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是按照一组基本操作来构建的，包括许多应用程序所依赖的线性代数功能。 因此，开源线性代数软件库的研究和开发几十年来一直是科学基础设施的优先事项。 一个颠覆这个领域的新兴趋势是认识到科学发现可以通过降低计算精度，利用非标准数据类型和开发自定义计算内核来更快和/或更具成本效益。 该项目将利用对如何构建所需软件的见解，以便软件复杂性的组合爆炸仍然是可管理的。 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中心的库，将支持未来几代计算应用在学术界，国家实验室和工业界。 此外，该项目将影响下一代高性能计算专业人员的培训，并帮助消除传统上代表性不足的群体成员进入该领域的障碍。拟议的工作将建立在以前的NSF赞助的研究基础上，以解决在单个软件解决方案中同时实现扩展精度（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float 16和bfloat 16等低精度类型以及double double等扩展精度类型来扩展。 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BLAS类库实例化软件（BLIS）框架也将更新，以提供集成扩展密集线性代数（DLA）操作所需的灵活性。然后，这个灵活的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）重要的张量收缩和因子分解操作以及机器学习的高性能原语。 作为演示，这些功能将用于构建最先进的QC代码，以执行具有完整EP/MP/MD功能的耦合集群极化传播器和张量分解耦合集群计算，该奖项反映了NSF的法定使命，并通过使用基金会的知识产权进行评估，被认为值得支持。优点和更广泛的影响审查标准。

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.