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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.

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.

Data-flow is a natural approach to parallelism. However, describing dependencies and control between fine-grained data-flow tasks can be complex and present unwanted overheads. TALM (TALM is an Architecture and Language for Multi-threading) introduces a user-defined coarse-grained parallel data-flow model, where programmers identify code blocks, called super-instructions, to be run in parallel and connect them in a data-flow graph. TALM has been implemented as a hybrid Von Neumann/data-flow execution system: the \emph{Trebuchet}. We have observed that TALM's usefulness largely depends on how programmers specify and connect super-instructions. Thus, we present \emph{Couillard}, a full compiler that creates, based on an annotated C-program, a data-flow graph and C-code corresponding to each super-instruction. We show that our toolchain allows one to benefit from data-flow execution and explore sophisticated parallel programming techniques, with small effort. To evaluate our system we have executed a set of real applications on a large multi-core machine. Comparison with popular parallel programming methods shows competitive speedups, while providing an easier parallel programing approach.

Data-flow is a natural approach to parallelism. However, describing dependencies and control between fine-grained data-flow tasks can be complex and present unwanted overheads. TALM (TALM is an Architecture and Language for Multi-threading) introduces a user-defined coarse-grained parallel data-flow model, where programmers identify code blocks, called super-instructions, to be run in parallel and connect them in a data-flow graph. TALM has been implemented as a hybrid Von Neumann/data-flow execution system: the \emph{Trebuchet}. We have observed that TALM's usefulness largely depends on how programmers specify and connect super-instructions. Thus, we present \emph{Couillard}, a full compiler that creates, based on an annotated C-program, a data-flow graph and C-code corresponding to each super-instruction. We show that our toolchain allows one to benefit from data-flow execution and explore sophisticated parallel programming techniques, with small effort. To evaluate our system we have executed a set of real applications on a large multi-core machine. Comparison with popular parallel programming methods shows competitive speedups, while providing an easier parallel programing approach.

This thesis presents a new programming model called the symmetric model in which the representation of programs is identical to the representation of data: to specify a computation, one defines a data structure. This data structure possesses the semantics of a first-class naming environment-it defines a scope and can be used to affect the evaluation environment of other expressions. We present a new programming language called Symmetric Lisp based on the symmetric model. Program structures in Symmetric Lisp are considered non- strict: a program's components may be examined even as its other elements continue to evaluate. The first part of the thesis investigates the interaction of non-strictness with first-class naming environments. The second part of the thesis discuss the compilation and implementation of Symmetric Lisp. We present an extended type-inference system for first-class environments that can be used to infer the proper evaluation environment of identifiers found within the scope of environment-yielding expressions. We also present a translation of Symmetric Lisp into a high-level data flow language.

This final report presents a summary of research accomplished to investigate parallel problem solving. Under the current grant a parallel problem solving system, PRISM (Parallel Inference System), that was implemented on the VAX/11-780, the PYRAMID and SUN machines, was ported successfully to McMOB and then to the BBN Butterfly parallel architecture. The McMOB architecture is essentially the ZMOB architecture with 16 Motorola 68000 processors, upgrading the Z80A microprocessors, interconnected in a ring structure. Experimental testing of PRISM on McMOB 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 ability to display AND-parallelism was added to the trace program which displays the execution of a program on the parallel machines. In addition to the above, work continued in the area of informative answers to be presented to a user. Heuristic techniques were developed to determine which information to display. Keywords: Splicing compilers, Debugging software, Artificial intelligence.

This final report presents a summary of research accomplished to investigate parallel problem solving. Under the current grant a parallel problem solving system, PRISM (Parallel Inference System), that was implemented on the VAX/11-780, the PYRAMID and SUN machines, was ported successfully to McMOB and then to the BBN Butterfly parallel architecture. The McMOB architecture is essentially the ZMOB architecture with 16 Motorola 68000 processors, upgrading the Z80A microprocessors, interconnected in a ring structure. Experimental testing of PRISM on McMOB 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 ability to display AND-parallelism was added to the trace program which displays the execution of a program on the parallel machines. In addition to the above, work continued in the area of informative answers to be presented to a user. Heuristic techniques were developed to determine which information to display. Keywords: Splicing compilers, Debugging software, Artificial intelligence.

