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Author: Borys Bradel
Publisher:
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Category :
Languages : en
Pages :
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Author: Borys Bradel
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Category :
Languages : en
Pages :
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Author: Christophe Cérin
Publisher:
ISBN:
Category :
Languages : en
Pages : 22
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Author: C. Cerin
Publisher:
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Category :
Languages : en
Pages : 0
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Author: Victor Malyshkin
Publisher: Springer Nature
ISBN: 303086359X
Category : Computers
Languages : en
Pages : 482
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Book Description
This book constitutes the proceedings of the 16th International Conference on Parallel Computing Technologies, PaCT 2021, which was held during September 13-18, 2021. The conference was planned to take place in Kaliningrad, Russia, but changed to an online event due to the COVID-19 pandemic. The 24 full and 12 short papers included in this book were carefully reviewed and selected from 62 submissions. They were organized in topical sections as follows: parallel programming methods and tools; applications; memory-efficient data structures; experimental studies; job management; essential algorithms; computing services; and cellular automata.
Author: Woenchan Lee
Publisher:
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Languages : en
Pages :
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Despite the decades of research, distributed programming is still a painful task and programming systems designed to improve productivity fall short in practice. Auto-parallelizing compilers simplify distributed programming by parallelizing sequential programs automatically for distributed execution. However, their applicability is severely limited due to the fundamental undecidability of their static analysis problem. Runtime systems for implicit parallelism can handle a broader class of programs via an expressive programming model, but their runtime overhead often becomes a performance bottleneck. To design a practical system for productive distributed programming, one must combine the strengths of different parallelization paradigms to overcome their weaknesses when used in isolation. This dissertation presents a hybrid approach to automatic program parallelization, which combines an auto-parallelizing compiler with an implicitly parallel tasking system. Our approach parallelizes programs in two steps. First, the auto-parallelizer materializes data parallelism in a program into task parallelism. Next, the tasking system dynamically analyzes dependencies between tasks and executes independent tasks in parallel. This two-stage process gives programmers a second chance when the auto-parallelizer "fails": When a part of a program is not amenable to the compiler auto-parallelization, the programmer can gracefully fall back to the run- time parallelization by writing that part directly with task parallelism. Furthermore, hand-written tasks can be seamlessly integrated with the auto-parallelized part via composable data partitioning enabled by our auto-parallelizer, which allows them to share the partitioning strategy and thereby avoid excessive communication. Key to the success of this hybrid approach is to minimize the overhead of the tasking system. To achieve this goal, we introduce dynamic tracing, a runtime mechanism for efficient tasking. The most expensive component in the tasking system is dynamic dependence analysis. Although this dynamic analysis is necessary when applications exhibit true dynamic behavior, the analysis is redundant for common cases where dependencies are (mostly) unchanging. Dynamic tracing eliminates this redundancy in dynamic dependence analysis by recording the dependence analysis of an execution trace and then replaying the recording for the subsequent occurrences of the same trace. To guarantee that a recording of a trace correctly replaces the trace's original analysis, dynamic tracing also records memory locations that hold valid data when it records a trace and replays the recording only when those locations are still valid. Dynamic tracing significantly improves the efficiency of tasking, and thereby brings the strong scalability of explicit parallelism to implicit task parallelism.
Author: Gerald Tomas
Publisher: Springer Science & Business Media
ISBN: 9783540577386
Category : Computers
Languages : en
Pages : 328
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The substantial effort of parallelizing scientific programs is only justified if the resulting codes are efficient. Thus, all types of performance tuning are important to parallel software development. But performance improvements are much more difficult to achieve with parallel programs than with sequential programs. One way to overcome this difficulty is to bring in graphical tools. This monograph covers recent developments in parallel program visualization techniques and tools and demonstrates the application of specific visualization techniques and software tools to scientific parallel programs. The solution of initial value problems of ordinary differential equations, and numerical integration are treated in detail as two important examples.
Author: Christoph W. Kessler
Publisher: Springer Science & Business Media
ISBN: 3322878651
Category : Computers
Languages : en
Pages : 235
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Book Description
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.
Author: Bernd Mohr
Publisher: Springer Science & Business Media
ISBN: 354039110X
Category : Computers
Languages : en
Pages : 429
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Book Description
This book constitutes the refereed proceedings of the 13th European PVM/MPI Users' Group Meeting, held in September 2006. The book presents 38 revised full papers together with abstracts of 6 invited contributions, 4 tutorial papers and 6 poster papers. The papers are organized in topical sections on collective communication, communication protocols, debugging and verification, fault tolerance, metacomputing and grid, parallel I/O, implementation issues, object-oriented message passing, limitations and extensions and performance.
