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https://annas-archive.org/md5/0415c8cdc503eb8fdb60065f55a10606 Open Library:- OL19896611W

https://annas-archive.org/md5/0415c8cdc503eb8fdb60065f55a10606 Open Library:- OL19896611W

One important feature of a parallel programming model is deterministic semantics: that is, a program produces the same output on every execution with a given input, regardless of the parallel schedule chosen. Determinism makes parallel programs much easier to write, understand, debug, and maintain. Further, many (though not all) parallel programs are, in fact, intended to have deterministic semantics. However, general-purpose languages, particularly those that allow arbitrary updates to shared data, typically do not guarantee determinism, leaving the burden on the programmer to ensure that the program is correctly synchronized. In this talk, I will present Deterministic Parallel Java (DPJ), an extension of the Java programming language that guarantees deterministic semantics in the presence of aliasing and updates to shared mutable objects. An important consequence of deterministic semantics is that a working sequential program needs no further testing after parallelization with DPJ. In DPJ, the programmer partitions the heap into regions and annotates method definitions with read and write effects on those regions. The programmer also explicitly marks parallel tasks and synchronization points. The compiler checks that the method effects are correctly summarized, and that the program is correctly synchronized to ensure determinism. I will describe the core language features, illustrate them with examples, and describe the results of an initial evaluation of the language. I will also describe ongoing work, including features we plan to add to the language. ©2009 Microsoft Corporation. All rights reserved.

One important feature of a parallel programming model is deterministic semantics: that is, a program produces the same output on every execution with a given input, regardless of the parallel schedule chosen. Determinism makes parallel programs much easier to write, understand, debug, and maintain. Further, many (though not all) parallel programs are, in fact, intended to have deterministic semantics. However, general-purpose languages, particularly those that allow arbitrary updates to shared data, typically do not guarantee determinism, leaving the burden on the programmer to ensure that the program is correctly synchronized. In this talk, I will present Deterministic Parallel Java (DPJ), an extension of the Java programming language that guarantees deterministic semantics in the presence of aliasing and updates to shared mutable objects. An important consequence of deterministic semantics is that a working sequential program needs no further testing after parallelization with DPJ. In DPJ, the programmer partitions the heap into regions and annotates method definitions with read and write effects on those regions. The programmer also explicitly marks parallel tasks and synchronization points. The compiler checks that the method effects are correctly summarized, and that the program is correctly synchronized to ensure determinism. I will describe the core language features, illustrate them with examples, and describe the results of an initial evaluation of the language. I will also describe ongoing work, including features we plan to add to the language. ©2009 Microsoft Corporation. All rights reserved.

A common approach in designing parallel languages is to provide some high level handles to manipulate the use of the parallel platform. This exposes some aspects of the target platform, for example, shared vs. distributed memory or task parallelism vs. data parallelism. Depending on the circumstances, these handles might be too powerful, not powerful enough or impose unnecessary constraints. Many potential programmers avoid these languages because they require too much understanding of parallelism. Many applications written this way don't come close to achieving their potential performance. Applications written in one such language often need to be rewritten for a parallel platform that is sufficiently different. Our goal is to significantly widen the set of programmers who can program for parallel targets by providing an approach that doesn't require any understanding of parallelism, while maximizing both the flexibility for optimization and the potential for reuse of the same program. Instead of viewing the language design problem as one of providing the programmer with high level handles, we view the problem as one of designing an interface. On one side of this interface is the programmer (domain expert) who knows the application but needs no knowledge of any aspects of the platform. On the other side of the interface is the performance expert (programmer or program) who demands maximal flexibility for optimizing the mapping to a wide range of target platforms (parallel / serial, shared / distributed, homogeneous / heterogeneous, etc.) but needs no knowledge of the domain. The separation of the description of the algorithm from the description of the mapping has several benefits. It allows the description of the algorithm to be stable across a much wider range of platforms. It allows for development of the algorithm by a domain expert who has no understanding or interest in parallelism. It allows for tuning by a performance expert who has little or no understanding of the domain. It facilitates independent development of the algorithm and the tuning even if they are performed by the same individual. ©2008 Microsoft Corporation. All rights reserved.

