In recent years program transformation technology has matured into a practical solution for many software reengineering and migration tasks. FermaT, an industrial strength program transformation system, has demonstrated that legacy systems can be successfully transformed into efficient and maintainable structured C or COBOL code. Its core, a transformation engine, is based on mathematically proven program transformations and ensures that transformed programs are semantically equivalent to its original state. Its engine facilitates a Wide Spectrum Language (WSL), with low-level as well as high-level constructs, to capture as much information as possible during transformation steps. FermaT’s methodology and technique lack in provision of concurrent migration and analysis. This provision is crucial if the transformation process is to be further automated. As the constraint based program migration theory has demonstrated, it is inefficient and time consuming, trying to satisfy the enormous computation of the generated transformation sequence search-space and its constraints. With the objective to solve the above problems and to extend the operating range of the FermaT transformation system, this thesis proposes a Parallel Transformations Framework which makes parallel transformations processing within the FermaT environment not only possible but also beneficial for its migration process. During a migration process, many thousands of program transformations have to be applied. For example a 1 million line of assembler to C migration takes over 21 hours to be processed on a single PC. Various approaches of search, prediction techniques and a constraint-based approach to address the presented issues already exist but they solve them unsatisfactorily. To remedy this situation, this dissertation proposes a framework to extend transformation processing systems with parallel processing capabilities. The parallel system can analyse specified parallel transformation tasks and produce appropriate parallel transformations processing outlines. To underpin an automated objective, a formal language is introduced. This language can be utilised to describe and outline parallel transformation tasks whereas parallel processing constraints underpin the parallel objective. This thesis addresses and explains how transformation processing steps can be automatically parallelised within a reengineering domain. It presents search and prediction tactics within this field. The decomposition and parallelisation of transformation sequence search-spaces is outlined. At the end, the presented work is evaluated on practical case studies, to demonstrate different parallel transformations processing techniques and conclusions are drawn.

The Large Hadron Collider (LHC), whose experiments include the Compact Muon Solenoid (CMS), produces over 30 million gigabytes of data annually, and implements a distributed computing architecture—a tiered hierarchy, from Tier-0 through Tier-3—in order to process and store all of this data. Out of all of the computing tiers, Tier-3 clusters allow scientists the most freedom and flexibility to perform their analyses of LHC data. Tier-3 clusters also provide local services such as login and storage services, provide a means to locally host and analyze LHC data, and allow both remote and local users to submit grid-based jobs. Using the Rocks cluster distribution software version 6.1.1, along with the Open Science Grid (OSG) roll version 3.2.35, a grid-enabled CMS Tier-3 computing cluster was deployed at Florida International University’s Modesto A. Maidique campus. Validation metric results from Ganglia, MyOSG, and CMS Dashboard verified a successful deployment.

