Collaborative Research: PPoSS: Large: A comprehensive framework for efficient, scalable, and performance-portable tensor applications

协作研究：PPoSS：大型：高效、可扩展和性能可移植的张量应用程序的综合框架

• 批准号: 2217081
• 负责人: Edward Valeev
• 金额: $ 45万
• 依托单位: Virginia Polytechnic Institute and State University
• 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=2217081&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.

张量计算是科学计算和机器学习中许多大规模并行软件应用的基础，它们的有效实现对于它们所取得的重大进步至关重要。然而，随着摩尔定律的终结，两个关键挑战仍然存在。进展：（1）随着晶体管成为有限资源，定制硬件对于维持性能和能源效率的提高至关重要，需要算法架构协同设计方法的进步；（2）硬件架构的定制化和异构性的增加加剧了本已严峻的挑战该项目汇集了具有算法/软件/硬件堆栈专业知识的研究人员，以解决这些挑战。该项目的影响包括（1）通过算法提高硬件架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力以及在各种目标平台上实现的性能，从而增强了计算技术在科学和工业中的优势；(3) 可扩展的机器学习和科学计算应用程序的进步。该项目做出了贡献沿着多个方向：（1）编译器优化：用于自动优化密集张量计算的强大统一方法，基于在一系列目标计算平台上进行多级超矩形平铺执行的非线性成本模型；（2）可扩展性；稀疏性：基于数据固有稀疏模式和相应数据重用模式的分析，采用多级分块方法来增强稀疏张量计算的可扩展性；（3）算法架构；协同设计：通过利用新的成本模型，开发用于密集和稀疏张量计算的加速器硬件软件协同设计的强大且通用的新方法；（4）正确性和准确性：开发确保正确性和浮动的技术- 编译器转换和编译器/硬件设计空间探索的点精度；（5）应用：使用开发的方法和工具推进机器学习和科学计算领域的前沿应用，包括偏微分方程求解器、量子多体模拟、该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。