored. First, techniques are being developed to remove this bottleneck. Second, the existing framework is being extended to consider nested loops, where one of the levels involves dependencies. Third, several classes of algorithms involve speculation and are candidates for benefiting from k-way speculation, possibly with reconstruction, and this is being explored. Finally, it is being examined if the function corresponding to the loop body does not have a closed-form combination, can the execution on speculated values nevertheless represent the function to a certain level of precision.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.

现代硬件以大量线程的形式提供并行性。如果应用程序涉及独立的工作组件，则可以将这些组件映射到该硬件以进行并行执行。但是，当工作组件不是独立的时，这种硬件通常会被不足。一种用于克服这个问题的技术是投机 - 涉及猜测某些值或结果并根据它们将工作分配给线程。如果猜测是不正确的，我们需要重新执行工作。在当前方法中，一个值或结果被考虑（或指定）：即，是否将计算或加载特定值，或是否有依赖性。在存在大量线程的情况下，有机会考虑K-Way投机，例如，猜测两个或更可能的值，或者并行执行一个假定依赖性的线程，而另一个假定没有依赖性的线程。 Project使用K猜测值从现代硬件提供的带有多个线程的现代硬件提供的并行性中受益于K-Way猜测的技术。动机来自现代硬件具有很高的并行性的事实，并且与设置顺序执行相比，使用此并行性具有冗余性。该项目目标的应用类别跨越科学计算，数据分析，机器学习和优化，因此该项目影响了所有这些领域。该项目还将为推进课程和扩大计算参与而做出贡献。该项目建立在两个与K道猜测有关的结果上。首先，在某些情况下，人们可能会推测k值，尽管它们都不正确，但使用它们的执行可以用于重建功能。其次，对于有限状态计算机（FSM），已经表明，K道猜测是（1向）猜测和枚举的更好替代方案。由于跨不同推测的执行，这两种方法，K道的投机和重建以及FSM的K-Way投机都无法扩展到大量内核。此外，现有的环路转换方法尊重依赖性，无法识别和利用可以并行化依赖性的循环。探索了以下研究方向。首先，正在开发去除这种瓶颈的技术。其次，正在扩展现有的框架以考虑嵌套循环，其中一个级别涉及依赖性。第三，几类算法涉及投机，是从K-way投机中受益的候选者，可以通过重建进行，并且正在探索这一点。最后，正在检查是否与循环主体相对应的功能没有封闭形式的组合，但是，对推测值的执行是否可以代表到一定级别的精度。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛影响的评估审查审查的审查标准来通过评估来通过评估来获得的支持。