Collaborative Research: PPoSS: Large: A Comprehensive Framework for Efficient, Scalable, and Performance-Portable Tensor Applications

合作研究：PPoSS：大型：高效、可扩展和性能可移植的张量应用的综合框架

• 批准号: 2217154
• 负责人: Ponnuswamy Sadayappan
• 金额: $ 364.96万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2217154&HistoricalAwards=false
• 关键词: Collaborative; Research; PPoSS; Large; Comprehensive

Computations on tensors are fundamental to many large-scale parallel software applications in scientific computing and machine learning, and their efficient implementation has been crucial for the significant advances they have enabled. However, with the end of Moore’s Law, two critical challenges now threaten continued progress: (1) with transistors becoming a bounded resource, hardware customization is critical to sustaining improved performance and energy efficiency, requiring advances in algorithm-architecture co-design methodology; (2) increasing customization and heterogeneity of hardware architectures aggravates the already daunting challenges of application-developer productivity and performance-portability of software. This project brings together researchers with expertise spanning the algorithm/software/hardware stack to address these challenges. The project’s impacts include (1) improved performance and energy efficiency of hardware architectures through algorithm-architecture co-design; (2) increased developer productivity for software applications and the performance achieved on a variety of target platforms, which enhances the benefits of computing technology in science and industry; (3) advances in scalable machine-learning and scientific computing applications.The project makes contributions along multiple directions: (1) compiler optimization: powerful unified methodology for automated optimization of dense tensor computations, based on non-linear cost models for multi-level hyper-rectangular tiled execution on a range of target computing platforms; (2) scalability with sparsity: multi-level blocking methodology to enhance scalability of sparse-tensor computations, based on analysis of the intrinsic sparsity patterns of the data and the corresponding data-reuse patterns; (3) algorithm-architecture co-design: by leveraging new cost models, development of powerful and general new approaches for hardware-software co-design of accelerators for dense- and sparse-tensor computations; (4) correctness and accuracy: development of techniques to ensure correctness and floating-point accuracy with compiler transformations and compiler/hardware design-space exploration; (5) applications: use of the developed methodology and tools to advance cutting-edge applications in machine learning and scientific computing, including PDE solvers, quantum many-body simulation, tensor networks in machine learning, and large-scale image analysis.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.

张量的计算对于科学计算和机器学习中的许多大规模并行软件应用至关重要，并且它们的有效实施对于它们启用的重大进展至关重要。但是，随着摩尔定律的终结，现在有两个关键挑战威胁到持续的进展：（1）随着晶体管成为有限的资源，硬件定制对于持续提高的性能和能源效率至关重要，需要在算法 - 建筑构造共同设计方法方面取得进步； （2）增加硬件体系结构的自定义和异质性加剧了应用程序开发者生产率和软件的性能通货性的挑战。该项目将跨越算法/软件/硬件堆栈的专业知识的研究人员汇集在一起​​，以应对这些挑战。该项目的影响包括（1）通过算法建筑共同设计提高了硬件体系结构的性能和能源效率； （2）提高了软件应用程序的开发人员生产率以及在各种目标平台上的性能，从而提高了计算技术在科学和行业中的好处； （3）可扩展的机器学习和科学计算应用程序的进步。该项目按多个方向做出了贡献：（1）编译器优化：基于多层超级高矩形瓷砖执行的非线性成本模型的强大统一方法，用于对密度张量计算的自动优化进行自动化，该方法是在目标计算平台上执行的； （2）具有稀疏性的可伸缩性：基于对数据的固有稀疏模式的分析以及相应的数据恢复模式，以增强稀疏量计算的可扩展性的多级阻止方法； （3）算法 - 建筑结构共同设计：通过利用新的成本模型，开发强大而一般的新方法，用于为加速器的硬件 - 软件共同设计，以进行密集和稀疏张量计算； （4）正确性和准确性：通过编译器转换以及编译器/硬件设计空间探索来确保正确性和浮点精度的技术开发； （5）应用：使用开发的方法和工具在机器学习和科学计算中提高了最先进的应用，包括PDE求解器，量子多体型模拟，机器学习中的张量网络以及大规模的图像分析。这项奖项反映了NSF的立法任务，并通过使用基础的智力评估诚实地评估了NSF的立法任务，并诚实地对基础的智力进行了评估。