International Journal of Parallel Programming 1999: Volume 27 , Issue CONTENTS. Digitized from IA1631414-07 . Previous issue: sim_international-journal-of-parallel-programming_1998-12_26_6 . Next issue: sim_international-journal-of-parallel-programming_1999-02_27_1 .

International Journal of Parallel Programming 1999: Volume 27 , Issue CONTENTS. Digitized from IA1631414-07 . Previous issue: sim_international-journal-of-parallel-programming_1998-12_26_6 . Next issue: sim_international-journal-of-parallel-programming_1999-02_27_1 .

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.

Mesham parallel programming language dumped with WikiTeam tools.

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.

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.

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.

DokuWiki: Programming Massively Parallel Multiprocessors with CUDA Dumped with DokuWiki-Dumper v0.1.44, and uploaded with dokuWikiUploader v0.1.44.

With rapidly evolving technology, multicore and manycore processors have emerged as promising architectures to benefit from increasing transistor numbers. The transition towards these parallel architectures makes today an exciting time to investigate challenges in parallel computing. The TILEPro64 is a manycore accelerator, composed of 64 tiles interconnected via multiple 8x8 mesh networks. It contains per-tile caches and supports cache-coherent shared memory by default. In this paper we present a programming technique to take advantages of distributed caching facilities in manycore processors. However, unlike other work in this area, our approach does not use architecture-specific libraries. Instead, we provide the programmer with a novel technique on how to program future Non-Uniform Cache Architecture (NUCA) manycore systems, bearing in mind their caching organisation. We show that our localised programming approach can result in a significant improvement of the parallelisation efficiency (speed-up).

The manual application of formal methods in system specification has produced successes, but in the end, despite any claims and assertions by practitioners, there is no provable relationship between a manually derived system specification or formal model and the customer's original requirements. Complex parallel and distributed system present the worst case implications for today s dearth of viable approaches for achieving system dependability. No avenue other than formal methods constitutes a serious contender for resolving the problem, and so recognition of requirements-based programming has come at a critical juncture. We describe a new, NASA-developed automated requirement-based programming method that can be applied to certain classes of systems, including complex parallel and distributed systems, to achieve a high degree of dependability.

From the bitsavers.org collection, a scanned-in computer-related document. bbn :: bbnaci :: butterfly :: BBN6149 The Uniform System Approach to Programming The Butterfly Parallel Processor Ver 2 Jun86

Several emerging fields of science and engineering are increasingly characterized by computationally intensive programs. Without parallelization, such programs do not benefit from the increasing core counts available in todays chip multiprocessors. However, writing correct and well-performing parallel programs is widely perceived to be an extremely hard problem. In order to understand the challenges faced by scientific programmers in effectively leveraging parallel computation, this dissertation first presents an in-depth field study of the practice of computational science. Based on the results of the field study, this dissertation proposes two new implicit parallel programming (IPP) solutions. With IPP, artificial constraints imposed by sequential models for automatic parallelization are overcome by use of semantic programming extensions. These preserve the ease of sequential programming and enable multiple parallelism forms without additional parallelism constructs, achieving the best of both automatic and explicit parallelization. The first IPP solution, Commutative Set, generalizes existing notions of semantic commutativity. It allows a programmer to relax execution orders prohibited under a sequential programming model with a high degree of expressiveness. The second IPP solution WeakC, provides language extensions to relax strict consistency requirements of sequential data structures, and dynamically optimizes a parallel configuration of these data structures via a combined compiler-runtime system. This dissertation evaluates both Commutative Set and WeakC on real-world applications running on real hardware, including some that are actively used by some scientists in their day-to-day research. The detailed experimental evaluation results demonstrate the effectiveness of the proposed techniques.

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.

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

These are the lecture notes for CS 15-840B, a hands-on class in programming parallel algorithms. The class was taught in the fall of 1992 by Guy Blelloch, using the programming, language NESL. It stressed the clean and concise expression of a variety of parallel algorithms. About 35 graduate students attended the class', of whom 28 took it for credit. These notes were written by students in the class, and were then reviewed and organized by Guy Blelloch and Jonathan Hardwick. The sample NESL code has been converted from the older LISP-style syntax into the new NlL-style syntax. These notes are not in a polished form, and probably contain several errors and omissions, particularly with respect to references in the literature. Corrections are welcome.

