Exploiting iteration-level parallelism in dataflow programs

The term "dataflow" generally encompasses three distinct aspects of computation - a data-driven model of computation, a functional/declarative programming language, and a special-purpose multiprocessor architecture. In this paper we decouple the language and architecture issues by demonstrating that declarative programming is a suitable vehicle for the programming of conventional distributed-memory multiprocessors.This is achieved by appling several transformations to the compiled declarative program to achieve iteration-level (rather than instruction-level) parallelism. The transformations first group individual instructions into sequential light-weight processes, and then insert primitives to: (1) cause array allocation to be distributed over multiple processors, (2) cause computation to follow the data distribution by inserting an index filtering mechanism into a given loop and spawning a copy of it on all PEs; the filter causes each instance of that loop to operate on a different subrange of the index variable.The underlying model of computation is a dataflow/von Neumann hybrid in that exection within a process is control-driven while the creation, blocking, and activation of processes is data-driven.The performance of this process-oriented dataflow system (PODS) is demonstrated using the hydrodynamics simulation benchmark called SIMPLE, where a 19-fold speedup on a 32-processor architecture has been achieved

The exploitation of parallelism on shared memory multiprocessors

PhD ThesisWith the arrival of many general purpose shared memory multiple processor (multiprocessor) computers into the commercial arena during the mid-1980's, a rift has opened between the raw processing power offered by the emerging hardware and the relative inability of its operating software to effectively deliver this power to potential users. This rift stems from the fact that, currently, no computational model with the capability to elegantly express parallel activity is mature enough to be universally accepted, and used as the basis for programming languages to exploit the parallelism that multiprocessors offer. To add to this, there is a lack of software tools to assist programmers in the processes of designing and debugging parallel programs. Although much research has been done in the field of programming languages, no undisputed candidate for the most appropriate language for programming shared memory multiprocessors has yet been found. This thesis examines why this state of affairs has arisen and proposes programming language constructs, together with a programming methodology and environment, to close the ever widening hardware to software gap. The novel programming constructs described in this thesis are intended for use in imperative languages even though they make use of the synchronisation inherent in the dataflow model by using the semantics of single assignment when operating on shared data, so giving rise to the term shared values. As there are several distinct parallel programming paradigms, matching flavours of shared value are developed to permit the concise expression of these paradigms.The Science and Engineering Research Council

Active data structures on GPGPUs

Active data structures support operations that may affect a large number of elements of an aggregate data structure. They are well suited for extremely fine grain parallel systems, including circuit parallelism. General purpose GPUs were designed to support regular graphics algorithms, but their intermediate level of granularity makes them potentially viable also for active data structures. We consider the characteristics of active data structures and discuss the feasibility of implementing them on GPGPUs. We describe the GPU implementations of two such data structures (ESF arrays and index intervals), assess their performance, and discuss the potential of active data structures as an unconventional programming model that can exploit the capabilities of emerging fine grain architectures such as GPUs

Parallel processing and expert systems

Whether it be monitoring the thermal subsystem of Space Station Freedom, or controlling the navigation of the autonomous rover on Mars, NASA missions in the 1990s cannot enjoy an increased level of autonomy without the efficient implementation of expert systems. Merely increasing the computational speed of uniprocessors may not be able to guarantee that real-time demands are met for larger systems. Speedup via parallel processing must be pursued alongside the optimization of sequential implementations. Prototypes of parallel expert systems have been built at universities and industrial laboratories in the U.S. and Japan. The state-of-the-art research in progress related to parallel execution of expert systems is surveyed. The survey discusses multiprocessors for expert systems, parallel languages for symbolic computations, and mapping expert systems to multiprocessors. Results to date indicate that the parallelism achieved for these systems is small. The main reasons are (1) the body of knowledge applicable in any given situation and the amount of computation executed by each rule firing are small, (2) dividing the problem solving process into relatively independent partitions is difficult, and (3) implementation decisions that enable expert systems to be incrementally refined hamper compile-time optimization. In order to obtain greater speedups, data parallelism and application parallelism must be exploited

Exploiting iteration-level parallelism in declarative programs

In order to achieve viable parallel processing three basic criteria must be met: (1) the system must provide a programming environment which hides the details of parallel processing from the programmer; (2) the system must execute efficiently on the given hardware; and (3) the system must be economically attractive.The first criterion can be met by providing the programmer with an implicit rather than explicit programming paradigm. In this way ali of the synchronization and distribution are handled automatically. To meet the second criterion, the system must perform synchronization and distribution in such a way that the available computing resources are used to their utmost. And to meet the third criterion, the system must not require esoteric or expensive hardware to achieve efficient utilization.This dissertation reports on the Process-Oriented Dataflow System (PODS), which meets all of the above criteria. PODS uses a hybrid von Neumann-Dataflow model of computation supported by an automatic partitioning and distribution scheme. The new partitioning and distribution algorithm is presented along with the underlying principles. Four new mechanisms for distribution are presented: (1) a distributed array allocation operator for data distribution; (2) a distributed L operator for code distribution; (3) a range filter for restriction index ranges for different PEs; and (4) a specialized apply operator for functional parallelism.Simulations show that PODS balances communication overhead with distributed processing to achieve efficient parallel execution on distributed memory multiprocessors. This is partially due to a new software array caching scheme, called remote caching, which greatly reduces the amount of remote memory reads. PODS is designed to use off-the-shelf components, with no specialized hardware. In this way a real PODS machine can be built quickly and cost effectively. The system is currently being retargeted to the Intel iPSC/2 so that it can be run on commercially available equipment

