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

Recent advances in state-of-the-art DNN architecture design have been moving toward
Transformer models. These models achieve superior accuracy across a wide range of …

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 …

Recent advances in Deep Neural Networks (DNNs) have led to active development of
specialized DNN accelerators, many of which feature a large number of processing …

In the last decade, machine-learning-based compilation has moved from an obscure
research niche to a mainstream activity. In this paper, we describe the relationship between …

Language models (LMs) built upon deep neural networks (DNNs) have recently
demonstrated breakthrough effectiveness in software engineering tasks like code …

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 …

A typical compiler such as GCC supports hundreds of optimizations controlled by
compilation flags for improving the runtime performance of the compiled program. Due to the …

Recent compilers offer a vast number of multilayered optimizations targeting different code
segments of an application. Choosing among these optimizations can significantly impact …

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 …