Auto-Tuning Compiler Transformations with Machine Learning – Dr. Biagio Cosenza

Slides (PDF): http://biagiocosenza.com/talk/LLVM-Berlin-Meetup-Nov2017.pdf

Description: https://www.meetup.com/LLVM-Social-Berlin/events/244936204/

"To deliver higher performance, today's computer architecture has evolved in complexity. Hardware design is taking an irreversible step toward parallel architectures, which burdens application programmers in porting and tuning their codes. It is desirable to write programs that execute efficiently on highly parallel computing systems, but peak performance is notoriously hard to reach, and the valuable cost of wasting these precious resources motivates application programmers to devote significant time to tuning their codes.

Program automatic tuning (autotuning) is an emerging approach that relies on automated search or machine learning to off-load the traditionally time-consuming manual tuning of applications, and while it can apply to very different scenarios, it has become particularly important for parallel architectures.

This talk will show how machine learning can be a powerful tool to design portable and efficient autotuners. Machine learning application to this context is challenging and requires to address very specific problems (encoding, modeling, and training data availability). I will show practical examples of such autotuners for vectorization, loop unrolling, heterogeneous task partitioning and stencil computations, and which have been applied on a variety of compiler infrastructures such as LLVM, GCC, Insieme, and Patus. In particular, I will show how modeling can be greatly enhanced by structural learning methods that adapt to the structure of the problem."