No description available.

No description available.

No description available.

No description available.

Clusters of commodity, "off the shelf" workstations have given developers and users access to scalable and affordable computing resources. However, unlike large, symmetric multi-processing machines, these clusters have an up front cost in complexity, both for the developer and the user. Existing software frameworks have attempted to mitigate this complexity with varied success. In most frameworks, the user is forgotten and left to deal with an unwieldy application. This thesis presents the design, development and testing of a new C++ computer programming library, Scopira Agents Library (SAL). SAL is a message passing interface and implementation suitable for building parallel applications, with a focus on developer ease of use and user application deployment specification. The target developers and users of such a solution would be those who prefer an easy to develop library, with simpler deployment and application integration options with acceptable sacrifices to performance and scalability. The novelty of this parallel programming library is that it is more user-friendly than other existing libraries. This novelty has two major facets: (i) programmer-usability and productivity and (ii) application integration. Together, they permit a wider range of programmers to utilize parallel programming in a wider range of new and existing applications. This goal, user-friendliness, is rare among current parallel programming libraries. The result of the novelty is that parallel programming can be embedded into more applications, especially desktop applications. The user base and use cases for parallel applications can be increased, resulting in more efficient use of resources in a variety of applications. With increased efficiency, work can be performed in less time and larger problems can be tackled.

Clusters of commodity, "off the shelf" workstations have given developers and users access to scalable and affordable computing resources. However, unlike large, symmetric multi-processing machines, these clusters have an up front cost in complexity, both for the developer and the user. Existing software frameworks have attempted to mitigate this complexity with varied success. In most frameworks, the user is forgotten and left to deal with an unwieldy application. This thesis presents the design, development and testing of a new C++ computer programming library, Scopira Agents Library (SAL). SAL is a message passing interface and implementation suitable for building parallel applications, with a focus on developer ease of use and user application deployment specification. The target developers and users of such a solution would be those who prefer an easy to develop library, with simpler deployment and application integration options with acceptable sacrifices to performance and scalability. The novelty of this parallel programming library is that it is more user-friendly than other existing libraries. This novelty has two major facets: (i) programmer-usability and productivity and (ii) application integration. Together, they permit a wider range of programmers to utilize parallel programming in a wider range of new and existing applications. This goal, user-friendliness, is rare among current parallel programming libraries. The result of the novelty is that parallel programming can be embedded into more applications, especially desktop applications. The user base and use cases for parallel applications can be increased, resulting in more efficient use of resources in a variety of applications. With increased efficiency, work can be performed in less time and larger problems can be tackled.

With 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.

No description available.

In order to utilize parallel computers, four approaches, broadly speaking, to the provision of parallel software have been followed: (1) automatic production of parallel code by parallelizing—compilers which act on sequential programs written in existing languages; (2) "add on" features to existing languages that enable the programmer to make use of the parallel computer—these are specific to each machine; (3) full-blown parallel languages—these could be completely new languages, but usually they are derived from existing languages; (4) the provision of tools to aid the programmer in the detection of inherent parallelism in a given algorithm and in the design and implementation of parallel programs.

With diminishing gains in processing power from successive generations of hardware development, there is a new focus on using advances in computer science and parallel programming to build faster, more efficient software. As computers trend toward including multiple and multicore processors, parallel computing serves as a promising option for optimizing the next generation of software applications. However, models for implementing parallel programs remain highly opaque due to their reliance on languages such as Fortran, C, and C++. In this paper I investigate Python an option for implementing parallel programming techniques in application development. I analyze the efficiency and accessibility of MPI for Python and IPython Parallel packages by calculating in parallel using a Monte Carlo simulation and comparing their speeds to the sequential calculation. While MPI for Python offers the core functionality of MPI and C-like syntax in Python, IPython Parallel's architecture provides a truly unique model.