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.
Author: Guy-Rene Perrin
Publisher: Springer Science & Business Media
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.
This paper provides a critical look at the utility of lightweight threads as applied to data parallel scientific programming."
Author: Thomas Fahringer
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.
This is only possible by carefully designing for those concepts and by providing supportive programming environments that facilitate program development and tuning.
Author: Frank Mueller
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.
These challenges and their relevance makes reductions a benchmark for compilers, runtime systems and hardware architectures today. This work advances research on algorithmic reductions.
Author: Jan Ciesko
Wide adoption of parallel processing hardware in mainstream computing as well as the interest for efficient parallel programming in developer communities increase the demand for programming models that offer support for common algorithmic patterns. An algorithmic pattern of particular interest are reductions. Reductions are iterative memory updates of a program variable and appear in many applications. While their definition is simple, their variety of implementations including the use of different loop constructs and calling patterns makes their support in parallel programming models difficult. Further, their characteristic update operation over arbitrary data types that requires atomicity makes their execution computationally expensive and scalable execution challenging. These challenges and their relevance makes reductions a benchmark for compilers, runtime systems and hardware architectures today. This work advances research on algorithmic reductions. It improves their programmability by adding support for task-parallel and array-type reductions. Task-parallel reductions occur in while-loops and recursive algorithms. While for each recursive algorithm an iterative formulation exists, while-loop programs represent a super class of for-loop computable programs and therefore cannot be transformed or substituted. This limitation requires an explicit support for reduction algorithms that fall within this class. Since tasks are suited for a concurrent formulation of these algorithms, the presented work focuses on language extension to the task construct in OmpSs and OpenMP. In the first section of this work we present a generic support for task-parallel reductions in OmpSs and OpenMP and introduce the ideas of reduction scope, reduction domains and static and on-demand memory allocation. With this foundation and the feedback received from the OpenMP language review board, we develop a formalized proposal to add support for task-parallel reductions in OpenMP. This engagement led to a fruitful outcome as our proposal has been accepted into OpenMP recently. As a first step towards support of array-type reduction in a task-parallel programming model, we present a landscape of support techniques and group them by their underlying strategy. Techniques follow either the strategy of direct access (atomics), redirection or iteration ordering. We call techniques that implement redirection into thread-private data containers as techniques with alternative memory layouts (AMLs) and techniques that are based on iteration ordering as techniques with alternative iteration space (AIS). A universal support of AML-based techniques in parallel programming models can be achieved by defining basic interface methods allocate, get and reduce. As examples for new techniques that implement this interface, we present CachedPrivate and PIBOR. CachedPrivate implements a software cache to reduce communication caused by irregular accesses to remote nodes on distributed memory systems. PIBOR implements Privatization with In-lined Block-ordering, a technique that improves data locality by redirecting accesses into thread-local bins. Both techniques implement a get-method that returns a private memory storage for each update operation of the reduction loop. As an example of a technique with an alternative iteration space (AIS), we present Commutative Reductions (ComRed). This technique uses an inspector-executor execution model to generate knowledge about memory access patterns and memory overlaps between participating tasks. This information is used during the execution phase to schedule tasks with overlaps commutatively. We show that this execution model requires only a small set of additional language constructs. Performance results obtained throughout different Chapters of this work demonstrate that software techniques can improve application performance by a factor of 2-4.
From the programmer perspective, the Pure attribute is just another attribute
allowing to identify data parallelism inside the ... we evaluated different parallel
programming models when implementing stream and data parallelism combined
Author: I. Foster
Publisher: IOS Press
The year 2019 marked four decades of cluster computing, a history that began in 1979 when the first cluster systems using Components Off The Shelf (COTS) became operational. This achievement resulted in a rapidly growing interest in affordable parallel computing for solving compute intensive and large scale problems. It also directly lead to the founding of the Parco conference series. Starting in 1983, the International Conference on Parallel Computing, ParCo, has long been a leading venue for discussions of important developments, applications, and future trends in cluster computing, parallel computing, and high-performance computing. ParCo2019, held in Prague, Czech Republic, from 10 – 13 September 2019, was no exception. Its papers, invited talks, and specialized mini-symposia addressed cutting-edge topics in computer architectures, programming methods for specialized devices such as field programmable gate arrays (FPGAs) and graphical processing units (GPUs), innovative applications of parallel computers, approaches to reproducibility in parallel computations, and other relevant areas. This book presents the proceedings of ParCo2019, with the goal of making the many fascinating topics discussed at the meeting accessible to a broader audience. The proceedings contains 57 contributions in total, all of which have been peer-reviewed after their presentation. These papers give a wide ranging overview of the current status of research, developments, and applications in parallel computing.
0 3 2 S S 6 7 6 7 Figure 1 : Uniform Data Distribution and Binary Partition
schemes for 8 processors 0 1 2 3 2 3 Figure 2 : Row Distribution and Column
Distribution for 4 processors 2 The Data - Parallel Programming Model In a data -
Author: Kang G. Shin
The second of a three-volume compendium which represents the proceedings from the 1992 International Conference on Parallel Processing. This book covers software. Volumes I and III cover the topics of architecture and algorithms respectively, and are intended for computer professionals in parallel processing, distributed systems and software engineering.
A Calculational Approach to Flattening Nested Data Parallelism in Functional
Languages Gabriele Keller and Martin Simons Technische Universitat Berlin
Forschungsgruppe Softwaretechnik* Abstract. The data-parallel programming
model is ...
Author: Joxan Jaffar
Publisher: Springer Science & Business Media
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.
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 ...
Author: Thomas J. Sheffler
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."
