ELEMENTS: CLAD ENABLING DIFFERENTIABLE PROGRAMMING IN SCIENCE

元素：CLAD 实现科学中的差异化编程

• 批准号: 2311471
• 负责人: David Lange
• 金额: $ 60万
• 依托单位: Princeton University
• 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: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2311471&HistoricalAwards=false
• 关键词: ELEMENTS; CLAD; ENABLING; DIFFERENTIABLE; PROGRAMMING; ELEMENTS; CLAD; ENABLING; DIFFERENTIABLE; PROGRAMMING

Scientists face a rapidly growing scale and complexity of data. Deep learning has proven to be a tremendously effective way of understanding large data sets, using backpropagation techniques that rely on automatic differentiation (AD) to provide efficient and scalable gradient-based optimization methods. Efforts to generalize deep learning have resulted in the emerging Differentiable Programming (DP) paradigm by incorporating a wide set of numerical computations and allowing the use of general numerical codes in optimization-based pipelines. Tasks such as parameter estimation, inverse problems, and apparatus design are naturally formulated as optimization problems amenable to gradient descent. The DP paradigm enables researchers to leverage domain knowledge while using powerful new techniques to enhance their science. This project will enable the use of differentiable programming techniques in large-scale science. C and C++ are the languages of choice for performance-sensitive scientific computing. However, C++ is currently a major challenge for AD implementations, which do not yet provide good feature coverage and adequate performance. Robustness, performance, native language support, and support for modern hardware architectures are critical for adoption in complex scientific pipelines.This project will extend the source transformation AD tool “Clad” to provide extensive C++ language support and interoperability capabilities. Deeply integrated into the LLVM compilation toolchain, Clad re-uses the Clang compiler frontend to differentiate C++ constructs, where it has access to the language details to support the necessary C++ features. By using the compiler to differentiate code, Clad provides advanced control over the differentiation process. Clad is already a very capable software for AD research. This project will: (a) extend support for C++ entities, including concurrency primitives to easily take advantage of GPUs while respecting high-level program structure; (b) facilitate AD interoperability and integration with large-scale scientific codes; and (c) ease differentiable programming adoption in science. This proposal will change Clad into sustainable cyberinfrastructure, and will engage scientists from numerous domains to use Clad as part of a DP pipeline to gain new insights from their data.This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Physics within the Directorate for Mathematical and Physical Sciences.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.

科学家面临着迅速增长的数据规模和复杂性。事实证明，深度学习是一种非常有效的理解大数据集的方法，使用依赖自动分化（AD）的反向传播技术来提供有效且可扩展的基于梯度的优化方法。概括深度学习的努力通过结合了广泛的数值计算并允许在基于优化的管道中使用一般数值代码，从而导致了新兴的可区分编程（DP）范式。诸如参数估计，逆问题和设备设计之类的任务自然被表达为优化问题，可与梯度下降。 DP范式使研究人员能够利用领域知识，同时使用强大的新技术来增强其科学。该项目将使在大规模科学中使用可区分的编程技术。 C ++是对性能敏感的科学计算的首选语言。但是，C ++目前是广告实施的主要挑战，尚未提供良好的功能覆盖范围和足够的性能。鲁棒性，性能，母语支持以及对现代硬件体系结构的支持对于在复杂的科学管道中采用至关重要。本项目将扩展源转换广告工具“ clad”，以提供广泛的C ++语言支持和互操作性功能。包裹深入集成到LLVM编译工具链中，重新使用Clang编译器前端以区分C ++构造，在此它可以访问语言详细信息以支持必要的C ++功能。通过使用编译器来区分代码，Clad提供了对差异化过程的高级控制。外壳已经是一个非常有能力的广告研究软件。该项目将：（a）扩展对C ++实体的支持，包括并发基础，以轻松利用GPU，同时尊重高级程序结构； （b）促进与大规模科学法规的AD互操作性和集成； （c）简化科学中的可区分编程。该提议将变成可持续的网络基础设施，并将吸引来自众多领域的科学家使用CLAD作为DP管道的一部分，以从其数据中获得新的见解。该奖项由高级网络基础设施办公室授予，该奖项由prysifation of Becordity of Becordition in Mathipation and Thembletion Issution Issive and Issive and Issive and Issution Issive and Nissfe nis Sf.智力优点和更广泛的影响审查标准。