A Colored Petri Net-Based Approach for Automated Deadlock Detection in Parallel Programs

A static analysis approach is proposed for automated detection of deadlocks in a common class of parallel programs, referred to as Single Code Multiple Data (SCMD) programs. It is based on colored Petri net (CP-net) modeling and reachability analysis, where colors correspond to parallel processes. An SCMD program is first translated into a CP-net and a reachability tree is then derived and analyzed for deadlock information. CP-subnets representing basic programming language constructs are described. These subnets are employed as building blocks by an algorithm that translates synchronization-related statements of a process in an SCMD program and connects the resulting subnets. The connection technique makes use of the characteristics of SCMD programs to produce a unified and folded CP-net model. These characteristics are also used to introduce a notion, referred to as poset-covering, that leads to a reduced reachability tree for the Cp-net. The usual algorithm for generating and analyzing reachability trees of CP-nets is modified by including poset-covering and excluding notions that are irrelevant to our application. The compactness of the CP-net model and the reachability tree makes the proposed approach appealing for practical implementation

A Survey on Compiler Autotuning using Machine Learning

Since the mid-1990s, researchers have been trying to use machine-learning based approaches to solve a number of different compiler optimization problems. These techniques primarily enhance the quality of the obtained results and, more importantly, make it feasible to tackle two main compiler optimization problems: optimization selection (choosing which optimizations to apply) and phase-ordering (choosing the order of applying optimizations). The compiler optimization space continues to grow due to the advancement of applications, increasing number of compiler optimizations, and new target architectures. Generic optimization passes in compilers cannot fully leverage newly introduced optimizations and, therefore, cannot keep up with the pace of increasing options. This survey summarizes and classifies the recent advances in using machine learning for the compiler optimization field, particularly on the two major problems of (1) selecting the best optimizations and (2) the phase-ordering of optimizations. The survey highlights the approaches taken so far, the obtained results, the fine-grain classification among different approaches and finally, the influential papers of the field.Comment: version 5.0 (updated on September 2018)- Preprint Version For our Accepted Journal @ ACM CSUR 2018 (42 pages) - This survey will be updated quarterly here (Send me your new published papers to be added in the subsequent version) History: Received November 2016; Revised August 2017; Revised February 2018; Accepted March 2018

Trace Spaces: an Efficient New Technique for State-Space Reduction

State-space reduction techniques, used primarily in model-checkers, all rely on the idea that some actions are independent, hence could be taken in any (respective) order while put in parallel, without changing the semantics. It is thus not necessary to consider all execution paths in the interleaving semantics of a concurrent program, but rather some equivalence classes. The purpose of this paper is to describe a new algorithm to compute such equivalence classes, and a representative per class, which is based on ideas originating in algebraic topology. We introduce a geometric semantics of concurrent languages, where programs are interpreted as directed topological spaces, and study its properties in order to devise an algorithm for computing dihomotopy classes of execution paths. In particular, our algorithm is able to compute a control-flow graph for concurrent programs, possibly containing loops, which is "as reduced as possible" in the sense that it generates traces modulo equivalence. A preliminary implementation was achieved, showing promising results towards efficient methods to analyze concurrent programs, with very promising results compared to partial-order reduction techniques

Computer-aided programming for multiprocessing systems

As both the number of processors and the complexity of problems to be solved increase, programming multiprocessing systems becomes more difficult and error-prone. This report discusses parallel models of computation and tools for computer-aided programming (CAP). Program development tools are necessary since programmers are not able to develop complex parallel programs efficiently. In particular, a CAP tool, named Hypertool, is described here. It performs scheduling and handles the communication primitive insertion automatically so that many errors are eliminated. It also generates the performance estimates and other program quality measures to help programmers in improving their algorithms and programs. Experiments have shown that up to a 300% performance improvement can be achieved by computer-aided programming