Parallel programming requires task scheduling to optimize performance; this primarily involves balancing the load over the processors. In many cases, it is critical to perform task scheduling at runtime. For example, (1) in many parallel applications the task load cannot be accurately predicted a priori; (2) in a network-based multicomputer the computational power of each processor may not remain constant. In order to support dynamic task scheduling, the programmer, usually needs to design and implement a complex set of scheduling routines, e.g., routines for maintaining task lists and handling interprocessor communication for load balancing. Unfortunately, it is very difficult and time-consuming to write and debug all of these scheduling routines. This thesis proposes a new approach which can greatly reduce the effort of developing efficient dynamic task scheduling routines. In our new approach, we decompose task scheduling into two parts - the specification of scheduling policies and the implementation of supportive scheduling operations - and then hide the latter from the programmer. We call this approach multilist scheduling, because it is based on a uniform scheduling model involving the use of multiple scheduling lists.

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.

During the second year of the award period, the Computer Science Department of the University of Rochester continued work in: 1) systems support algorithms, 2) the Butterfly programming environment, and 3) vision applications. This research produced several internal and external reports as well as much exportable code. The University of Rochester also employed DARPA Parallel Architecture Benchmark problems to test different algorithms using four different Butterfly programming environments. These tests produced several interesting results and demonstrated that the Butterfly architecture is a flexible general purpose architecture that can be effectively programmed by non-experts, using tools developed at BBN and Rochester. The University of Rochester is continuing to study the issures and concerns surrounding the effective implementation of parallel algorithms. Keywords: Parallel processors; Butterfly computers; Computer vision.

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.

This paper addresses the issue of how to make the task of writing correct and efficient parallel programs easier. Domain morphism is a new construct for specifying parallel-program optimization at a high level, and enables an equational program transformation whereby the optimized program containing implementation details is derived mechanically. In addition, we introduce the constructs index domain, data field, communication form, and hypersurfaces for facilitating parallel programming and generating efficient target parallel programs with explicit synchronization and communication. We describe an equational theory and new program transformation procedures motivated by these new constructs. A programming example illustrating the use of these constructs and the program transformation steps is given.

This paper addresses the issue of how to make the task of writing correct and efficient parallel programs easier. Domain morphism is a new construct for specifying parallel-program optimization at a high level, and enables an equational program transformation whereby the optimized program containing implementation details is derived mechanically. In addition, we introduce the constructs index domain, data field, communication form, and hypersurfaces for facilitating parallel programming and generating efficient target parallel programs with explicit synchronization and communication. We describe an equational theory and new program transformation procedures motivated by these new constructs. A programming example illustrating the use of these constructs and the program transformation steps is given.

The manual application of formal methods in system specification has produced successes, but in the end, despite any claims and assertions by practitioners, there is no provable relationship between a manually derived system specification or formal model and the customer's original requirements. Complex parallel and distributed system present the worst case implications for today s dearth of viable approaches for achieving system dependability. No avenue other than formal methods constitutes a serious contender for resolving the problem, and so recognition of requirements-based programming has come at a critical juncture. We describe a new, NASA-developed automated requirement-based programming method that can be applied to certain classes of systems, including complex parallel and distributed systems, to achieve a high degree of dependability.

Parallel programs are ore difficult to develop and reason about thansequential programs. There are two broad classes of parallel programs: (1) programs whose specifications describe ongoing behavior and interaction with an environment, and (2) programs whose specifications describe the relation between initial and final states.

The Sync Model, a parallel execution method for logic programming, is preposed. The Sync Model is a multiple-solution data-driven model that realizes AND parallelism and OR-parallelism in a logic program assuming a message-passing multiprocessor system. AND parallelism is implemented by constructing a dynamic data flow graph of the literals in the clause body with an ordering algorithm. OR parallelism is achieved by adding special Synchronization signals to the stream of partial solutions and synchronizing the multiple streams with a merge algorithm. The ordering algorithm and the merge algorithm are described. The merge algorithm is proved to be correct and therefore, the Sync Model is proved complete, i.e., the execution of a logic program under the Sync Model generates all the solutions.

