Taming Irregular Control-Flow with Targeted Compiler Transformations

Charitha Saumya Gusthinna Waduge, Purdue University

Abstract

Irregular control-flow structures like deeply nested conditional branches are common in real-world software applications. Improving the performance and efficiency of such programs is often challenging because it is difficult to analyze and optimize programs with irregular control flow. We observe that real-world programs contain similar or identical computations within different code paths of the conditional branches. Compilers can merge similar code to improve performance or code size. However, existing compiler optimizations like code hoisting/sinking, and tail merging do not fully exploit this opportunity. We propose a new technique called Control-Flow Melding (CFM) that can merge similar code sequences at the control-flow region level. We evaluate CFM in two applications. First, we show that CFM reduces the control divergence in GPU programs and improves the performance. Second, we apply CFM to CPU programs and show its effectiveness in reducing code size without sacrificing performance. In the next part of this dissertation, we investigate how CFM can be extended to improve dynamic test generation techniques like Dynamic Symbolic Execution (DSE). DSE suffers from path explosion problem when many conditional branches are present in the program. We propose a non-semantics-preserving branch elimination transformation called CFM-SE that reduces the number of symbolic branches in a program. We also provide a framework for detecting and reasoning about false positive bugs that might be added to the program by non-semantics-preserving transformations like CFM-SE. Furthermore, we evaluate CFM-SE on real-world applications and show its effectiveness in improving DSE performance and code coverage.

Degree

Ph.D.

Advisors

Qiu, Purdue University.

Subject Area

Logic

COinS