Survey on Parallel Computing and Performance Modelling in High Performance Computing

The parallel programming come a long way with the advances in the HPC. The high performance computing landscape is shifting from collections of homogeneous nodes towards heterogeneous systems, in which nodes consist of a combination of traditional out-of-order execution cores and accelerator devices. Accelerators, built around GPUs, many-core chips, FPGAs or DSPs, are used to offload compute-intensive tasks. Large-scale GPU clusters are gaining popularity in the scientific computing community and having massive range of applications. However, their deployment and production use are associated with a number of new challenges including CUDA. In this paper, we present our efforts to address some of the issues related to HPC and also introduced some performance modelling techniques along with GPU clustering. DOI: 10.17762/ijritcc2321-8169.15029

REAL-TIME DATA MINING FOR PROCESS OPERATIONS USING GRAPHICS PROCESSING UNIT (GPU)-BASED HIGH PERFORMANCE COMPUTING

Ph.DDOCTOR OF PHILOSOPH

Towards real-time geodemographic information systems: design, analysis and evaluation

Geodemographic classifications provide discrete indicators of the social, economic and demographic characteristics of people living in small neighbourhood areas. They have been regarded as products, which are the final 'best' outcome that can be achieved using available data and algorithms. However, reduction in the cost of geocomputation, increased network bandwidths and increasingly accessible spatial data infrastructures have together created the potential for the creation of classifications in near real-time within distributed environments. Current geodemographic classifications are said to be 'closed' in nature due to the data and algorithms used. This thesis is a step towards an open geodemographic information system that allows users to specify the importance of their selected variables and then perform a range of statistical analysis functions which are necessary to create classifications tailored to user requirements. This thesis discusses the socio-economic data sources currently used in the creation of geodemographic classifications, and explains the work towards the creation of a non-conventional data sources arising out of the UCL's surname database. Such data sources are seen as key to the creation of tailor made classifications. The thesis explains and compares different cluster analysis techniques for the segmentation of geodemographic classifications. The development of an online information system employs an optimisation of k-means clustering algorithm. This optimisation uses CUDA (Computer Unified Development Architecture) for parallel processing of computationally expensive k-means on NVIDIA's graphics cards. The concluding chapters of the thesis set out the architecture of a real-time geodemographic information system. The thesis also presents the results of the creation of bespoke local area classifications. The developmental work culminates in a pilot real-time geodemographic information system for the specification, estimation and testing of classifications on the fly