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

Most modern software systems (operating systems like Linux or Android, Web browsers like
Firefox or Chrome, video encoders like ffmpeg, x264 or VLC, mobile and cloud applications …

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

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 …

Autotuning refers to the automatic generation of a search space of possible implementations
of a computation that are evaluated through models and/or empirical measurement to …

As parallel applications become more complex, auto-tuning becomes more desirable,
challenging, and time-consuming. We propose, Bliss, a novel solution for auto-tuning …

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 …

Modern software systems often exhibit numerous configuration options to tailor them to user
requirements, including the system's performance behavior. Performance models derived …

Advanced video analytic systems, including scene classification and object detection, have
seen widespread success in various domains such as smart cities and autonomous …

When a computational task tolerates a relaxation of its specification or when an algorithm
tolerates the effects of noise in its execution, hardware, system software, and programming …