We present the design and the implementation of a kernel principal component regression software that handles training datasets with a million or more observations. Kernel regressions are nonlinear and interpretable models that have wide downstream applications, and are shown to have a close connection to deep learning. Nevertheless, the exact regression of large-scale kernel models using currently available software has been notoriously difficult because it is both compute and memory intensive and it requires extensive tuning of hyperparameters.

Large bibliographic data sets hold the promise of revolutionizing the scientific enterprise when combined with state-of-the-science computational capabilities. Providing high-quality data services for large network datasets such as the Microsoft Academic Graph, which contains more than two billion citation links, poses significant difficulties for universities. Data systems based on the property graph model are capable of delivering efficient graph query services for large networks. However, real-life queries often combine multiple types of data models. To satisfy the needs of different user groups, we developed and deployed a cloud-based data system consisting of scalable graph and text-indexed query engines. For non-expert users, the property graph model also presents a technological barrier. To alleviate the steep learning curve, we designed an intuitive graphical user interface for query-building. For advanced users, a scalable notebook service in our platform provides a more flexible computing environments where the query results can be further analyzed. These systems form the data-backbone of the Collaborative Archive and Data Research Environment (CADRE), which provides efficient and high-quality bibliographic data services to eleven large public universities in North America.

This article describes the PRIMME software package for solving large, sparse Hermitian standard eigenvalue problems. The difficulty and importance of these problems have increased over the years, necessitating the use of preconditioning and near optimally converging iterative methods. However, the complexity of tuning or even using such methods has kept them outside the reach of many users. Responding to this problem, we have developed PRIMME, a comprehensive package that brings state-of-the-art methods from “bleeding edge” to production, with the best possible robustness, efficiency, and a flexible, yet highly usable interface that requires minimal or no tuning. We describe (1) the PRIMME multimethod framework that implements a variety of algorithms, including the near optimal methods GD+kand JDQMR; (2) a host of algorithmic innovations and implementation techniques that endow the software with its robustness and efficiency; (3) a multilayer interface that captures our experience and addresses the needs of both expert and end users.

Clusters of workstations have become a cost-effective means of performing scientific computations. However, large network latencies, resource sharing, and heterogeneity found in networks of clusters and Grids can impede the performance of applications not specifically tailored for use in such environments. A typical example is the traditional fine grain implementations of Krylov-like iterative methods, a central component in many scientific applications. To exploit the potential of these environments, advances in networking technology must be complemented by advances in parallel algorithmic design. In this paper, we present an algorithmic technique that increases the granularity of parallel block iterative methods by inducing additional work during the preconditioning (inexact solution) phase of the iteration. During this phase, each vector in the block is preconditioned by a different subgroup of processors, yielding a much coarser granularity. The rest of the method comprises a small portion of the total time and is still implemented in fine grain. We call this combination of fine and coarse grain parallelism multigrain. We apply this idea to the block Jacobi-Davidson eigensolver, and present experimental data that shows the significant reduction of latency effects on networks of clusters of roughly equal capacity and size. We conclude with a discussion on how multigrain can be applied dynamically based on runtime network performance monitoring.