The performance of Graphic Processing Units (GPU) is sensitive to irregular memory
references. Some recent work shows the promise of data reorganization for eliminating non …

Fine-grained data parallelism is increasingly common in mainstream processors in the form
of longer vectors and on-chip GPUs. This paper develops support for exploiting such data …

With the advent of programmer-friendly GPU computing environments, there has been much
interest in offloading workloads that can exploit the high degree of parallelism available on …

Loop fusion is a key program transformation for data locality optimization that is
implemented in production compilers. But optimizing compilers for imperative languages …

Formally verifying properties of programs that manipulate arrays in loops is computationally
challenging. In this paper, we focus on a useful class of such programs, and present a novel …

The pursuit of computational efficiency has led to the proliferation of throughput-oriented
hardware, from GPUs to increasingly wide vector units on commodity processors and …

Generally applicable techniques for improving temporal locality in irregular programs, which
operate over pointer-based data structures such as trees and graphs, are scarce. Focusing …

Repeated tree traversals are ubiquitous in many domains such as scientific simulation, data
mining and graphics. Modern commodity processors support SIMD instructions, and using …

Production compilers commonly perform dozens of transformations on an intermediate
representation. Running those transformations in separate passes harms performance. One …

This paper describes the design and implementation of a layered domain-specific compiler
to support MADNESS—Multiresolution ADaptive Numerical Environment for Scientific …