JOLTS : checkpointing and coordination in grid systems

The need for increased computational power is growing faster than our ability to produce faster computers. Already researchers are proposing systems that require peta-flop capable super computers, a far cry from what is currently capable. To meet such high computational requirements, networks of computers will be required. While it is possible to network together computers to achieve a single task, making that network more flexible to handle a multitude of different tasks is the promise of grid computing. Grid systems are slowly appearing that are designed to run many independent tasks, and provide the ability for programs to migrate between machines before completion. However, these systems lack coordination capabilities. Many grid systems/environments allow multiple tasks to communicate/coordinate with each other based on various paradigms, but don't provide migration capabilities. This thesis proposes a system, called JOLTS, that attempts to fill a gap by providing both checkpointing and coordination capabilities. The coordination model offered by JOLTS is based on the Objective Linda coordination language, with some additions. This thesis will show that the object space model is an effective form of coordination and communication, and can effectively be combined with checkpointing capabilities inside the same grid system

JOLTS : checkpointing and coordination in grid systems

The need for increased computational power is growing faster than our ability to produce faster computers. Already researchers are proposing systems that require peta-flop capable super computers, a far cry from what is currently capable. To meet such high computational requirements, networks of computers will be required. While it is possible to network together computers to achieve a single task, making that network more flexible to handle a multitude of different tasks is the promise of grid computing. Grid systems are slowly appearing that are designed to run many independent tasks, and provide the ability for programs to migrate between machines before completion. However, these systems lack coordination capabilities. Many grid systems/environments allow multiple tasks to communicate/coordinate with each other based on various paradigms, but don't provide migration capabilities. This thesis proposes a system, called JOLTS, that attempts to fill a gap by providing both checkpointing and coordination capabilities. The coordination model offered by JOLTS is based on the Objective Linda coordination language, with some additions. This thesis will show that the object space model is an effective form of coordination and communication, and can effectively be combined with checkpointing capabilities inside the same grid system

Parallel data compression

Data compression schemes remove data redundancy in communicated and stored data and increase the effective capacities of communication and storage devices. Parallel algorithms and implementations for textual data compression are surveyed. Related concepts from parallel computation and information theory are briefly discussed. Static and dynamic methods for codeword construction and transmission on various models of parallel computation are described. Included are parallel methods which boost system speed by coding data concurrently, and approaches which employ multiple compression techniques to improve compression ratios. Theoretical and empirical comparisons are reported and areas for future research are suggested

Autonomic behavioural framework for structural parallelism over heterogeneous multi-core systems.

With the continuous advancement in hardware technologies, significant research has been devoted to design and develop high-level parallel programming models that allow programmers to exploit the latest developments in heterogeneous multi-core/many-core architectures. Structural programming paradigms propose a viable solution for e ciently programming modern heterogeneous multi-core architectures equipped with one or more programmable Graphics Processing Units (GPUs). Applying structured programming paradigms, it is possible to subdivide a system into building blocks (modules, skids or components) that can be independently created and then used in di erent systems to derive multiple functionalities. Exploiting such systematic divisions, it is possible to address extra-functional features such as application performance, portability and resource utilisations from the component level in heterogeneous multi-core architecture. While the computing function of a building block can vary for di erent applications, the behaviour (semantic) of the block remains intact. Therefore, by understanding the behaviour of building blocks and their structural compositions in parallel patterns, the process of constructing and coordinating a structured application can be automated. In this thesis we have proposed Structural Composition and Interaction Protocol (SKIP) as a systematic methodology to exploit the structural programming paradigm (Building block approach in this case) for constructing a structured application and extracting/injecting information from/to the structured application. Using SKIP methodology, we have designed and developed Performance Enhancement Infrastructure (PEI) as a SKIP compliant autonomic behavioural framework to automatically coordinate structured parallel applications based on the extracted extra-functional properties related to the parallel computation patterns. We have used 15 di erent PEI-based applications (from large scale applications with heavy input workload that take hours to execute to small-scale applications which take seconds to execute) to evaluate PEI in terms of overhead and performance improvements. The experiments have been carried out on 3 di erent Heterogeneous (CPU/GPU) multi-core architectures (including one cluster machine with 4 symmetric nodes with one GPU per node and 2 single machines with one GPU per machine). Our results demonstrate that with less than 3% overhead, we can achieve up to one order of magnitude speed-up when using PEI for enhancing application performance

Parallel algorithms for iris biometrics

Iris biometrics involves preprocessing, feature extraction and identification phase. In this thesis,an effort has been made to introduce parallelism in feature extraction and identification phases. Local features invariant to scale, rotation, illumination are extracted using Scale Invariant Feature Transform (SIFT). In order to achieve speedup during feature extraction, parallelism has been introduced during scale space construction using SIMD hypercube. The parallel time complexity is O(N2) whereas sequential algorithm performs with complexity of O(lsN2, where l is the number of octaves, s is the number of Gaussian scale levels within an octave and N × N is the size of iris image