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

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 …

Automatic parallelization of sequential programs combined with auto-tuning is an alternative
to manual parallelization. With wider research directions and the increased number of …

Leveraging machine-learning (ML) techniques for compiler optimizations has been widely
studied and explored in academia. However, the adoption of ML in general-purpose …

Since manual black-box testing of GUI-based applications (GAPs) is tedious and laborious,
test engineers create test scripts to automate the testing process. These test scripts interact …

Modern compilers implement a large number of optimizations which all interact in complex
ways, and which all have a different impact on code quality, compilation time, code size …

Inlining is a core transformation in optimizing compilers. It replaces a function call (call site)
with the body of the called function (callee). It helps reduce function call overhead and …

Method inlining is considered to be one of the most important optimizations in a compiler.
However, a poor inlining heuristic can lead to significant degradation of a program's running …

Despite continuing research into inter-GPU communication mechanisms, extracting
performance from multi-GPU systems remains a significant challenge. Inter-GPU …

Tuning compiler heuristics and parameters is well known to improve optimization outcomes
dramatically. Prior works have tuned command line flags and a few expert identified …