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.

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 work introduces a runtime model for managing communication with support for latency-hiding. The model enables non-computer science researchers to exploit communication latency-hiding techniques seamlessly. For compiled languages, it is often possible to create efficient schedules for communication, but this is not the case for interpreted languages. By maintaining data dependencies between scheduled operations, it is possible to aggressively initiate communication and lazily evaluate tasks to allow maximal time for the communication to finish before entering a wait state. We implement a heuristic of this model in DistNumPy, an auto-parallelizing version of numerical Python that allows sequential NumPy programs to run on distributed memory architectures. Furthermore, we present performance comparisons for eight benchmarks with and without automatic latency-hiding. The results shows that our model reduces the time spent on waiting for communication as much as 27 times, from a maximum of 54% to only 2% of the total execution time, in a stencil application.

We show that in a knapsack feasibility problem an integral vector $p$, which is short, and near parallel to the constraint vector gives a branching direction with small integer width. We use this result to analyze two computationally efficient reformulation techniques on low density knapsack problems. Both reformulations have a constraint matrix with columns reduced in the sense of Lenstra, Lenstra, and Lov\'asz. We prove an upper bound on the integer width along the last variable, which becomes 1, when the density is sufficiently small. In the proof we extract from the transformation matrices a vector which is near parallel to the constraint vector $a.$ The near parallel vector is a good branching direction in the original knapsack problem, and this transfers to the last variable in the reformulations.

We show that in a knapsack feasibility problem an integral vector $p$, which is short, and near parallel to the constraint vector gives a branching direction with small integer width. We use this result to analyze two computationally efficient reformulation techniques on low density knapsack problems. Both reformulations have a constraint matrix with columns reduced in the sense of Lenstra, Lenstra, and Lov\'asz. We prove an upper bound on the integer width along the last variable, which becomes 1, when the density is sufficiently small. In the proof we extract from the transformation matrices a vector which is near parallel to the constraint vector $a.$ The near parallel vector is a good branching direction in the original knapsack problem, and this transfers to the last variable in the reformulations.

This paper presents an overview of the "Applied Parallel Computing" course taught to final year Software Engineering undergraduate students in Spring 2014 at NUST, Pakistan. The main objective of the course was to introduce practical parallel programming tools and techniques for shared and distributed memory concurrent systems. A unique aspect of the course was that Java was used as the principle programming language. The course was divided into three sections. The first section covered parallel programming techniques for shared memory systems that include multicore and Symmetric Multi-Processor (SMP) systems. In this section, Java threads was taught as a viable programming API for such systems. The second section was dedicated to parallel programming tools meant for distributed memory systems including clusters and network of computers. We used MPJ Express-a Java MPI library-for conducting programming assignments and lab work for this section. The third and the final section covered advanced topics including the MapReduce programming model using Hadoop and the General Purpose Computing on Graphics Processing Units (GPGPU).

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.

We evaluate optimized parallel sparse matrix-vector operations for two representative application areas on widespread multicore-based cluster configurations. First the single-socket baseline performance is analyzed and modeled with respect to basic architectural properties of standard multicore chips. Going beyond the single node, parallel sparse matrix-vector operations often suffer from an unfavorable communication to computation ratio. Starting from the observation that nonblocking MPI is not able to hide communication cost using standard MPI implementations, we demonstrate that explicit overlap of communication and computation can be achieved by using a dedicated communication thread, which may run on a virtual core. We compare our approach to pure MPI and the widely used "vector-like" hybrid programming strategy.

We compare the writing and execution of programs written in Cosmic Cube C with programs written in the graphical parallel programming environment and language Poker. Our example programs, an implementation of a Cholesky algorithm for a banded matrix, were written in both languages and compiled into object codes that ran on the Cosmic Cube. However the program written in Poker is shorter, faster and easier to write, easier to debug, and portable without changes to other parallel computer architectures. The Poker program was slower than the program written directly in Cosmic Cube C, however the experiments provided insights into changes that make Poker programs nearly as fast. Keywords: High level languages, Low level languages.

