Maintaining leadership in HPC requires the ability to support simulations at large scales and
fidelity. In this study, we detail one of the most significant productivity challenges in …

We present a prototypical linear algebra compiler that automatically exploits domain-specific
knowledge to generate high-performance algorithms. The input to the compiler is a target …

In this paper, we tackle the problem of automatically generating algorithms for linear algebra
operations by taking advantage of problem-specific knowledge. In most situations, users …

Numerical applications and, more recently, machine learning applications rely on high-
dimensional data that is typically organized into multi-dimensional tensors. Many existing …

Since its introduction by Gauss, Matrix Algebra has facilitated understanding of scientific
problems, hiding distracting details and finding more elegant and efficient ways of …

A long-standing challenge in High-Performance Computing (HPC) is the simultaneous
achievement of programmer productivity and hardware computational efficiency. The …

ABSTRACT A design pattern is considered in which a distributed memory, multi processor
computer is viewed as an early generation processor. In such processors, each instruction …

Traditional compilation faces numerous challenges with program optimizations for parallel
architectures. A particular challenge is the design of proper intermediate languages and …

Technologies for verification and debugging have made significant strides in the context of
general systems software. An investment in such technologies to make them applicable for …

Tensor factorization is a frequently used modelling tool in problems involving large amounts
of n-way data. Probabilistic Latent Tensor Factorization framework provides a probabilistic …