The Data Parallel Programming Model

This monograph-like book assembles the thorougly revised and cross-reviewed lectures given at the School on Data Parallelism, held in Les Menuires, France, in May 1996.

The Data Parallel Programming Model

Author: Guy-Rene Perrin

Publisher: Springer Science & Business Media

ISBN: 9783540617365

Page: 284

View: 555

This monograph-like book assembles the thorougly revised and cross-reviewed lectures given at the School on Data Parallelism, held in Les Menuires, France, in May 1996. The book is a unique survey on the current status and future perspectives of the currently very promising and popular data parallel programming model. Much attention is paid to the style of writing and complementary coverage of the relevant issues throughout the 12 chapters. Thus these lecture notes are ideally suited for advanced courses or self-instruction on data parallel programming. Furthermore, the book is indispensable reading for anybody doing research in data parallel programming and related areas.

The Data Parallel Programming Model

This monograph-like book assembles the thorougly revised and cross-reviewed lectures given at the School on Data Parallelism, held in Les Menuires, France, in May 1996.

The Data Parallel Programming Model

Author: Guy-Rene Perrin

Publisher: Springer

ISBN: 9783662194195

Page: 292

View: 929

This monograph-like book assembles the thorougly revised and cross-reviewed lectures given at the School on Data Parallelism, held in Les Menuires, France, in May 1996. The book is a unique survey on the current status and future perspectives of the currently very promising and popular data parallel programming model. Much attention is paid to the style of writing and complementary coverage of the relevant issues throughout the 12 chapters. Thus these lecture notes are ideally suited for advanced courses or self-instruction on data parallel programming. Furthermore, the book is indispensable reading for anybody doing research in data parallel programming and related areas.

PDDP

PDDP allows the user to program in a shared-memory style and generates codes that are portable to a variety of parallel machines. For interprocessor communication, PDDP uses the fastest communication primitives on each platform.

PDDP

Author:

Publisher:

ISBN:

Page: 7

View: 505

PDDP, the Parallel Data Distribution Preprocessor, is a data parallel programming model for distributed memory parallel computers. PDDP impelments High Performance Fortran compatible data distribution directives and parallelism expressed by the use of Fortran 90 array syntax, the FORALL statement, and the (WRERE?) construct. Distribued data objects belong to a global name space; other data objects are treated as local and replicated on each processor. PDDP allows the user to program in a shared-memory style and generates codes that are portable to a variety of parallel machines. For interprocessor communication, PDDP uses the fastest communication primitives on each platform.

Programming Models for Parallel Computing

This book offers an overview of some of the most prominent parallel programming models used in high-performance computing and supercomputing systems today.

Programming Models for Parallel Computing

Author: Pavan Balaji

Publisher: MIT Press

ISBN: 0262528819

Page: 488

View: 144

An overview of the most prominent contemporary parallel processing programming models, written in a unique tutorial style.

A Programming Model for Massive Data Parallelism with Data Dependencies

In this work, we investigate another approach. We run massively data-parallel applications on GPU clusters. We further propose a programming model for massive data parallelism with data dependencies for this scenario.

A Programming Model for Massive Data Parallelism with Data Dependencies

Author:

Publisher:

ISBN:

Page:

View: 993

Accelerating processors can often be more cost and energy effective for a wide range of data-parallel computing problems than general-purpose processors. For graphics processor units (GPUs), this is particularly the case when program development is aided by environments such as NVIDIA s Compute Unified Device Architecture (CUDA), which dramatically reduces the gap between domain-specific architectures and general purpose programming. Nonetheless, general-purpose GPU (GPGPU) programming remains subject to several restrictions. Most significantly, the separation of host (CPU) and accelerator (GPU) address spaces requires explicit management of GPU memory resources, especially for massive data parallelism that well exceeds the memory capacity of GPUs. One solution to this problem is to transfer data between the GPU and host memories frequently. In this work, we investigate another approach. We run massively data-parallel applications on GPU clusters. We further propose a programming model for massive data parallelism with data dependencies for this scenario. Experience from micro benchmarks and real-world applications shows that our model provides not only ease of programming but also significant performance gains.