DRAM-based memory system suffers from increasing aggravating row buffer interference, which causes significant performance degradation and power consumption. With DRAM scaling, the overheads of row buffer interference become even worse due to higher row activation and precharge latency. Clusters have been a prevalent and successful computing framework for processing large amount of data due to their distributed and parallelized working paradigm. A task submitted to a cluster is typically divided into a number of subtasks which are designated to different work nodes running the same code but dealing with different equal portion of the dataset to be processed. Due to the existence of heterogeneity, it could easily result in stragglers unfairly slowing down the entire processing, because work nodes finish their subtasks at different rates. With the increasing problem complexity, more irregular applications are deployed on high-performance clusters due to the parallel working paradigm, and yield irregular memory access behaviors across nodes. However, the irregularity of memory access behaviors is not comprehensively studied, which results in low utilization of the integrated hybrid memory system compositing of stacked DRAM and off-chip DRAM. This dissertation lists our research results on the above three mentioned challenges in order to optimize the memory system for high efficiency in computing clusters. Details are as follows: To address low row buffer utilization caused by row buffer interference, we propose Row Buffer Cache (RBC) architecture to efficiently mitigate row buffer interference overheads. At the core of the RBC architecture, the DRAM pages with good locality are cached and escape from the row buffer interference.Such an RBC architecture significantly reduces the overheads caused by row activation and precharge, thus improves overall system performance and energy efficiency. We evaluate our RBC using SPEC CPU2006 on a DDR4 memory compared to the commodity baseline memory system along with the state-of-art methods, DICE and Bingo. Results show that RBC improves the memory performance by up to 2.24X (16.1% on average) and reduces the overall memory energy by up to 68.2% (23.6% on average) for single-core simulations. For multi-core simulations, RBC increases the performance by up to 1.55X (16.7% on average) and reduces the energy by up to 35.4% (21.3% on average). Comparing with the state-of-art methods, RBC outperforms DICE and Bingo by 8% and 5.1% on average for single-core scenario, and by 10.1% and 4.7% for multi-core scenario. To relax the straggling effect observed in clusters, we aim to speed up straggling work nodes to quicken the overall processing by leveraging exhibited performance variation, and propose StragglerHelper which conveys the memory access characteristics experienced by the forerunner to the stragglers such that stragglers can be sped up due to the accurately informed memory prefetching. A Progress Monitor is deployed to supervise the respective progresses of the work nodes and inform the memory access patterns of forerunner to straggling nodes. Our evaluation results with the SPEC MPI 2007 and BigDataBench on a cluster of 64 work nodes have shown that StragglerHelper is able to improve the execution time of stragglers by up to 99.5% with an average of 61.4%, contributing to an overall improvement of the entire cohort of the cluster by up to 46.7% with an average of 9.9% compared to the baseline cluster. To address the performance difference in the irregular application, we devise a novel method called Similarity-Managed Hybrid Memory System (SM-HMS) to improve the hybrid memory system performance by leveraging the memory access similarity among nodes in a cluster. Within SM-HMS, two techniques are proposed, Memory Access Similarity Measuring and Similarity-based Memory Access Behavior Sharing. To quantify the memory access similarity, memory access behaviors of each node are vectorized, and the distance between two vectors is used as the memory access similarity. The calculated memory access similarity is used to share memory access behaviors precisely across nodes. With the shared memory access behaviors, SM-HMS divides the stacked DRAM into two sections, the sliding window section and the outlier section. The shared memory access behaviors guide the replacement of the sliding window section while the outlier section is managed in the LRU manner. Our evaluation results with a set of irregular applications on various clusters consisting of up to 256 nodes have shown that SM-HMS outperforms the state-of-the-art approaches, Cameo, Chameleon, and Hyrbid2, on job finish time reduction by up to 58.6%, 56.7%, and 31.3%, with 46.1%, 41.6%, and 19.3% on average, respectively. SM-HMS can also achieve up to 98.6% (91.9% on average) of the ideal hybrid memory system performance. / Computer and Information Science

The work is given an analysis of Open Source Software (further referred as OSS), which allows use and create computer clusters. It explored the issue of clustering and construction of clusters. All installations, configuration and cluster management have been done on the operating system GNU / Linux. Presented OSS makes possible to compile a storage cluster, cluster with load distribution, cluster with high availability and computing cluster. Different types of benchmarks was theoretically analyzed, and practically used for measuring cluster’s performance. Results were compared with others, eg. the TOP500 list of the best clusters available online. Practical part of the work deals with comparing performance computing clusters. With several tens of computational nodes has been established cluster, where was installed package OpenMPI, which allows parallelization of calculations. Subsequently, tests were performed with the High Performance Linpack, which by calculation of linear equations provides total performance. Influence of the parallelization to algorithm PEA was also tested. To present practical usability, cluster has been tested by program John the Ripper, which serves to cracking users passwords. The work shall include the quantity of graphs clarifying the function and mainly showing the achieved results.

This thesis describes the research underpinning and the development of a cross platform application for the analysis of simultaneously recorded multi-dimensional spike trains. These spike trains are believed to carry the neural code that encodes information in a biological brain. A number of statistical methods already exist to analyse the temporal relationships between the spike trains. Historically, hundreds of spike trains have been simultaneously recorded, however as a result of technological advances recording capability has increased. The analysis of thousands of simultaneously recorded spike trains is now a requirement. Effective analysis of large data sets requires software tools that fully exploit the capabilities of modern research computers and effectively manage and present large quantities of data. To be effective such software tools must; be targeted at the field under study, be engineered to exploit the full compute power of research computers and prevent information overload of the researcher despite presenting a large and complex data set. The Visualisation Studio application produced in this thesis brings together the fields of neuroscience, software engineering and information visualisation to produce a software tool that meets these criteria. A visual programming language for neuroscience is produced that allows for extensive pre-processing of spike train data prior to visualisation. The computational challenges of analysing thousands of spike trains are addressed using parallel processing to fully exploit the modern researcher’s computer hardware. In the case of the computationally intensive pairwise cross-correlation analysis the option to use a high performance compute cluster (HPC) is seamlessly provided. Finally the principles of information visualisation are applied to key visualisations in neuroscience so that the researcher can effectively manage and visually explore the resulting data sets. The final visualisations can typically represent data sets 10 times larger than previously while remaining highly interactive.