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

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 …

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 …

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

Accurate automatic optimization heuristics are necessary for dealing with thecomplexity and
diversity of modern hardware and software. Machine learning is aproven technique for …

The identification of performance-optimizing parameter settings is an important part of the
development and application of algorithms. We describe an automatic framework for this …

We explore the novel application of Large Language Models to code optimization. We
present a 7B-parameter transformer model trained from scratch to optimize LLVM assembly …

Traditional potato growth models evidence certain limitations, such as the cost of obtaining
the input data required to run the models, the lack of spatial information in some instances …

Machine learning (ML) models are widely used in many important domains. For efficiently
processing these computational-and memory-intensive applications, tensors of these …

Hyperspectral anomaly detection is aimed at detecting observations that differ from their
surroundings. To achieve this goal, low-rank models and autoencoders (AEs) have attracted …