Parallel and Distributed Computing

Application developers will find this book helpful to get an overview before choosing a particular programming style to study in depth, and researchers and programmers will appreciate the wealth of information concerning the various areas ...

Parallel and Distributed Computing

Author: Claudia Leopold

Publisher: Wiley-Interscience

ISBN:

Page: 260

View: 679

An all-inclusive survey of the fundamentals of parallel and distributed computing. The use of parallel and distributed computing has increased dramatically over the past few years, giving rise to a variety of projects, implementations, and buzzwords surrounding the subject. Although the areas of parallel and distributed computing have traditionally evolved separately, these models have overlapping goals and characteristics. Parallel and Distributed Computing surveys the models and paradigms in this converging area of parallel and distributed computing and considers the diverse approaches within a common text. Covering a comprehensive set of models and paradigms, the material also skims lightly over more specific details and serves as both an introduction and a survey. Novice readers will be able to quickly grasp a balanced overview with the review of central concepts, problems, and ideas, while the more experienced researcher will appreciate the specific comparisons between models, the coherency of the parallel and distributed computing field, and the discussion of less well-known proposals. Other topics covered include: * Data parallelism * Shared-memory programming * Message passing * Client/server computing * Code mobility * Coordination, object-oriented, high-level, and abstract models * And much more Parallel and Distributed Computing is a perfect tool for students and can be used as a foundation for parallel and distributed computing courses. Application developers will find this book helpful to get an overview before choosing a particular programming style to study in depth, and researchers and programmers will appreciate the wealth of information concerning the various areas of parallel and distributed computing.

Opportunities and Constraints of Parallel Computing

The Steering Committee of the workshop consisted of Prof. R. Karp (University of California at Berkeley), Prof. L. Snyder (University of Washington at Seattle), and Dr. J. L. C. Sanz (IBM Almaden Research Center).

Opportunities and Constraints of Parallel Computing

Author: Jorge L.C. Sanz

Publisher: Springer Science & Business Media

ISBN: 1461396689

Page: 166

View: 306

At the initiative of the IBM Almaden Research Center and the National Science Foundation, a workshop on "Opportunities and Constraints of Parallel Computing" was held in San Jose, California, on December 5-6, 1988. The Steering Committee of the workshop consisted of Prof. R. Karp (University of California at Berkeley), Prof. L. Snyder (University of Washington at Seattle), and Dr. J. L. C. Sanz (IBM Almaden Research Center). This workshop was intended to provide a vehicle for interaction for people in the technical community actively engaged in research on parallel computing. One major focus of the workshop was massive parallelism, covering theory and models of computing, algorithm design and analysis, routing architectures and interconnection networks, languages, and application requirements. More conventional issues involving the design and use of parallel computers with a few dozen processors were not addressed at the meeting. A driving force behind the realization of this workshop was the need for interaction between theoreticians and practitioners of parallel computation. Therefore, a group of selected participants from the theory community was invited to attend, together with well-known colleagues actively involved in parallelism from national laboratories, government agencies, and industry.

Structured Parallel Programming

The patterns-based approach offers structure and insight that developers can apply to a variety of parallel programming models Develops a composable, structured, scalable, and machine-independent approach to parallel computing Includes ...

Structured Parallel Programming

Author: Michael D. McCool

Publisher: Elsevier

ISBN: 0124159931

Page: 406

View: 642

Programming is now parallel programming. Much as structured programming revolutionized traditional serial programming decades ago, a new kind of structured programming, based on patterns, is relevant to parallel programming today. Parallel computing experts and industry insiders Michael McCool, Arch Robison, and James Reinders describe how to design and implement maintainable and efficient parallel algorithms using a pattern-based approach. They present both theory and practice, and give detailed concrete examples using multiple programming models. Examples are primarily given using two of the most popular and cutting edge programming models for parallel programming: Threading Building Blocks, and Cilk Plus. These architecture-independent models enable easy integration into existing applications, preserve investments in existing code, and speed the development of parallel applications. Examples from realistic contexts illustrate patterns and themes in parallel algorithm design that are widely applicable regardless of implementation technology. The patterns-based approach offers structure and insight that developers can apply to a variety of parallel programming models Develops a composable, structured, scalable, and machine-independent approach to parallel computing Includes detailed examples in both Cilk Plus and the latest Threading Building Blocks, which support a wide variety of computers

Data Parallel C

This book begins by introducing data parallelism and foundational topics for effective use of the SYCL standard from the Khronos Group and Data Parallel C++ (DPC++), the open source compiler used in this book.

