The Data Parallel Programming Model

by Guy-Rene Perrin

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: 927

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

by Guy-Rene Perrin

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: 545

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

by

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: 481

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

by Pavan Balaji

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: 791

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

by

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: 126

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.

On the Utility of Threads for Data Parallel Programming

by Thomas Fahringer

This paper provides a critical look at the utility of lightweight threads as applied to data parallel scientific programming. (KAR) P. 2.

On the Utility of Threads for Data Parallel Programming

Author: Thomas Fahringer

Publisher:

ISBN:

Page: 15

View: 551

Threads provide a useful programming model for asynchronous behavior because of their ability to encapsulate units of work that can then be scheduled for execution at runtime, based on the dynamic state of a system. Recently, the threaded model has been applied to the domain of data parallel scientific codes, and initial reports indicate that the threaded model can produce performance gains over non-threaded approaches, primarily through the use of overlapping useful computation with communication latency. However, overlapping computation with communication is possible without the benefit of threads if the communication system supports asynchronous primitives, and this comparison has not been made in previous papers. This paper provides a critical look at the utility of lightweight threads as applied to data parallel scientific programming. (KAR) P. 2.

Data Parallel C

by James Reinders

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: 157

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++.

Opportunities and Constraints of Parallel Computing

by Jorge L.C. Sanz

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: 464

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.

Requirements for Data Parallel Programming Environments

by

A more recent example is the "interactive vectorizer." The goal of this paper is to convey an understanding of the tools and strategies that will be needed to adequately support efficient, machine-independent, data-parallel programming.

Requirements for Data Parallel Programming Environments

Author:

Publisher:

ISBN:

Page: 25

View: 153

Over the past decade, research in programming systems to support scalable parallel computation has sought ways to provide an efficient machine-independent programming model. Initial efforts concentrated on automatic detection of parallelism using extensions to compiler technology developed for automatic vectorization. Many advanced techniques were tried. However, after over a half-decade of research, most investigators were ready to admit that fully automatic techniques would be insufficient by themselves to support general parallel programming, even in the limited domain of scientific computation. In other words, in an effective parallel programming system, the programmer would have to provide additional information to help the system parallelize applications. This realization led the research community to consider extensions to existing programming languages, such as Fortran and C, that could be used to help specify parallelism. An important strategy for exploiting scalable parallelism is the use of data parallelism, in which the problem domain is subdivided into regions and each region is mapped onto a different processor. These factors have led to a widespread interest in data-parallel languages such as Fortran D, High Performance Fortran (HPF), and DataParallel C as a means of writing portable parallel software. To help the programmer make good design decisions, the programming system should include mechanisms that explain the behavior of object code in terms of the source program from which it was compiled. For sequential programs, the standard "symbolic debugger," supporting single-step execution of the program source rather than the object program, provides such a facility. A more recent example is the "interactive vectorizer." The goal of this paper is to convey an understanding of the tools and strategies that will be needed to adequately support efficient, machine-independent, data-parallel programming.

An Object oriented Approach to Nested Data Parallelism

by Thomas J. Sheffler

pC ++ The PCH ( 7 ) language defines a new programming model called the
distributed collection model . ” This model is not quite data - parallel and it does
not support nested parallelism . Its collections provide “ object parallelism " : a ...

An Object oriented Approach to Nested Data Parallelism

Author: Thomas J. Sheffler

Publisher:

ISBN:

Page: 16

View: 814

Abstract: "This paper describes an implementation technique for integrating nested data parallelism into an object-oriented language. Data-parallel programming employs sets of data called 'collections' and expresses parallelism as operations performed over the elements of a collection. When the elements of a collection are also collections, then there is the possibility for 'nested data parallelism.' Few current programming languages support nested data parallelism however. In an object-oriented framework, a collection is a single object. Its type defines the parallel operations that may be applied to it. Our goal is to design and build an object-oriented data-parallel programming environment supporting nested data parallelism. Our initial approach is built upon three fundamental additions to C++. We add new parallel base types by implementing them as classes, and add a new parallel collection type called a 'vector' that is implemented as a template. Only one new language feature is introduced: the foreach construct, which is the basis for exploiting elementwise parallelism over collections. The strength of the method lies in the compilation strategy, which translates nested data-parallel C++ into ordinary C++. Extracting the potential parallelism in nested foreach constructs is called 'flattening' nested parallelism. We show how to flatten foreach constructs using a simple program transformation. Our prototype system produces vector code which has been successfully run on workstations, a CM-2 and a CM-5."

