Since the mid-1990s, researchers have been trying to use machine-learning-based
approaches to solve a number of different compiler optimization problems. These …

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

The polyhedral model provides powerful abstractions to optimize loop nests with regular
accesses. Affine transformations in this model capture a complex sequence of execution …

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

Abstract Graphics Processing Units (GPUs) offer tremendous computational power. CUDA
(Compute Unified Device Architecture) provides a multi-threaded parallel programming …

Many applications, such as medical imaging, generate intensive data traffic between the
FPGA and off-chip memory. Significant improvements in the execution time can be achieved …

This paper describes a general and robust loop transformation framework that enables
compilers to generate efficient code on complex loop nests. Despite two decades of prior …

GPUs are a class of specialized parallel architectures with tremendous computational
power. The new Compute Unified Device Architecture (CUDA) programming model from …

High-level loop optimizations are necessary to achieve good performance over a wide
variety of processors. Their performance impact can be significant because they involve in …