Data Parallel C

Author: James Reinders

Publisher: Apress

ISBN: 9781484255735

Page: 548

View: 683

Learn how to accelerate C++ programs using data parallelism. This open access book enables C++ programmers to be at the forefront of this exciting and important new development that is helping to push computing to new levels. It is full of practical advice, detailed explanations, and code examples to illustrate key topics. Data parallelism in C++ enables access to parallel resources in a modern heterogeneous system, freeing you from being locked into any particular computing device. Now a single C++ application can use any combination of devices—including GPUs, CPUs, FPGAs and AI ASICs—that are suitable to the problems at hand. This book begins by introducing data parallelism and foundational topics for effective use of the SYCL standard from the Khronos Group and Data Parallel C++ (DPC++), the open source compiler used in this book. Later chapters cover advanced topics including error handling, hardware-specific programming, communication and synchronization, and memory model considerations. Data Parallel C++ provides you with everything needed to use SYCL for programming heterogeneous systems. What You'll Learn Accelerate C++ programs using data-parallel programming Target multiple device types (e.g. CPU, GPU, FPGA) Use SYCL and SYCL compilers Connect with computing’s heterogeneous future via Intel’s oneAPI initiative Who This Book Is For Those new data-parallel programming and computer programmers interested in data-parallel programming using C++.

Concurrency and Parallelism Programming Networking and Security

The data - parallel programming model is currently the most successful model for programming massively parallel computers . Unfortunately , it is , in its ...

Concurrency and Parallelism  Programming  Networking  and Security

Author: Joxan Jaffar

Publisher: Springer Science & Business Media

ISBN: 9783540620310

Page: 394

View: 288

This book constitutes the refereed proceedings of the Second Asian Conference on Computing Science, ASIAN'96, held in Singapore in December 1996. The volume presents 31 revised full papers selected from a total of 169 submissions; also included are three invited papers and 14 posters. The papers are organized in topical sections on algorithms, constraints and logic programming, distributed systems, formal systems, networking and security, programming and systems, and specification and verification.

An Introduction to Parallel Programming

Takes a tutorial approach, starting with small programming examples and building progressively to more challenging examples Explains how to develop parallel programs using MPI, Pthreads and OpenMP programming models A robust package of ...

An Introduction to Parallel Programming

Author: Peter Pacheco

Publisher: Morgan Kaufmann

ISBN: 012804618X

Page: 496

View: 291

An Introduction to Parallel Programming, Second Edition presents a tried-and-true tutorial approach that shows students how to develop effective parallel programs with MPI, Pthreads and OpenMP. As the first undergraduate text to directly address compiling and running parallel programs on multi-core and cluster architecture, this second edition carries forward its clear explanations for designing, debugging and evaluating the performance of distributed and shared-memory programs while adding coverage of accelerators via new content on GPU programming and heterogeneous programming. New and improved user-friendly exercises teach students how to compile, run and modify example programs. Takes a tutorial approach, starting with small programming examples and building progressively to more challenging examples Explains how to develop parallel programs using MPI, Pthreads and OpenMP programming models A robust package of online ancillaries for instructors and students includes lecture slides, solutions manual, downloadable source code, and an image bank New to this edition: New chapters on GPU programming and heterogeneous programming New examples and exercises related to parallel algorithms

High Level Parallel Programming Models and Supportive Environments

Integrating Task and Data Parallelism by Means of Coordination Patterns⋆ Manuel D ́ıaz, Bartolomé Rubio, Enrique Soler, and José M. Troya Dpto.

