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 a new algorithm to automatically schedule Halide programs for high-
performance image processing and deep learning. We significantly improve upon the …

Program autotuning has been shown to achieve better or more portable performance in a
number of domains. However, autotuners themselves are rarely portable between projects …

While modern parallel computing systems offer high performance, utilizing these powerful
computing resources to the highest possible extent demands advanced knowledge of …

High-performance computing has recently seen a surge of interest in heterogeneous
systems, with an emphasis on modern Graphics Processing Units (GPUs). These devices …

Enabling compilers to automatically optimize code has been a longstanding goal for the
compiler community. Efficiently solving this problem requires using precise cost models …

In this paper, we address the question of how to automatically map computational kernels to
highly efficient code for a wide range of computing platforms and establish the correctness of …

This work presents a systematic exploration on the promise and special challenges of deep
learning for sparse matrix format selection---a problem of determining the best storage …

Today's compilers have a plethora of optimizations to choose from, and the correct choice of
optimizations can have a significant impact on the performance of the code being optimized …

A daunting challenge faced by program performance autotuning is input sensitivity, where
the best autotuned configuration may vary with different input sets. This paper presents a …