Parallel discrete event simulation: A shared memory approach

With traditional event list techniques, evaluating a detailed discrete event simulation model can often require hours or even days of computation time. Parallel simulation mimics the interacting servers and queues of a real system by assigning each simulated entity to a processor. By eliminating the event list and maintaining only sufficient synchronization to insure causality, parallel simulation can potentially provide speedups that are linear in the number of processors. A set of shared memory experiments is presented using the Chandy-Misra distributed simulation algorithm to simulate networks of queues. Parameters include queueing network topology and routing probabilities, number of processors, and assignment of network nodes to processors. These experiments show that Chandy-Misra distributed simulation is a questionable alternative to sequential simulation of most queueing network models

A Study of Concurrency Bugs and Advanced Development Support for Actor-based Programs

The actor model is an attractive foundation for developing concurrent applications because actors are isolated concurrent entities that communicate through asynchronous messages and do not share state. Thereby, they avoid concurrency bugs such as data races, but are not immune to concurrency bugs in general. This study taxonomizes concurrency bugs in actor-based programs reported in literature. Furthermore, it analyzes the bugs to identify the patterns causing them as well as their observable behavior. Based on this taxonomy, we further analyze the literature and find that current approaches to static analysis and testing focus on communication deadlocks and message protocol violations. However, they do not provide solutions to identify livelocks and behavioral deadlocks. The insights obtained in this study can be used to improve debugging support for actor-based programs with new debugging techniques to identify the root cause of complex concurrency bugs.Comment: - Submitted for review - Removed section 6 "Research Roadmap for Debuggers", its content was summarized in the Future Work section - Added references for section 1, section 3, section 4.3 and section 5.1 - Updated citation

Sound Static Deadlock Analysis for C/Pthreads (Extended Version)

We present a static deadlock analysis approach for C/pthreads. The design of our method has been guided by the requirement to analyse real-world code. Our approach is sound (i.e., misses no deadlocks) for programs that have defined behaviour according to the C standard, and precise enough to prove deadlock-freedom for a large number of programs. The method consists of a pipeline of several analyses that build on a new context- and thread-sensitive abstract interpretation framework. We further present a lightweight dependency analysis to identify statements relevant to deadlock analysis and thus speed up the overall analysis. In our experimental evaluation, we succeeded to prove deadlock-freedom for 262 programs from the Debian GNU/Linux distribution with in total 2.6 MLOC in less than 11 hours

Dynamic Race Prediction in Linear Time

Writing reliable concurrent software remains a huge challenge for today's programmers. Programmers rarely reason about their code by explicitly considering different possible inter-leavings of its execution. We consider the problem of detecting data races from individual executions in a sound manner. The classical approach to solving this problem has been to use Lamport's happens-before (HB) relation. Until now HB remains the only approach that runs in linear time. Previous efforts in improving over HB such as causally-precedes (CP) and maximal causal models fall short due to the fact that they are not implementable efficiently and hence have to compromise on their race detecting ability by limiting their techniques to bounded sized fragments of the execution. We present a new relation weak-causally-precedes (WCP) that is provably better than CP in terms of being able to detect more races, while still remaining sound. Moreover it admits a linear time algorithm which works on the entire execution without having to fragment it.Comment: 22 pages, 8 figures, 1 algorithm, 1 tabl

UPC-CHECK: A scalable tool for detecting run-time errors in Unified Parallel C

Unied Parallel C (UPC) is a language used to write parallel programs for shared and distributed memory parallel computers. UPC-CHECK is a scalable tool developed to automatically detect argument errors in UPC functions and deadlocks in UPC programs at run-time and issue high quality error messages to help programmers quickly x those errors. The tool is easy to use and involves merely replacing the compiler command with upc-check. The tool uses a novel distributed algorithm for detecting argument and deadlock errors in collective operations. The run-time complexity of the algorithm has been proven to be O(1). The algorithm has been extended to detect deadlocks created involving locks with a run-time complexity of O(T), where T is the number of threads waiting to acquire a lock. Error messages issued by UPC-CHECK were evaluated using the UPC RTED test suite for argument errors in UPC functions and deadlocks. Results of these tests show that the error messages issued by UPC-CHECK for these tests are excellent. The scalability of all the algorithms used was demonstrated using performance-evaluation test programs and the UPC NAS Parallel Benchmarks

The combinatorics of resource sharing

We discuss general models of resource-sharing computations, with emphasis on the combinatorial structures and concepts that underlie the various deadlock models that have been proposed, the design of algorithms and deadlock-handling policies, and concurrency issues. These structures are mostly graph-theoretic in nature, or partially ordered sets for the establishment of priorities among processes and acquisition orders on resources. We also discuss graph-coloring concepts as they relate to resource sharing.Comment: R. Correa et alii (eds.), Models for Parallel and Distributed Computation, pp. 27-52. Kluwer Academic Publishers, Dordrecht, The Netherlands, 200