This contract developed and disseminated papers, ideas, algorithms, analysis, software, applications, and implementations for parallel programming environments for computer vision and for vision applications. The work has been widely reported and highly influential. The most significant work centered on the Butterfly Parallel Processor, the MaxVideo pipelined parallel image processor, and the development of the real-time computer vision laboratory. For the Butterfly, the Psyche multi-model operating system was developed and the CONSUL autoparallelizing compiler was designed. Much basic and influential performance monitoring and debugging work was completed, resulting in working systems and novel algorithms. There was also significant research in systems and applications using other parallel architectures in the laboratory, such as the MaxVideo parallel pipelined image processor. The contract developed a heterogeneous parallel architecture involving pipelined and MIMD parallelism and integrated it with a robot head.

Modern distributed systems consisting of powerful workstations and high-speed interconnection networks are an economical alternative to special- purpose super computers. The technical issues that need to be addressed in exploiting the parallelism inherent in a distributed system include heterogeneity, high-latency communication, fault tolerance and dynamic load balancing. Current software systems for parallel programming provide little or no automatic support towards these issues and require users to be experts in fault-tolerant distributed computing. The Paralex system is aimed at exploring the extent to which the parallel application programmer can be liberated from the complexities of distributed systems. Paralex is a complete programming environment and makes extensive use of graphics to define, edit, execute and debug parallel scientific applications. All of the necessary code for distributing the computation across a network and replicating it to achieve fault tolerance and dynamic load balancing is automatically generated by the system. In this paper we give an overview of Paralex and present our experiences with a prototype implementation.

Parallel computing is a type of computation in which many processing are performed concurrently often by dividing large problems into smaller ones that execute independently of each other. There are several different types of parallel computing. The first one is the shared memory architecture which harnesses the power of multiple processors and multiple cores on a single machine and uses threads of programs and shared memory to exchange data. The second type of parallel computing is the distributed architecture which harnesses the power of multiple machines in a networked environment and uses message passing to communicate processes actions to one another. This paper implements several computational algorithms using parallel programming techniques namely distributed message passing. The algorithms are Mandelbrot set, Bucket Sort, Monte Carlo, Grayscale Image Transformation, Array Summation, and Insertion Sort algorithms. All these algorithms are to be implemented using C .NET and tested in a parallel environment using the MPI. NET SDK and the DeinoMPI API. Experiments conducted showed that the proposed parallel algorithms have faster execution time than their sequential counterparts. As future work, the proposed algorithms are to be redesigned to operate on shared memory multi processor and multi core architectures.  by Youssef Bassil "Implementation of Computational Algorithms using Parallel Programming"  Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019,  URL: https://www.ijtsrd.com/papers/ijtsrd22947.pdf Paper URL: https://www.ijtsrd.com/computer-science/parallel-computing/22947/implementation-of-computational-algorithms-using-parallel-programming/youssef-bassil

Parallel computing is a type of computation in which many processing are performed concurrently often by dividing large problems into smaller ones that execute independently of each other. There are several different types of parallel computing. The first one is the shared memory architecture which harnesses the power of multiple processors and multiple cores on a single machine and uses threads of programs and shared memory to exchange data. The second type of parallel computing is the distributed architecture which harnesses the power of multiple machines in a networked environment and uses message passing to communicate processes actions to one another. This paper implements several computational algorithms using parallel programming techniques namely distributed message passing. The algorithms are Mandelbrot set, Bucket Sort, Monte Carlo, Grayscale Image Transformation, Array Summation, and Insertion Sort algorithms. All these algorithms are to be implemented using C .NET and tested in a parallel environment using the MPI. NET SDK and the DeinoMPI API. Experiments conducted showed that the proposed parallel algorithms have faster execution time than their sequential counterparts. As future work, the proposed algorithms are to be redesigned to operate on shared memory multi processor and multi core architectures.  by Youssef Bassil "Implementation of Computational Algorithms using Parallel Programming"  Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019,  URL: https://www.ijtsrd.com/papers/ijtsrd22947.pdf Paper URL: https://www.ijtsrd.com/computer-science/parallel-computing/22947/implementation-of-computational-algorithms-using-parallel-programming/youssef-bassil