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

This dissertation presents an automated system to generate highly efficient, platform-
adapted implementations of sparse matrix kernels. We show that conventional …

Compiler transformations can significantly improve data locality for many scientific programs.
In this paper, we show iterative solvers for partial differential equations (PDEs) in three …

On modern computer systems, the memory performance of an application depends on its
locality. For a single execution, locality-correlated measures like average miss rate or …

This paper presents a novel approach for integrating arrays with access time (1) into
functional languages. It introduces n-dimensional arrays combined with a type system that …

This paper presents the first-ever compile-time method for allocating a portion of the heap
data to scratch-pad memory. A scratch-pad is a fast directly addressed compiler-managed …

Recently, several experimental studies have been conducted on block data layout in
conjunction with tiling as a data transformation technique to improve cache performance. In …

Achieving peak performance from the computational kernels that dominate application
performance often requires extensive machine-dependent tuning by hand. Automatic tuning …

The performance of modern machines is increasingly limited by insufficient memory
bandwidth. One way to alleviate this bandwidth limitation for a given program is to minimize …

We present an approach for synthesizing transformations to enhance locality in imperfectly-
nested loops. The key idea is to embed the iteration space of every statement in a loop nest …