How do we get programmers to routinely write parallel software? We have been working in earnest on this problem for over 25 years; actually much longer if you consider that the first multi-threaded machine appeared in 1958 (the Gamma 60 by Bull). But at this point, I’m not sure we’re really getting any closer to solving it. If we study the history of parallel programming, it is clear that in order to solve the parallel programming problem we need to: (1) understand how people write parallel software (mine the key design patterns), (2) agree on how to discuss programmability, and (3) stop scaring away our software developers. In this talk, I will describe these issues and how my (our?) collaborations at the UC Berkeley’s ParLab are addressing them. ©2009 Microsoft Corporation. All rights reserved.

A common approach in designing parallel languages is to provide some high level handles to manipulate the use of the parallel platform. This exposes some aspects of the target platform, for example, shared vs. distributed memory or task parallelism vs. data parallelism. Depending on the circumstances, these handles might be too powerful, not powerful enough or impose unnecessary constraints. Many potential programmers avoid these languages because they require too much understanding of parallelism. Many applications written this way don't come close to achieving their potential performance. Applications written in one such language often need to be rewritten for a parallel platform that is sufficiently different. Our goal is to significantly widen the set of programmers who can program for parallel targets by providing an approach that doesn't require any understanding of parallelism, while maximizing both the flexibility for optimization and the potential for reuse of the same program. Instead of viewing the language design problem as one of providing the programmer with high level handles, we view the problem as one of designing an interface. On one side of this interface is the programmer (domain expert) who knows the application but needs no knowledge of any aspects of the platform. On the other side of the interface is the performance expert (programmer or program) who demands maximal flexibility for optimizing the mapping to a wide range of target platforms (parallel / serial, shared / distributed, homogeneous / heterogeneous, etc.) but needs no knowledge of the domain. The separation of the description of the algorithm from the description of the mapping has several benefits. It allows the description of the algorithm to be stable across a much wider range of platforms. It allows for development of the algorithm by a domain expert who has no understanding or interest in parallelism. It allows for tuning by a performance expert who has little or no understanding of the domain. It facilitates independent development of the algorithm and the tuning even if they are performed by the same individual. ©2008 Microsoft Corporation. All rights reserved.

Tri 2 2016/2017

Tri 2 2016/2017

Lectures on Parallel Programming by Roman Elizarov. PDMI Computer Science Club, spring term 2009.

Lectures on Parallel Programming by Roman Elizarov. PDMI Computer Science Club, spring term 2009.

When algorithmic skeletons were first introduced by Cole in late 1980 the idea had an almost immediate success. The skeletal approach has been proved to be effective when application algorithms can be expressed in terms of skeletons composition. However, despite both their effectiveness and the progress made in skeletal systems design and implementation, algorithmic skeletons remain absent from mainstream practice. Cole and other researchers, focused the problem. They recognized the issues affecting skeletal systems and stated a set of principles that have to be tackled in order to make them more effective and to take skeletal programming into the parallel mainstream. In this thesis we propose tools and models for addressing some among the skeletal programming environments issues. We describe three novel approaches aimed at enhancing skeletons based systems from different angles. First, we present a model we conceived that allows algorithmic skeletons customization exploiting the macro data-flow abstraction. Then we present two results about the exploitation of meta-programming techniques for the run-time generation and optimization of macro data-flow graphs. In particular, we show how to generate and how to optimize macro data-flow graphs accordingly both to programmers provided non-functional requirements and to execution platform features. The last result we present are the Behavioural Skeletons, an approach aimed at addressing the limitations of skeletal programming environments when used for the development of component-based Grid applications. We validated all the approaches conducting several test, performed exploiting a set of tools we developed.

I present Chorus, a high-level parallel programming model targeting irregular, heap-manipulating applications like mesh refinement and triangulation, and Jchorus, an implementation of the model on top of sequential Java. One focus of Chorus is dynamism: the fact that in irregularly parallel applications, ownership and isolation are usually instance-dependent and highly dynamic. The other is locality: the property that in many of the same applications, typical imperative commands only affect small, local regions in the heap. Chorus addresses dynamism and locality through the unifying abstraction of an *object assembly*, a short-lived, speculative task that owns a local region in a shared data structure. The task can only access objects that it owns; while changes in ownership are frequent, they are local – i.e., objects only move from one assembly to a neighboring one – and linear – i.e., aliasing and copying are tightly controlled. Programming primitives include a merge-operation, by which an assembly merges with an adjacent assembly, and a split-operation, which linearly splits an assembly into smaller ones. Our abstractions are race and deadlock-free, and inherently data-centric. We demonstrate that Chorus and Jchorus allow natural programming of several important applications exhibiting irregular data-parallelism. From preliminary investigations, it demonstrates competitive performance as well. I finish with a discussion of current work on fractional ownership in Chorus, and on the integration of Chorus with the Habanero-Java language from Rice University. ©2009 Microsoft Corporation. All rights reserved.