Dataflow development of medium-grained parallel software

PhD ThesisIn the 1980s, multiple-processor computers (multiprocessors) based on conven- tional processing elements emerged as a popular solution to the continuing demand for ever-greater computing power. These machines offer a general-purpose parallel processing platform on which the size of program units which can be efficiently executed in parallel - the "grain size" - is smaller than that offered by distributed computing environments, though greater than that of some more specialised architectures. However, programming to exploit this medium-grained parallelism remains difficult. Concurrent execution is inherently complex, yet there is a lack of programming tools to support parallel programming activities such as program design, implementation, debugging, performance tuning and so on. In helping to manage complexity in sequential programming, visual tools have often been used to great effect, which suggests one approach towards the goal of making parallel programming less difficult. This thesis examines the possibilities which the dataflow paradigm has to offer as the basis for a set of visual parallel programming tools, and presents a dataflow notation designed as a framework for medium-grained parallel programming. The implementation of this notation as a programming language is discussed, and its suitability for the medium-grained level is examinedScience and Engineering Research Council of Great Britain EC ERASMUS schem

The PARSE Programming Paradigm. Part I: Software Development Methodology. Part II: Software Development Support Tools

The programming methodology of PARSE (parallel software environment), a software environment being developed for reconfigurable non-shared memory parallel computers, is described. This environment will consist of an integrated collection of language interfaces, automatic and semi-automatic debugging and analysis tools, and operating system —all of which are made more flexible by the use of a knowledge-based implementation for the tools that make up PARSE. The programming paradigm supports the user freely choosing among three basic approaches /abstractions for programming a parallel machine: logic-based descriptive, sequential-control procedural, and parallel-control procedural programming. All of these result in efficient parallel execution. The current work discusses the methodology underlying PARSE, whereas the companion paper, “The PARSE Programming Paradigm — II: Software Development Support Tools,” details each of the component tools

Directions in parallel programming: HPF, shared virtual memory and object parallelism in pC++

Fortran and C++ are the dominant programming languages used in scientific computation. Consequently, extensions to these languages are the most popular for programming massively parallel computers. We discuss two such approaches to parallel Fortran and one approach to C++. The High Performance Fortran Forum has designed HPF with the intent of supporting data parallelism on Fortran 90 applications. HPF works by asking the user to help the compiler distribute and align the data structures with the distributed memory modules in the system. Fortran-S takes a different approach in which the data distribution is managed by the operating system and the user provides annotations to indicate parallel control regions. In the case of C++, we look at pC++ which is based on a concurrent aggregate parallel model

An Application Perspective on High-Performance Computing and Communications

We review possible and probable industrial applications of HPCC focusing on the software and hardware issues. Thirty-three separate categories are illustrated by detailed descriptions of five areas -- computational chemistry; Monte Carlo methods from physics to economics; manufacturing; and computational fluid dynamics; command and control; or crisis management; and multimedia services to client computers and settop boxes. The hardware varies from tightly-coupled parallel supercomputers to heterogeneous distributed systems. The software models span HPF and data parallelism, to distributed information systems and object/data flow parallelism on the Web. We find that in each case, it is reasonably clear that HPCC works in principle, and postulate that this knowledge can be used in a new generation of software infrastructure based on the WebWindows approach, and discussed in an accompanying paper

Parallel alogorithms for MIMD parallel computers

This thesis mainly covers the design and analysis of asynchronous parallel algorithms that can be run on MIMD (Multiple Instruction Multiple Data) parallel computers, in particular the NEPTUNE system at Loughborough University. Initially the fundamentals of parallel computer architectures are introduced with different parallel architectures being described and compared. The principles of parallel programming and the design of parallel algorithms are also outlined. Also the main characteristics of the 4 processor MIMD NEPTUNE system are presented, and performance indicators, i.e. the speed-up and the efficiency factors are defined for the measurement of parallelism in a given system. Both numerical and non-numerical algorithms are covered in the thesis. In the numerical solution of partial differential equations, a new parallel 9-point block iterative method is developed. Here, the organization of the blocks is done in such a way that each process contains its own group of 9 points on the network, therefore, they can be run in parallel. The parallel implementation of both 9-point and 4- point block iterative methods were programmed using natural and redblack ordering with synchronous and asynchronous approaches. The results obtained for these different implementations were compared and analysed. Next the parallel version of the A.G.E. (Alternating Group Explicit) method is developed in which the explicit nature of the difference equation is revealed and exploited when applied to derive the solution of both linear and non-linear 2-point boundary value problems. Two strategies have been used in the implementation of the parallel A.G.E. method using the synchronous and asynchronous approaches. The results from these implementations were compared. Also for comparison reasons the results obtained from the parallel A.G.E. were compared with the ~ corresponding results obtained from the parallel versions of the Jacobi, Gauss-Seidel and S.O.R. methods. Finally, a computational complexity analysis of the parallel A.G.E. algorithms is included. In the area of non-numeric algorithms, the problems of sorting and searching were studied. The sorting methods which were investigated was the shell and the digit sort methods. with each method different parallel strategies and approaches were used and compared to find the best results which can be obtained on the parallel machine. In the searching methods, the sequential search algorithm in an unordered table and the binary search algorithms were investigated and implemented in parallel with a presentation of the results. Finally, a complexity analysis of these methods is presented. The thesis concludes with a chapter summarizing the main results