In this work, a model of computation for shared memory parallelism is presented. To address fundamental constraints of modern memory systems, the presented model constrains how parallelism interacts with memory access patterns and in doing so provides a method for design and analysis of algorithms that estimates reliable execution time based on a few architectural parameters. This model is presented as an alternative to modern thread based models that focus on computational concurrency but rely on reactive hardware policies to hide and amortize memory latency. Since modern processors use reactive mechanisms and heuristics to deduce the data access requirement of computations, the memory access costs of these threaded programs may be difficult to predict reliably. This research presents the Queue Streaming Model (QSM) that aims to address these shortcomings by providing a prescriptive mechanism to achieve latency-amortized and predictable-cost data access. Further, the work presents application of the QSM to algorithms commonly used in a number of applications. These algorithms include structured regular computations represented by merge sort, unstructured irregular computations represented by sparse matrix dense vector multiplication, and dynamic computations represented by MapReduce. The analysis of these algorithms reveal architectural tradeoffs between memory system bottlenecks and algorithm design. The techniques described in this dissertation reveal a general software approach that could be used to construct more general irregular applications, provided they can be transformed into a relational query form. It demonstrates that the QSM can be used to design algorithms that enhance utilization of memory system resources by structuring concurrency and memory accesses such that system bandwidths are balanced and latency is amortized. Finally, the benefit of applying the QSM algorithm to the Euler inviscid flow solver is demonstrated through experiments on the Intel(R) Xeon(R) E5-2680 v2 processor using ten cores. The transformation produced a speed-up of 25% over an optimized OpenMP implementation having identical computational structure.

New digital distribution and value capture models have emerged on the global music industry scene in the last ten years. These models are highly dependent on a strong underlying communications and broadband internet infrastructure which is largely present in most developed markets. South Africa, however, is a developing country whose broadband infrastructure is still nascent and not as widely available or accessible. Due to the permeation of mobile technology, most broadband internet access is likely to occur through the mobile networks in most developing markets. This stands in stark contrast to developed markets where broadband internet access occurs primarily via fixed line infrastructure with mobile as a secondary option.This research set out to investigate whether digital value distribution and value capture models which succeeded in developed countries would be suitable for the South African music industry given the broadband infrastructure constraints and the different internet access methodologies prevalent in this developing market. This research employed an exploratory research methodology in order to investigate this question and found that a mixture of value distribution and value capture models would address the entire market requirement, with mobile-centric digital distribution models being most suitable for mass market deployment. / Dissertation (MBA)--University of Pretoria, 2012. / Gordon Institute of Business Science (GIBS) / unrestricted