Mario Méndez-Lojo

TL;DR: It is suggested that the operator formulation and tao-analysis of algorithms can be the foundation of a systematic approach to parallel programming.

TL;DR: This paper describes a high-performance GPU implementation of an important graph algorithm used in compilers such as gcc and LLVM: Andersen-style inclusion-based points-to analysis, which achieves an average speedup of 7x compared to a sequential CPU implementation and outperforms a parallel implementation of the same algorithm running on 16 CPU cores.

TL;DR: This work presents a framework for analysis of object-oriented languages in which in a first phase the authors transform the input program into a representation based on Horn clauses, which facilitates on one hand proving the correctness of the transformation attending to a simple condition and on the other allows applying existing analyzers for (constraint) logic programming to automatically derive a safe approximation of the semantics of the original program.

TL;DR: A complementary approach based on exploiting parallelism is described, which achieves a scaling of up to 3x on a 8-core machine for a suite of ten large C programs and outperforms a state-of-the-art, highly optimized, serial implementation of the same algorithm.

TL;DR: This paper shows that many irregular algorithms have structure that can be exploited and presents three key optimizations that take advantage of algorithmic structure to reduce speculative overheads and describes the implementation of these optimizations in the Galois system and presents experimental results to demonstrate their benefits.