High Level Parallel Programming Models and Supportive Environments

Author: Frank Mueller

Publisher: Springer

ISBN: 3540454012

Page: 142

View: 469

On the 23rd of April, 2001, the 6th Workshop on High-Level Parallel P- gramming Models and Supportive Environments (LCTES’98) was held in San Francisco. HIPShas been held over the past six years in conjunction with IPDPS, the Internation Parallel and Distributed Processing Symposium. The HIPSworkshop focuses on high-level programming of networks of wo- stations, computing clusters and of massively-parallel machines. Its goal is to bring together researchers working in the areas of applications, language design, compilers, system architecture and programming tools to discuss new devel- ments in programming such systems. In recent years, several standards have emerged with an increasing demand of support for parallel and distributed processing. On one end, message-passing frameworks, such as PVM, MPI and VIA, provide support for basic commu- cation. On the other hand, distributed object standards, such as CORBA and DCOM, provide support for handling remote objects in a client-server fashion but also ensure certain guarantees for the quality of services. The key issues for the success of programming parallel and distributed en- ronments are high-level programming concepts and e?ciency. In addition, other quality categories have to be taken into account, such as scalability, security, bandwidth guarantees and fault tolerance, just to name a few. Today’s challenge is to provide high-level programming concepts without s- ri?cing e?ciency. This is only possible by carefully designing for those concepts and by providing supportive programming environments that facilitate program development and tuning.

Euro Par 96 Parallel Processing

The TML language yields a synchronous programming model , similar to classical dataparallel languages such as C * [ 12 ] , HYPERC [ 9 ] , Data - Parallel ...

Euro Par  96   Parallel Processing

Author: Jan Van Leeuwen

Publisher: Springer Science & Business Media

ISBN: 9783540616269

Page: 842

View: 217

Content Description #Includes bibliographical references and index.

Proceedings of the 1993 International Conference on Parallel Processing

Th functionparallelism of these problems cannot normally be directly expressed using the data - parallel programming model .

Proceedings of the 1993 International Conference on Parallel Processing

Author: Alok N. Choudhary

Publisher: CRC Press

ISBN: 9780849389856

Page: 336

View: 558

This three-volume work presents a compendium of current and seminal papers on parallel/distributed processing offered at the 22nd International Conference on Parallel Processing, held August 16-20, 1993 in Chicago, Illinois. Topics include processor architectures; mapping algorithms to parallel systems, performance evaluations; fault diagnosis, recovery, and tolerance; cube networks; portable software; synchronization; compilers; hypercube computing; and image processing and graphics. Computer professionals in parallel processing, distributed systems, and software engineering will find this book essential to their complete computer reference library.

Foundations of Data Intensive Applications

Programming. Models. Programming a parallel data-intensive application is a complex undertaking. Data-intensive frameworks provide programming abstractions ...

Foundations of Data Intensive Applications

Author: Supun Kamburugamuve

Publisher: John Wiley & Sons

ISBN: 1119713013

Page: 416

View: 647

PEEK “UNDER THE HOOD” OF BIG DATA ANALYTICS The world of big data analytics grows ever more complex. And while many people can work superficially with specific frameworks, far fewer understand the fundamental principles of large-scale, distributed data processing systems and how they operate. In Foundations of Data Intensive Applications: Large Scale Data Analytics under the Hood, renowned big-data experts and computer scientists Drs. Supun Kamburugamuve and Saliya Ekanayake deliver a practical guide to applying the principles of big data to software development for optimal performance. The authors discuss foundational components of large-scale data systems and walk readers through the major software design decisions that define performance, application type, and usability. You???ll learn how to recognize problems in your applications resulting in performance and distributed operation issues, diagnose them, and effectively eliminate them by relying on the bedrock big data principles explained within. Moving beyond individual frameworks and APIs for data processing, this book unlocks the theoretical ideas that operate under the hood of every big data processing system. Ideal for data scientists, data architects, dev-ops engineers, and developers, Foundations of Data Intensive Applications: Large Scale Data Analytics under the Hood shows readers how to: Identify the foundations of large-scale, distributed data processing systems Make major software design decisions that optimize performance Diagnose performance problems and distributed operation issues Understand state-of-the-art research in big data Explain and use the major big data frameworks and understand what underpins them Use big data analytics in the real world to solve practical problems