Containing over 300 entries in an A-Z format, the Encyclopedia of Parallel Computing provides easy, intuitive access to relevant information for professionals and researchers seeking access to any aspect within the broad field of parallel computing. Topics for this comprehensive reference were selected, written, and peer-reviewed by an international pool of distinguished researchers in the field. The Encyclopedia is broad in scope, covering machine organization, programming languages, algorithms, and applications. Within each area, concepts, designs, and specific implementations are presented. The highly-structured essays in this work comprise synonyms, a definition and discussion of the topic, bibliographies, and links to related literature. Extensive cross-references to other entries within the Encyclopedia support efficient, user-friendly searchers for immediate access to useful information. Key concepts presented in the Encyclopedia of Parallel Computing include; laws and metrics; specific numerical and non-numerical algorithms; asynchronous algorithms; libraries of subroutines; benchmark suites; applications; sequential consistency and cache coherency; machine classes such as clusters, shared-memory multiprocessors, special-purpose machines and dataflow machines; specific machines such as Cray supercomputers, IBM’s cell processor and Intel’s multicore machines; race detection and auto parallelization; parallel programming languages, synchronization primitives, collective operations, message passing libraries, checkpointing, and operating systems. Topics covered: Speedup, Efficiency, Isoefficiency, Redundancy, Amdahls law, Computer Architecture Concepts, Parallel Machine Designs, Benmarks, Parallel Programming concepts & design, Algorithms, Parallel applications. This authoritative reference will be published in two formats: print and online. The online edition features hyperlinks to cross-references and to additional significant research. Related Subjects: supercomputing, high-performance computing, distributed computing

LCPC’98 Steering and Program Committes for their time and energy in - viewing the submitted papers. Finally, and most importantly, we thank all the authors and participants of the workshop. It is their signi cant research work and their enthusiastic discussions throughout the workshopthat made LCPC’98 a success. May 1999 Siddhartha Chatterjee Program Chair Preface The year 1998 marked the eleventh anniversary of the annual Workshop on Languages and Compilers for Parallel Computing (LCPC), an international - rum for leading research groups to present their current research activities and latest results. The LCPC community is interested in a broad range of te- nologies, with a common goal of developing software systems that enable real applications. Amongthetopicsofinteresttotheworkshoparelanguagefeatures, communication code generation and optimization, communication libraries, d- tributed shared memory libraries, distributed object systems, resource m- agement systems, integration of compiler and runtime systems, irregular and dynamic applications, performance evaluation, and debuggers. LCPC’98 was hosted by the University of North Carolina at Chapel Hill (UNC-CH) on 7 - 9 August 1998, at the William and Ida Friday Center on the UNC-CH campus. Fifty people from the United States, Europe, and Asia attended the workshop. The program committee of LCPC’98, with the help of external reviewers, evaluated the submitted papers. Twenty-four papers were selected for formal presentation at the workshop. Each session was followed by an open panel d- cussion centered on the main topic of the particular session.

This book constitutes the thoroughly refereed post-proceedings of the 19th International Workshop on Languages and Compilers for Parallel Computing, LCPC 2006, held in New Orleans, LA, USA in November 2006. The 24 revised full papers presented together with two keynote talks cover programming models, code generation, parallelism, compilation techniques, data structures, register allocation, and memory management.

This volume contains the papers presented at the 13th International Workshop on Languages and Compilers for Parallel Computing. It also contains extended abstracts of submissions that were accepted as posters. The workshop was held at the IBM T. J. Watson Research Center in Yorktown Heights, New York. As in previous years, the workshop focused on issues in optimizing compilers, languages, and software environments for high performance computing. This continues a trend in which languages, compilers, and software environments for high performance computing, and not strictly parallel computing, has been the organizing topic. As in past years, participants came from Asia, North America, and Europe. This workshop re?ected the work of many people. In particular, the members of the steering committee, David Padua, Alex Nicolau, Utpal Banerjee, and David Gelernter, have been instrumental in maintaining the focus and quality of the workshop since it was ?rst held in 1988 in Urbana-Champaign. The assistance of the other members of the program committee – Larry Carter, Sid Chatterjee, Jeanne Ferrante, Jans Prins, Bill Pugh, and Chau-wen Tseng – was crucial. The infrastructure at the IBM T. J. Watson Research Center provided trouble-free logistical support. The IBM T. J. Watson Research Center also provided ?nancial support by underwriting much of the expense of the workshop. Appreciation must also be extended to Marc Snir and Pratap Pattnaik of the IBM T. J. Watson Research Center for their support.

This book constitutes the thoroughly refereed post-conference proceedings of the 21th International Workshop on Languages and Compilers for Parallel Computing, LCPC 2008, held in Edmonton, Canada, in July/August 2008. The 18 revised full papers and 6 revised short papers presented were carefully reviewed and selected from 35 submissions. The papers address all aspects of languages, compiler techniques, run-time environments, and compiler-related performance evaluation for parallel and high-performance computing and comprise also presentations on program analysis that are precursors of high performance in parallel environments.

This book constitutes the refereed proceedings of the 5th International Conference on Runtime Verification, RV 2014, held in Toronto, ON, Canada in September 2014. The 28 revised full papers presented together with 2 tool papers, and 8short papers were carefully reviewed and selected from 70 submissions. The scope of the conference was on following topics: monitoring and trace slicing, runtime verification of distributed and concurrent systems, runtime Verification of real-time and embedded systems, testing and bug finding, and inference and learning.

This book constitutes the thoroughly refereed post-proceedings of the 14th International Workshop on Languages and Compilers for Parallel Computing, LCPC 2001, held in Lexington, KY, USA, in August 1-3, 2001. The 28 revised full papers presented were carefully selected during two rounds of reviewing and improvement. All current issues in parallel processing are addressed, in particular compiler optimization, HP Java programming, power-aware parallel architectures, high performance applications, power management of mobile computers, data distribution, shared memory systems, load balancing, garbage collection, parallel components, job scheduling, dynamic parallelization, cache optimization, specification, and dataflow analysis.

Distributed-memory multiprocessing systems (DMS), such as Intel's hypercubes, the Paragon, Thinking Machine's CM-5, and the Meiko Computing Surface, have rapidly gained user acceptance and promise to deliver the computing power required to solve the grand challenge problems of Science and Engineering. These machines are relatively inexpensive to build, and are potentially scalable to large numbers of processors. However, they are difficult to program: the non-uniformity of the memory which makes local accesses much faster than the transfer of non-local data via message-passing operations implies that the locality of algorithms must be exploited in order to achieve acceptable performance. The management of data, with the twin goals of both spreading the computational workload and minimizing the delays caused when a processor has to wait for non-local data, becomes of paramount importance. When a code is parallelized by hand, the programmer must distribute the program's work and data to the processors which will execute it. One of the common approaches to do so makes use of the regularity of most numerical computations. This is the so-called Single Program Multiple Data (SPMD) or data parallel model of computation. With this method, the data arrays in the original program are each distributed to the processors, establishing an ownership relation, and computations defining a data item are performed by the processors owning the data.