We present a library of generic software components for parallel and distributed computations on graphs, based on the Boost Graph Library (BGL). The BGL consists of a rich set of generic graph algorithms and supporting data structures, but it was not originally designed with parallelism in mind. In this talk, we revisit the abstractions comprising the BGL in the context of distributed-memory parallelism, lifting away the implicit requirements of sequential execution and a single shared address space. We illustrate our approach by describing the process as applied to one of the core algorithms in the BGL, breadth-first search. The result is a generic algorithm that is unchanged from the sequential algorithm, requiring only the introduction of external (distributed) data structures for parallel execution. More importantly, the generic implementation retains its interface and semantics, such that other distributed algorithms can be built upon it, just as algorithms are layered in the sequential case. By characterizing these extensions as well as the extension process, we develop general principles and patterns for using (and reusing) generic parallel software libraries. We demonstrate that the resulting algorithm implementations are both efficient and scalable with performance results for several algorithms implemented in the open-source Parallel Boost Graph Library. ©2007 Microsoft Corporation. All rights reserved.

Contents: OpenMP Fortran Application Program Interface; OpenMP constructs; The OpenMP run-time library; The environment variables. Lecture Notes Collection FreeScience.info ID2852 Obtained from http://www.openmp.org/presentations/miguel/F95_OpenMPv1_v2.pdf http://www.freescience.info/go.php?pagename=books&id=2852

The Program Development Environment (PDE) is a tool for massively parallel programming of distributed-memory architectures. Adopting a knowledge-based approach, the PDE eliminates the complexity introduced by parallel hardware with distributed memory and offers complete transparency in respect of parallelism exploitation. The knowledge-based part of the PDE is realized in CLIPS. Its principal task is to find an efficient parallel realization of the application specified by the user in a comfortable, abstract, domain-oriented formalism. A large collection of fine-grain parallel algorithmic skeletons, represented as COOL objects in a tree hierarchy, contains the algorithmic knowledge. A hybrid knowledge base with rule modules and procedural parts, encoding expertise about application domain, parallel programming, software engineering, and parallel hardware, enables a high degree of automation in the software development process. In this paper, important aspects of the implementation of the PDE using CLIPS and COOL are shown, including the embedding of CLIPS with C++-based parts of the PDE. The appropriateness of the chosen approach and of the CLIPS language for knowledge-based software engineering are discussed.

International Journal of Parallel Programming 1994: Volume 22 , Issue CONTENTS. Digitized from IA1631414-07 . Previous issue: sim_international-journal-of-parallel-programming_1992-12_21_6 . Next issue: sim_international-journal-of-parallel-programming_1994-02_22_1 .

Parallel machines provide a wonderful opportunity for applications with large computational requirements. Effective use of these machines, requires a keen understanding of how they work. Each chapter provides an overview.

Programming multiprocessor architectures is a critical research issue. An overview is given of the various approaches to programming these architectures that are currently being explored. It is argued that two of these approaches, interactive programming environments and functional parallel languages, are particularly attractive since they remove much of the burden of exploiting parallel architectures from the user. Also described is recent work by the author in the design of parallel languages. Research on languages for both shared and nonshared memory multiprocessors is described, as well as the relations of this work to other current language research projects.

Here, it is explained why the FORCE parallel programming language is easily portable among six different shared-memory microprocessors, and how a two-level macro preprocessor makes it possible to hide low level machine dependencies and to build machine-independent high level constructs on top of them. These FORCE constructs make it possible to write portable parallel programs largely independent of the number of processes and the specific shared memory multiprocessor executing them.

Here, it is explained why the FORCE parallel programming language is easily portable among six different shared-memory microprocessors, and how a two-level macro preprocessor makes it possible to hide low level machine dependencies and to build machine-independent high level constructs on top of them. These FORCE constructs make it possible to write portable parallel programs largely independent of the number of processes and the specific shared memory multiprocessor executing them.