Structured Parallel Programming

by Michael D. McCool

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: 836

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

An Introduction to Parallel Programming

by Peter Pacheco

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: 9780128046050

Page: 450

View: 536

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. In edition, this new edition includes coverage of accelerators via new content on GPU programming and heterogeneous programming. New and improved user-friendly exercises teach student how to compile, run and modify example programs. Takes a tutorial approach, starting with small programming examples and building progressively to more challenging examples Focuses on designing, debugging and evaluating the performance of distributed and shared-memory programs Explains how to develop parallel programs using MPI, Pthreads and OpenMP programming models Includes a robust package of online ancillaries for instructors and students Provides lecture slides, a solutions manual, downloadable source code and an image bank

XcalableMP PGAS Programming Language

by Mitsuhisa Sato

This open access book presents XcalableMP language from its programming model and basic concept to the experience and performance of applications described in XcalableMP.

XcalableMP PGAS Programming Language

Author: Mitsuhisa Sato

Publisher: Springer

ISBN: 9789811576829

Page: 262

View: 967

XcalableMP is a directive-based parallel programming language based on Fortran and C, supporting a Partitioned Global Address Space (PGAS) model for distributed memory parallel systems. This open access book presents XcalableMP language from its programming model and basic concept to the experience and performance of applications described in XcalableMP. XcalableMP was taken as a parallel programming language project in the FLAGSHIP 2020 project, which was to develop the Japanese flagship supercomputer, Fugaku, for improving the productivity of parallel programing. XcalableMP is now available on Fugaku and its performance is enhanced by the Fugaku interconnect, Tofu-D. The global-view programming model of XcalableMP, inherited from High-Performance Fortran (HPF), provides an easy and useful solution to parallelize data-parallel programs with directives for distributed global array and work distribution and shadow communication. The local-view programming adopts coarray notation from Coarray Fortran (CAF) to describe explicit communication in a PGAS model. The language specification was designed and proposed by the XcalableMP Specification Working Group organized in the PC Consortium, Japan. The Omni XcalableMP compiler is a production-level reference implementation of XcalableMP compiler for C and Fortran 2008, developed by RIKEN CCS and the University of Tsukuba. The performance of the XcalableMP program was used in the Fugaku as well as the K computer. A performance study showed that XcalableMP enables a scalable performance comparable to the message passing interface (MPI) version with a clean and easy-to-understand programming style requiring little effort.

Data Parallelism with Hierarchically Tiled Objects

by James C. Brodman

Exploiting parallelism in modern machines increases the di culty of developing applications.

Data Parallelism with Hierarchically Tiled Objects

Author: James C. Brodman

Publisher:

ISBN:

Page:

View: 804

Exploiting parallelism in modern machines increases the di culty of developing applications. Thus, new abstractions are needed that facilitate parallel programming and at the same time allow the programmer to control performance. Tiling is a very important primitive for controlling both parallelism and locality, but many traditional approaches to tiling are only applicable to computations on dense arrays. This thesis makes several contributions, all in the general area of data parallel operators for the programming of multiprocessors and their current most popular incarnation, multicores. It accomplishes this through the development of Ravenna, a library of data parallel operators for shared-memory systems. Ravenna extends previous work on a data type for dense arrays called the Hierarchically Tiled Array, or HTA. Ravenna supports arbitrary data types, enabling programmers to write data parallel computations based on other data types such as sets or graphs. Ravenna provides programmers with several mechanisms for tiling data types. In particular for data structures other than dense arrays, it provides a generalized approach called functional tiling. Functional tiling provides programmers with a separation of concerns between implementing a computation and how to tile it. Functional tiling in this way also acts as a tuning mechanism that allows programmers to tune the performance of their codes by plugging in di erent tiling strategies. This thesis evaluates the programming model of expressing programs as a sequence of higher level data parallel operators through examining several applications from di erent domains written in Ravenna. These applications include simple microbenchmarks used to compare against another shared-memory programming library, a solver for banded linear systems called SPIKE, n-body simulation, clustering, and discrete optimization. The evaluation shows that these programs can be elegantly expressed by the programming model, and that the model's applicability is not limited to computations based on dense arrays. Particularly, it shows that the resulting programs resemble conventional, sequential programs, simplifying programmer e ort and that the available abstractions provided by Ravenna allow programmers to tune in order to obtain good parallel performance.