Author: Doroshenko, Anatoliy
Publisher: IGI Global
ISBN: 1522593861
Category : Computers
Languages : en
Pages : 279
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Book Description
Emerging scientific and industrial applications in today’s world require significant computing power. Modern software tools are available for such platforms but are relatively complex and require the use of innovative programming models. One promising area in modern software design is the development, analysis, and implementation of algorithms and adaptive methods. These advancements in programming are promising but lack relevant research and understanding. Formal and Adaptive Methods for Automation of Parallel Programs Construction: Emerging Research and Opportunities is an essential reference source that solves the problem of the development of efficient models, methods, and tools for parallel programming automation based on the algebra of algorithms, term rewriting, and auto-tuning paradigms. The results of this book will help to further develop and improve existing research on design, synthesis, and optimization of sequential and parallel algorithms and programs. Featuring research on topics such as auto-tuning methods, graphics processing, and algorithmic language, this book is ideally designed for mathematicians, software engineers, data scientists, researchers, academicians, and students seeking coverage on developing tools for automated design and parallel programs.
Author: Michael A. Heroux
Publisher: SIAM
ISBN: 0898716195
Category : Computers
Languages : en
Pages : 407
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Book Description
Scientific computing has often been called the third approach to scientific discovery, emerging as a peer to experimentation and theory. Historically, the synergy between experimentation and theory has been well understood: experiments give insight into possible theories, theories inspire experiments, experiments reinforce or invalidate theories, and so on. As scientific computing has evolved to produce results that meet or exceed the quality of experimental and theoretical results, it has become indispensable.Parallel processing has been an enabling technology in scientific computing for more than 20 years. This book is the first in-depth discussion of parallel computing in 10 years; it reflects the mix of topics that mathematicians, computer scientists, and computational scientists focus on to make parallel processing effective for scientific problems. Presently, the impact of parallel processing on scientific computing varies greatly across disciplines, but it plays a vital role in most problem domains and is absolutely essential in many of them. Parallel Processing for Scientific Computing is divided into four parts: The first concerns performance modeling, analysis, and optimization; the second focuses on parallel algorithms and software for an array of problems common to many modeling and simulation applications; the third emphasizes tools and environments that can ease and enhance the process of application development; and the fourth provides a sampling of applications that require parallel computing for scaling to solve larger and realistic models that can advance science and engineering. This edited volume serves as an up-to-date reference for researchers and application developers on the state of the art in scientific computing. It also serves as an excellent overview and introduction, especially for graduate and senior-level undergraduate students interested in computational modeling and simulation and related computer science and applied mathematics aspects.Contents List of Figures; List of Tables; Preface; Chapter 1: Frontiers of Scientific Computing: An Overview; Part I: Performance Modeling, Analysis and Optimization. Chapter 2: Performance Analysis: From Art to Science; Chapter 3: Approaches to Architecture-Aware Parallel Scientific Computation; Chapter 4: Achieving High Performance on the BlueGene/L Supercomputer; Chapter 5: Performance Evaluation and Modeling of Ultra-Scale Systems; Part II: Parallel Algorithms and Enabling Technologies. Chapter 6: Partitioning and Load Balancing; Chapter 7: Combinatorial Parallel and Scientific Computing; Chapter 8: Parallel Adaptive Mesh Refinement; Chapter 9: Parallel Sparse Solvers, Preconditioners, and Their Applications; Chapter 10: A Survey of Parallelization Techniques for Multigrid Solvers; Chapter 11: Fault Tolerance in Large-Scale Scientific Computing; Part III: Tools and Frameworks for Parallel Applications. Chapter 12: Parallel Tools and Environments: A Survey; Chapter 13: Parallel Linear Algebra Software; Chapter 14: High-Performance Component Software Systems; Chapter 15: Integrating Component-Based Scientific Computing Software; Part IV: Applications of Parallel Computing. Chapter 16: Parallel Algorithms for PDE-Constrained Optimization; Chapter 17: Massively Parallel Mixed-Integer Programming; Chapter 18: Parallel Methods and Software for Multicomponent Simulations; Chapter 19: Parallel Computational Biology; Chapter 20: Opportunities and Challenges for Parallel Computing in Science and Engineering; Index.