Here, it is explained why the FORCE parallel programming language is easily portable among six different shared-memory microprocessors, and how a two-level macro preprocessor makes it possible to hide low level machine dependencies and to build machine-independent high level constructs on top of them. These FORCE constructs make it possible to write portable parallel programs largely independent of the number of processes and the specific shared memory multiprocessor executing them.

Under the current grant parallel hardware and systems software implemented on ZMOB in the previous year underwent extensive testing. A parallel problem solving system, PRISM (Parallel Inference System) implemented on the VAX/11-780 in the previous year was implemented on the PYRAMID and SUN machines. The initial version of PRISM uses a simulation of the ZMOB hardware, and has been fully tested and debugged. Experimental testing of PRISM on the simulated system was undertaken in the current year. In addition, several enhancements were made to PRISM to permit experimental analyses to be made, and to incorporate additional features to take full advantage of parallelism in a problem solving environment. The tracing and statistical gathering packages were extended. An AND-parallelism capability was added to achieve a second version of the PRISM system, and other features were added to the system to more fully exploit parallelism. A constraint solving machine was integrated with PRISM. In addition to the above, a general method to permit informative answers to be presented to a user has been developed. Theoretical results were obtained for circumscription and a method for computing in protected circumscription, using Horn clauses was developed. In the area of systems hardware and software, the ZMOB processor is now fully functional and in everyday use with 128 processors.

Object-oriented programming techniques have become a vital part of modern software engineering. Most large new commercial software products are designed today using objects oriented principles along with supporting development environments and tools. Such methodology is particularly appealing for parallel computing, where there is great need for approaches capable of simplifying the programming task and producing high quality software more quickly. In this project we have carried out research that will lead to a new parallel object-oriented language based on C++. The new language, C++-Linda, is a novel combination of the most widely used object-oriented language and a successful environment for parallel computing that supports a virtual shared memory model for process interaction. This combination is an especially appropriate one because Linda's memory model is naturally object-oriented, enabling a seamless design that is completely consistent with object-oriented methodology. In this Phase I project, we have carried out research to understand the basic issues underlying parallel object-oriented languages, and we have created a preliminary design for C++-Linda. In a future Phase II project, we plan to develop a prototype implementation that will serve as a guide for eventual commercialization. Parallel computing, Programming languages, Object-oriented languages, Linda, C++-Linda, (C++).

Automatic generation of sequential and parallel programs can bring the power of modern computers to more engineers and scientists. The project investigated parallel code generation and automatic derivation of parallel finite element routines. Software packages GENCRAY, a code translator producing Cray Fortran, and PIER, a finite element code derivation system, have been constructed as research tools.

A hands-on introduction to parallel programming based on the Message-Passing Interface (MPI) standard, the de-facto industry standard adopted by major vendors of commercial parallel systems. This textbook/tutorial, based on the C language, contains many fully-developed examples and exercises. The complete source code for the examples is available in both C and Fortran 77. Students and professionals will find that the portability of MPI, combined with a thorough grounding in parallel programming principles, will allow them to program any parallel system, from a network of workstations to a parallel supercomputer. 

This progress report summarizes work performed under AFOSR-88-0152 on parallel logic programming, problem solving, and deductive databases. A parallel problem solving system, PRISM (Parallel Inference System), that was implemented on McMOB was ported to the BBN Butterfly machine. Two versions of PRISM were developed and are operational on the Butterfly: a message passing ring structure system and a shared-memory system. Experimental testing of PRISM on McMOB continued, while experiments were also conducted on the Butterfly systems. Three enhancements were made and completed during the grant period. These are: a capability to handle negated queries and a capability to assert and retract statements. In addition to the above, work continued in the area of informative answers to queries in deductive data bases. A thesis was completed on the subject. An interpreter was developed and is running, that can take restricted natural language as input and can respond with a cooperative natural language output. In the area of parallel software development, the following were accomplished. Theoretical work on slicing/splicing was completed. Tools were provided for software development using artificial intelligence techniques. AI software for massively parallel architectures was started.

