Several organizations have large databases which are growing at a rapid rate day by day, which need to be regularly maintained. Content based searches are similar searched based on certain features that are obtained from various multi media data. For various applications like multimedia content retrieval, data mining, pattern recognition, etc., performing the nearest neighbor search is a challenging task in multidimensional data. The important factors in nearest neighbor search kNN are searching speed and accuracy. Implementation of kNN on GPU is an ongoing research from last few years, focusing on improving the performance of kNN. By considering these aspects, our research has been started and found a gap in this research area. This master thesis shows effective and efficient parallelism on multi-core of CPU and GPU to compare the performance with single core CPU. This paper shows an experimental implementation of kNN on single core CPU, Mutli-core CPU and GPU using C, Pthreads and CUDA respectively. We considered different levels of inputs (size, dimensions) to evaluate the performance. The experiment shows the GPU outperforms for kNN  when compared to CPU single core with a factor of approximately 5.8 to 16 and CPU multi-core with a factor of approximately 1.2 to 3 for different levels of inputs.

The emergence of scientific applications embedded with multiple modes of parallelism has made heterogeneous computing systems indispensable in high performance computing. The popularity of such systems is evident from the fact that three out of the top five fastest supercomputers in the world employ heterogeneous computing, i.e., they use dissimilar computational units. A closer look at the performance of these supercomputers reveals that they achieve only around 50% of their theoretical peak performance. This suggests that applications that were tuned for erstwhile homogeneous computing may not be efficient for today's heterogeneous computing and hence, novel optimization strategies are required to be exercised. However, optimizing an application for heterogeneous computing systems is extremely challenging, primarily due to the architectural differences in computational units in such systems. This thesis intends to act as a cookbook for optimizing applications on heterogeneous computing systems that employ graphics processing units (GPUs) as the preferred mode of accelerators. We discuss optimization strategies for multicore CPUs as well as for the two popular GPU platforms, i.e., GPUs from AMD and NVIDIA. Optimization strategies for NVIDIA GPUs have been well studied but when applied on AMD GPUs, they fail to measurably improve performance because of the differences in underlying architecture. To the best of our knowledge, this research is the first to propose optimization strategies for AMD GPUs. Even on NVIDIA GPUs, there exists a lesser known but an extremely severe performance pitfall called partition camping, which can affect application performance by up to seven-fold. To facilitate the detection of this phenomenon, we have developed a performance prediction model that analyzes and characterizes the effect of partition camping in GPU applications. We have used a large-scale, molecular modeling application to validate and verify all the optimization strategies. Our results illustrate that if appropriately optimized, AMD and NVIDIA GPUs can provide 371-fold and 328-fold improvement, respectively, over a hand-tuned, SSE-optimized serial implementation. / Master of Science

Telecom companies do a lot of analytics to provide consumers a better service and to stay in competition. These companies accumulate special big data that has potential to provide inputs for business. Query processing is one of the major tool to fire analytics at their data. Traditional query processing techniques which follow in-memory algorithm cannot cope up with the large amount of data of telecom operators. The k nearest neighbour technique(kNN) is best suitable method for classification and regression of large datasets. Our research is focussed on implementation of kNN as query processing algorithm and evaluate the performance of it on large datasets using single core, multi-core and on GPU. This thesis shows an experimental implementation of kNN query processing on single core CPU, Multicore CPU and GPU using Python, P- threads and CUDA respectively. We considered different levels of sizes, dimensions and k as inputs to evaluate the performance. The experiment shows that GPU performs better than CPU single core on the order of 1.4 to 3 times and CPU multi-core on the order of 5.8 to 16 times for different levels of inputs.

<p>A large volume of data is continuously being generated by data centers, humans, and the internet of things (IoT). In order to get useful insights, such enormous data must be processed in time with high throughput, low latency, and high accuracy. To meet such performance demands, a large body of new hardware is being shipped by vendors, such as multi-core CPUs, 3D-stacked memory, embedded microcontrollers, and other accelerators.</p><br><p>However, traditional operating systems (OSes) and data analytics frameworks, the key layer that bridges high-level data processing applications and low-level hardware, fails to deliver these requirements due to quickly evolving new hardware and increases in explosion of data. For instance, general OSes are not aware of the unique characters and demands of data processing applications. Data analytics engines for stream processing, e.g., Apache Spark and Beam, always add more machines to deal with more data but leave every single machine underutilized without fully exploiting underlying hardware features, which leads to poor efficiency. Data analytics frameworks for machine learning inference on IoT devices cannot run neural networks that exceed SRAM size, which disqualifies many important use cases.</p><br><p>In order to bridge the gap between the performance demands of data analytics and the new features of emerging hardware, in this thesis we exploit runtime system designs for high-level data processing applications by exploiting low-level modern hardware features. We study two important data analytics applications, including real-time stream processing and on-device machine learning inference, on three important hardware platforms across the Cloud and the Edge, including multicore CPUs, hybrid memory system combining 3D-stacked memory and general DRAM, and embedded microcontrollers with limited resources. </p><br><p>In order to speed up and enable the two data analytics applications on the three hardware platforms, this thesis contributes three related research projects. In project StreamBox, we exploit the parallelism and memory hierarchy of modern multicore hardware on single machines for stream processing, achieving scalable and highly efficient performance. In project StreamBox-HBM, we exploit hybrid memories to balance bandwidth and latency, achieving memory scalability and highly efficient performance. StreamBox and StreamBox-HBM both offer orders of magnitude performance improvements over the prior state of the art, opening up new applications with higher data processing needs. In project SwapNN, we investigate a system solution for microcontrollers (MCUs) to execute neural networks (NNs) inference out-of-core without losing accuracy, enabling new use cases and significantly expanding the scope of NN inference on tiny MCUs. </p><br><p>We report the system designs, system implementations, and experimental results. Based on our experience in building above systems, we provide general guidance on designing runtime systems across hardware/software stack for a wider range of new applications on future hardware platforms.</p><div><br></div>

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