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

Performance is arguably the most crucial attribute that reflects the quality of a configurable
software system. However, given the increasing scale and complexity of modern software …

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

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

On-device deep learning (DL) inference has attracted vast interest. Mobile CPUs are the
most common hardware for on-device inference and many inference frameworks have been …

Predictive modeling using machine learning is an effective method for building compiler
heuristics, but there is a shortage of benchmarks. Typical machine learning experiments …

This work introduces CLBlast, an open-source BLAS library providing optimized OpenCL
routines to accelerate dense linear algebra for a wide variety of devices. It is targeted at …

A very common problem in GPU programming is that some combination of thread block
dimensions and other code optimization parameters, like tiling or unrolling factors, results in …

In recent years, the heterogeneity of both commodity and supercomputers hardware has
increased sharply. Accelerators, such as GPUs or Intel Xeon Phi co-processors, are often …

Mobile GPU, as a ubiquitous and powerful accelerator, plays an important role in
accelerating on-device DNN (Deep Neural Network) inference. The frequent-upgrade and …