Challenges addressed under the grant include: (i) improving our understanding of the many-electron quantum mechanisms by which nature uses strong electron correlation for efficient energy transfer, particularly in photosynthesis and bioluminescence, (ii) providing an innovative paradigm for energy transfer in photovoltaic materials by which new levels of solar efficiency are achieved through the use of strong electron correlation and entanglement, (iii) enhancing two-electron reduced-density-matrix (2-RDM)-based electronic-structure methods that significantly expand the range of strongly correlated molecular systems that can be studied with applications throughout science and engineering, and (iv) developing a new generation of large-scale, parallel algorithms for performing semidefinite programming with applications to problems in engineering, computer science, statistics, finance and economics. Research led to important technology transitions including the formation of RDMCHEM LLC, a software company that is developing the next generation of computational software for chemistry with applications to engineering, molecular biology, and physics.

CONTEXT: Writing software for the current generation of parallel systems requires significant programmer effort, and the community is seeking alternatives that reduce effort while still achieving good performance. OBJECTIVE: Measure the effect of parallel programming models (message-passing vs. PRAM-like) on programmer effort. DESIGN, SETTING, and SUBJECTS: One group of subjects implemented sparse-matrix dense-vector multiplication using message-passing (MPI), and a second group solved the same problem using a PRAM-like model (XMTC). The subjects were students in two graduate-level classes: one class was taught MPI and the other was taught XMTC. MAIN OUTCOME MEASURES: Development time, program correctness. RESULTS: Mean XMTC development time was 4.8 hours less than mean MPI development time (95% confidence interval, 2.0-7.7), a 46% reduction. XMTC programs were more likely to be correct, but the difference in correctness rates was not statistically significant (p=.16). CONCLUSIONS: XMTC solutions for this particular problem required less effort than MPI equivalents, but further studies are necessary which examine different types of problems and different levels of programmer experience.

The initial version of PRISM uses a simulation of the ZMOB hardware, and has been fully tested and debugged. In addition, several enhancements were made to PRISM to permit experimental analyses to be made, and to incorporate additional features to take full advantage of parallelism in a problem solving environment. Tracing and a statistical gathering package were added to permit experimental analysis. An AND-parallelism capability was added to achieve a second version of the PRISM system, and other features were added to the system to more fully exploit parallelism. Preliminary application and evaluation studies were performed.

The initial version of PRISM uses a simulation of the ZMOB hardware, and has been fully tested and debugged. In addition, several enhancements were made to PRISM to permit experimental analyses to be made, and to incorporate additional features to take full advantage of parallelism in a problem solving environment. Tracing and a statistical gathering package were added to permit experimental analysis. An AND-parallelism capability was added to achieve a second version of the PRISM system, and other features were added to the system to more fully exploit parallelism. Preliminary application and evaluation studies were performed.

We present a library of generic software components for parallel and distributed computations on graphs, based on the Boost Graph Library (BGL). The BGL consists of a rich set of generic graph algorithms and supporting data structures, but it was not originally designed with parallelism in mind. In this talk, we revisit the abstractions comprising the BGL in the context of distributed-memory parallelism, lifting away the implicit requirements of sequential execution and a single shared address space. We illustrate our approach by describing the process as applied to one of the core algorithms in the BGL, breadth-first search. The result is a generic algorithm that is unchanged from the sequential algorithm, requiring only the introduction of external (distributed) data structures for parallel execution. More importantly, the generic implementation retains its interface and semantics, such that other distributed algorithms can be built upon it, just as algorithms are layered in the sequential case. By characterizing these extensions as well as the extension process, we develop general principles and patterns for using (and reusing) generic parallel software libraries. We demonstrate that the resulting algorithm implementations are both efficient and scalable with performance results for several algorithms implemented in the open-source Parallel Boost Graph Library. ©2007 Microsoft Corporation. All rights reserved.

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.

Recently, networked and cluster computation have become very popular. This paper is an introduction to a new C based parallel language for architecture-adaptive programming, aCe C. The primary purpose of aCe (Architecture-adaptive Computing Environment) is to encourage programmers to implement applications on parallel architectures by providing them the assurance that future architectures will be able to run their applications with a minimum of modification. A secondary purpose is to encourage computer architects to develop new types of architectures by providing an easily implemented software development environment and a library of test applications. This new language should be an ideal tool to teach parallel programming. In this paper, we will focus on some fundamental features of aCe C.