Indeed , many of the algorithms are straightforward and based on the data
parallel paradigm . In the data parallel programming model , processors perform
the same work on different parts of data . It is the distribution of the data across
Author: Alexey Lastovetsky
New approaches to parallel computing are being developed that make better use of the heterogeneous cluster architecture Provides a detailed introduction to parallel computing on heterogenous clusters All concepts and algorithms are illustrated with working programs that can be compiled and executed on any cluster The algorithms discussed have practical applications in a range of real-life parallel computing problems, such as the N-body problem, portfolio management, and the modeling of oil extraction
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.
Author: James Reinders
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++.
Function - Parallel Computation in a Data - Parallel Environment Automatic
Parallelization Techniques for the EM - 4 Lubomir Bic ... of these problems cannot
normally be directly expressed using the data - parallel programming model .
Author: Alok N. Choudhary
Publisher: CRC Press
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.
Programming Models for Heterogeneous Systems Accelerator boards are hard to
program. In addition to the task ... It uses SPMD data parallelism programming
model with streaming data access to efficiently use memory. Ct  is a new data
Author: Barbara Chapman
Publisher: IOS Press
Parallel computing technologies have brought dramatic changes to mainstream computing; the majority of today's PC's, laptops and even notebooks incorporate multiprocessor chips with up to four processors. Standard components are increasingly combined with GPU's (Graphics Processing Unit), originally designed for high-speed graphics processing, and FPGA's (Free Programmable Gate Array) to build parallel computers with a wide spectrum of high-speed processing functions. The scale of this powerful hardware is limited only by factors such as energy consumption and thermal control However, in addition to hardware factors, the practical use of petascale and exascale machines is often hampered by the difficulty of developing software which will run effectively and efficiently on such architecture This book includes selected and refereed papers, presented at the 2009 international Parallel Computing conference (ParCo2009), which set out to address these problems. It provides a snapshot of the state-of-the-art of parallel computing technologies in hardware, application and software development Areas covered include: numerical algorithms, grid and cloud computing, programming - including GPU and cell programming. The book also includes papers presented at the six mini-symposia held at the conference
By this experiment we want to show the two important points : concepts of shared
data object and the process into a single parallel ... The programming model that
Orca used is Distributed Shared Memory ( DSM ) [ 7 ] for task parallelism .
HIPS 2003 is a forum for researchers in the areas of applications, computational models, language design, compilers, system architecture, and programming tools to discuss new developments in programming parallel and grid systems. The proceedings covers the design and implementation of high-level programming models for parallel and grid environments. It also looks at current programming models such as MPI and OpenMP and covers implementation techniques for OpenMP on SMP systems.
Data-parallel languages offer a programming model structured and easy to
understand. The challenge consists in taking advantage of the power of present
parallel architectures by a compilation process allowing to reduce the number
and the ...
Author: Jan Van Leeuwen
Publisher: Springer Science & Business Media
Content Description #Includes bibliographical references and index.
The data parallel programming approach is characterized by a relatively large
number of synchronous processes ... The distributed - memory model has
received considerable attention because it appears to be scalable to higher
orders of ...
Author: Seyed H Roosta
Publisher: Springer Science & Business Media
Motivation It is now possible to build powerful single-processor and multiprocessor systems and use them efficiently for data processing, which has seen an explosive ex pansion in many areas of computer science and engineering. One approach to meeting the performance requirements of the applications has been to utilize the most powerful single-processor system that is available. When such a system does not provide the performance requirements, pipelined and parallel process ing structures can be employed. The concept of parallel processing is a depar ture from sequential processing. In sequential computation one processor is in volved and performs one operation at a time. On the other hand, in parallel computation several processors cooperate to solve a problem, which reduces computing time because several operations can be carried out simultaneously. Using several processors that work together on a given computation illustrates a new paradigm in computer problem solving which is completely different from sequential processing. From the practical point of view, this provides sufficient justification to investigate the concept of parallel processing and related issues, such as parallel algorithms. Parallel processing involves utilizing several factors, such as parallel architectures, parallel algorithms, parallel programming lan guages and performance analysis, which are strongly interrelated. In general, four steps are involved in performing a computational problem in parallel. The first step is to understand the nature of computations in the specific application domain.
All rights reserved. doi:10.3233/978-1-60750-004-9-24 Formalizing Parallel
Programming in Large Scale Distributed Networks: From Tasks Parallel and Data
Parallel to Applied Categorical Structures Phan CONG-VINH Centrefor Applied ...
Author: F. Xhafa
Publisher: IOS Press
The demand for more computing power has been a constant trend in many fields of science, engineering and business. Now more than ever, the need for more and more processing power is emerging in the resolution of complex problems from life sciences, financial services, drug discovery, weather forecasting, massive data processing for e-science, e-commerce and e-government etc. Grid and P2P paradigms are based on the premise to deliver greater computing power at less cost, thus enabling the solution of such complex problems. Parallel Programming, Models and Applications in Grid and P2P Systems presents recent advances for grid and P2P paradigms, middleware, programming models, communication libraries, as well as their application to the resolution of real-life problems. By approaching grid and P2P paradigms in an integrated and comprehensive way, we believe that this book will serve as a reference for researchers and developers of the grid and P2P computing communities. Important features of the book include an up-to-date survey of grid and P2P programming models, middleware and communication libraries, new approaches for modeling and performance analysis in grid and P2P systems, novel grid and P2P middleware as well as grid and P2P-enabled applications for real-life problems. Academics, scientists, software developers and engineers interested in the grid and P2P paradigms will find the comprehensive coverage of this book useful for their academic, research and development activity.