Various research communities have independently arrived at stream processing as a
programming model for efficient and parallel computing. These communities include digital …

Matrix computations are both fundamental and ubiquitous in computational science and its
vast application areas. Along with the development of more advanced computer systems …

We propose a parameter server framework for distributed machine learning problems. Both
data and workloads are distributed over worker nodes, while the server nodes maintain …

Recent workload trends indicate rapid growth in the deployment of machine learning,
genomics and scientific workloads on cloud computing infrastructure. However, efficiently …

In the nearly seven years since I finished writing the first edition of this book research on the
accuracy and stability of numerical algorithms has continued to flourish and mature. Our …

The Method of Moments in Electromagnetics, Third Edition details the numerical solution of
electromagnetic integral equations via the Method of Moments (MoM). Previous editions …

The promise of unsupervised learning methods lies in their potential to use vast amounts of
unlabeled data to learn complex, highly nonlinear models with millions of free parameters …

Determining the best set of optimizations to apply to a kernel to be executed on the graphics
processing unit (GPU) is a challenging problem. There are large sets of possible …

This paper describes the LINPACK Benchmark and some of its variations commonly used to
assess the performance of computer systems. Aside from the LINPACK Benchmark suite, the …

We present the design and implementation of an automatic polyhedral source-to-source
transformation framework that can optimize regular programs (sequences of possibly …