In many fields, real-world applications for High Performance Computing have already been developed. For these applications to stay up-to-date, new parallel strategies must be explored to yield the best performance; however, restructuring or modifying a real-world application may be daunting depending on the size of the code. In this case, a mini-app may be employed to quickly explore such options without modifying the entire code. In this work, several mini-apps have been created to enhance a real-world application performance, namely the VULCAN code for complex flow analysis developed at the NASA Langley Research Center. These mini-apps explore hybrid parallel programming paradigms with Message Passing Interface (MPI) for distributed memory access and either Shared MPI (SMPI) or OpenMP for shared memory accesses. Performance testing shows that MPI+SMPI yields the best execution performance, while requiring the largest number of code changes. A maximum speedup of 23 was measured for MPI+SMPI, but only 11 was measured for MPI+OpenMP.

In many fields, real-world applications for High Performance Computing have already been developed. For these applications to stay up-to-date, new parallel strategies must be explored to yield the best performance; however, restructuring or modifying a real-world application may be daunting depending on the size of the code. In this case, a mini-app may be employed to quickly explore such options without modifying the entire code. In this work, several mini-apps have been created to enhance a real-world application performance, namely the VULCAN code for complex flow analysis developed at the NASA Langley Research Center. These mini-apps explore hybrid parallel programming paradigms with Message Passing Interface (MPI) for distributed memory access and either Shared MPI (SMPI) or OpenMP for shared memory accesses. Performance testing shows that MPI+SMPI yields the best execution performance, while requiring the largest number of code changes. A maximum speedup of 23 was measured for MPI+SMPI, but only 11 was measured for MPI+OpenMP.

From the bitsavers.org collection, a scanned-in computer-related document. rand :: ipl :: P-1946 On Programming A Highly Parallel Machine To Be An Intelligent Technician

From the bitsavers.org collection, a scanned-in computer-related document. rand :: ipl :: P-1946 On Programming A Highly Parallel Machine To Be An Intelligent Technician

International Journal of Parallel Programming 1997: Volume 25 , Issue CONTENTS. Digitized from IA1631414-07 . Previous issue: sim_international-journal-of-parallel-programming_1996-12_24_6 . Next issue: sim_international-journal-of-parallel-programming_1997-02_25_1 .

Experimenting with Chapel Programming Language - a parallel processing programming language by Cray the Supercomputer people. It uses as much parallelism as you have available. This is a dual core laptop, so it splits up hello, world across the 2 cores efficiently. If I had 2 million? Same.

Experimenting with Chapel Programming Language - a parallel processing programming language by Cray the Supercomputer people. It uses as much parallelism as you have available. This is a dual core laptop, so it splits up hello, world across the 2 cores efficiently. If I had 2 million? Same.

Experimenting with Chapel Programming Language - a parallel processing programming language by Cray the Supercomputer people. It uses as much parallelism as you have available. This is a dual core laptop, so it splits up hello, world across the 2 cores efficiently. If I had 2 million? Same.

Tri 2 2015/2016

International Journal of Parallel Programming 2002: Volume 30 , Issue CONTENTS. Digitized from IA1631414-07 . Previous issue: sim_international-journal-of-parallel-programming_2001-12_29_6 . Next issue: sim_international-journal-of-parallel-programming_2002-02_30_1 .

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++.

This paper addresses the problem of universal synchronization primitives that can support scalable thread synchronization for large-scale many-core architectures. The universal synchronization primitives that have been deployed widely in conventional architectures like CAS and LL/SC are expected to reach their scalability limits in the evolution to many-core architectures with thousands of cores. We introduce a non-blocking full/empty bit primitive, or NB-FEB for short, as a promising synchronization primitive for parallel programming on may-core architectures. We show that the NB-FEB primitive is universal, scalable, feasible and convenient to use. NB-FEB, together with registers, can solve the consensus problem for an arbitrary number of processes (universality). NB-FEB is combinable, namely its memory requests to the same memory location can be combined into only one memory request, which consequently mitigates performance degradation due to synchronization "hot spots" (scalability). Since NB-FEB is a variant of the original full/empty bit that always returns a value instead of waiting for a conditional flag, it is as feasible as the original full/empty bit, which has been implemented in many computer systems (feasibility). The original full/empty bit is well-known as a special-purpose primitive for fast producer-consumer synchronization and has been used extensively in the specific domain of applications. In this paper, we show that NB-FEB can be deployed easily as a general-purpose primitive. Using NB-FEB, we construct a non-blocking software transactional memory system called NBFEB-STM, which can be used to handle concurrent threads conveniently. NBFEB-STM is space efficient: the space complexity of each object updated by $N$ concurrent threads/transactions is $\Theta(N)$, the optimal.

