Collaborative Research: Frameworks: Beyond the BLAS: A framework for accelerating computational and data science

传统的科学和机器学习高性能计算软件通常由一组基本运算组成，包括作为许多应用程序基础的线性代数功能。出于这个原因，几十年来，研究和开发开源线性代数软件库一直是科学基础设施的优先事项。颠覆这一领域的一个新趋势是，人们认识到，通过降低计算精度、利用非标准数据类型和开发定制计算内核，可以更快和/或更具成本效益地进行科学发现。该项目将利用对如何构建所需软件的洞察力，以使软件复杂性的组合爆炸保持可控。该项目的成果将是一个现代线性代数软件框架和以应用为重点的库，将支持学术界、国家实验室和工业中的未来几代计算应用程序。此外，该项目将影响下一代高性能计算专业人员的培训，并有助于消除传统上代表性不足群体成员进入该领域的障碍。拟议的工作将建立在NSF以前赞助的研究基础上，以解决在单一软件解决方案中同时实施扩展精度(EP)、混合精度(MP)和混合域(MD)算法。从最近的MP/MD矩阵乘法演示中获得的见解将通过添加低精度类型(如FLOAT16和BFLOAT16)和扩展精度类型(如DOUBLE-DOUBLE)来扩展。目标基本线性代数子程序(BLAS)功能将扩展到所有1级、2级和3级操作，这反过来将支持关于如何最好地利用MP/MD实现LAPACK功能的新研究。新的类似BLAS的库实例化软件(BLIS)框架也将更新，以提供集成扩展稠密线性代数(DLA)运算所需的灵活性。这一灵活的DLA框架将用于实现计算和数据科学中的关键功能：对量子化学(QC)至关重要的张量收缩和因式分解运算，以及用于机器学习的高性能原语。作为示范，这些能力将被用来构建最先进的QC代码，以执行具有完整EP/MP/MD功能的耦合集群极化传播子和张量分解耦合集群计算，并且机器学习内核将被集成到计算机视觉和图像识别工作流中。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Traditional scientific and machine learning high-performance computing software is often cast in terms of a set of fundamental operations, including the linear algebra functionality that underlies many applications. For this reason, research into and development of open-source linear algebra software libraries has been a science infrastructure priority for decades. An emerging trend that has disrupted this field is the recognition that scientific discovery can be made faster and/or more cost efficient by lowering the precision of computations, utilizing non-standard data types, and developing custom computational kernels. The project will leverage insights into how to structure the required software so that the combinatorial explosion in software complexity remains manageable. The outcome of the project will be a modern linear algebra software framework and application-focused libraries that will support future generations of computational applications in academia, at the national labs, and in industry. In addition, the project will impact the training of the next generation of high-performance computing professions and help remove barriers into the field for members of traditionally underrepresented groups.The proposed work will build on previous NSF-sponsored research in order to address the implementation of expanded precision (EP), mixed precision (MP), and mixed domain (MD) algorithms simultaneously in a single software solution. Insights gained from a recent demonstration of MP/MD matrix multiplication will be extended by adding low precision types like float16 and bfloat16 and extended precision types like double-double. The target Basic Linear Algebra Subprograms (BLAS) functionality will be expanded to all level-1, level-2, and level-3 operations which in turn will support new research on how best to exploit MP/MD for LAPACK functionality. The new BLAS-like Library Instantiation Software (BLIS) framework will also be updated to provide the flexibility required to integrate extended dense linear algebra (DLA) operations. This flexible DLA framework will then be used to implement key functionality in computational and data science: tensor contraction and factorization operations important to quantum chemistry (QC) and high-performance primitives for machine learning. As a demonstration, these capabilities will be used to build state-of-the-art QC codes to perform coupled cluster polarization propagator and tensor-factorized coupled cluster calculations with full EP/MP/MD functionality, and the machine learning kernels will be integrated into computer vision and image recognition workflows.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.