This paper addresses the problem of universal synchronization primitives that can support scalable thread synchronization for large-scale many-core architectures. The universal synchronization primitives that have been deployed widely in conventional architectures like CAS and LL/SC are expected to reach their scalability limits in the evolution to many-core architectures with thousands of cores. We introduce a non-blocking full/empty bit primitive, or NB-FEB for short, as a promising synchronization primitive for parallel programming on may-core architectures. We show that the NB-FEB primitive is universal, scalable, feasible and convenient to use. NB-FEB, together with registers, can solve the consensus problem for an arbitrary number of processes (universality). NB-FEB is combinable, namely its memory requests to the same memory location can be combined into only one memory request, which consequently mitigates performance degradation due to synchronization "hot spots" (scalability). Since NB-FEB is a variant of the original full/empty bit that always returns a value instead of waiting for a conditional flag, it is as feasible as the original full/empty bit, which has been implemented in many computer systems (feasibility). The original full/empty bit is well-known as a special-purpose primitive for fast producer-consumer synchronization and has been used extensively in the specific domain of applications. In this paper, we show that NB-FEB can be deployed easily as a general-purpose primitive. Using NB-FEB, we construct a non-blocking software transactional memory system called NBFEB-STM, which can be used to handle concurrent threads conveniently. NBFEB-STM is space efficient: the space complexity of each object updated by $N$ concurrent threads/transactions is $\Theta(N)$, the optimal.

This paper addresses the problem of universal synchronization primitives that can support scalable thread synchronization for large-scale many-core architectures. The universal synchronization primitives that have been deployed widely in conventional architectures like CAS and LL/SC are expected to reach their scalability limits in the evolution to many-core architectures with thousands of cores. We introduce a non-blocking full/empty bit primitive, or NB-FEB for short, as a promising synchronization primitive for parallel programming on may-core architectures. We show that the NB-FEB primitive is universal, scalable, feasible and convenient to use. NB-FEB, together with registers, can solve the consensus problem for an arbitrary number of processes (universality). NB-FEB is combinable, namely its memory requests to the same memory location can be combined into only one memory request, which consequently mitigates performance degradation due to synchronization "hot spots" (scalability). Since NB-FEB is a variant of the original full/empty bit that always returns a value instead of waiting for a conditional flag, it is as feasible as the original full/empty bit, which has been implemented in many computer systems (feasibility). The original full/empty bit is well-known as a special-purpose primitive for fast producer-consumer synchronization and has been used extensively in the specific domain of applications. In this paper, we show that NB-FEB can be deployed easily as a general-purpose primitive. Using NB-FEB, we construct a non-blocking software transactional memory system called NBFEB-STM, which can be used to handle concurrent threads conveniently. NBFEB-STM is space efficient: the space complexity of each object updated by $N$ concurrent threads/transactions is $\Theta(N)$, the optimal.

This paper addresses the problem of universal synchronization primitives that can support scalable thread synchronization for large-scale many-core architectures. The universal synchronization primitives that have been deployed widely in conventional architectures like CAS and LL/SC are expected to reach their scalability limits in the evolution to many-core architectures with thousands of cores. We introduce a non-blocking full/empty bit primitive, or NB-FEB for short, as a promising synchronization primitive for parallel programming on may-core architectures. We show that the NB-FEB primitive is universal, scalable, feasible and convenient to use. NB-FEB, together with registers, can solve the consensus problem for an arbitrary number of processes (universality). NB-FEB is combinable, namely its memory requests to the same memory location can be combined into only one memory request, which consequently mitigates performance degradation due to synchronization "hot spots" (scalability). Since NB-FEB is a variant of the original full/empty bit that always returns a value instead of waiting for a conditional flag, it is as feasible as the original full/empty bit, which has been implemented in many computer systems (feasibility). The original full/empty bit is well-known as a special-purpose primitive for fast producer-consumer synchronization and has been used extensively in the specific domain of applications. In this paper, we show that NB-FEB can be deployed easily as a general-purpose primitive. Using NB-FEB, we construct a non-blocking software transactional memory system called NBFEB-STM, which can be used to handle concurrent threads conveniently. NBFEB-STM is space efficient: the space complexity of each object updated by $N$ concurrent threads/transactions is $\Theta(N)$, the optimal.