Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 12 of 12 for “"Data-intensive Computing"”.
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Software and Hardware Support for Data Intensive Computing
In the architectural aspect, we propose a Near-Memory Processor (NMP), a heterogeneous architecture that couples on one chip a commodity microprocessor together with a coprocessor that is designed to run well applications that have poor locality or that require bit manipulations. The coprocessor …
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Optimizing Data-Intensive Computing with Efficient Configuration Tuning
… systems’ environment (e.g., increase in input data size, changes in the allocation of resources). Paradoxically, existing solutions for workload tuning either assume static tuning environment or workloads that are inexpensive to run (i.e. requiring hundreds of execution samples). Recently, …
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Insight Driven Sampling for Interactive Data Intensive Computing
Data Visualization is used to help humans perceive high dimensional data, but it is unable to be applied in real time to data intensive computing applications. Attempts to process and apply traditional information visualization techniques to such applications result in slow or non-responsive …
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Optimizing Timeliness, Accuracy, and Cost in Geo-Distributed Data-Intensive Computing Systems
Big Data touches every aspect of our lives, from the way we spend our free time to the way we make scientific discoveries. Netflix streamed more than 42 billion hours of video in 2015, and in the process recorded massive volumes of data to inform video recommendations and plan investments in new …
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Reliable service chain orchestration for scalable data-intensive computing at infrastructure edges
… video analytics demands massive imagery/video data 'collection' from Internet-of-Things (IoT) and their seamless 'computation/consumption' within a geo-distributed (edge/core) cloud infrastructure in order to cater to user Quality of Experience (QoE) expectations. Thus, the edge computing needs …
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Scaling simple, compact and extended compact genetic algorithms using MapReduce
Data-intensive computing has emerged as a key player for processing large volumes of data exploiting massive parallelism. Data-intensive computing frameworks have shown that terabytes and petabytes of data can be routinely processed. However, there has been little effort to explore how …
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robustraster - A Python Software Package To Lower the Barrier of Entry for Large-Scale Geospatial Analysis
… witnessed a substantial increase in geospatial data availability and size, predominantly derived from automated high-resolution remote sensing instruments. As our capacity to generate geospatial data continues to expand, we confront challenges in effectively analyzing these datasets due to their …
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An Adaptive Framework for Managing Heterogeneous Many-Core Clusters
The computing needs and the input and result datasets of modern scientific and enterprise applications are growing exponentially. To support such applications, High-Performance Computing (HPC) systems need to employ thousands of cores and innovative data management. At the same time, an emerging …
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Samhita: Virtual Shared Memory for Non-Cache-Coherent Systems
Among the key challenges of computing today are the emergence of many-core architectures and the resulting need to effectively exploit explicit parallelism. Indeed, programmers are striving to exploit parallelism across virtually all platforms and application domains. The shared memory programming …
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Lab-to-Fab Monolithic 3D Integrated Carbon Nanotube Transistors: Scaling and Reliability
… improvements that keep pace with the increasing computing demands of abundant-data applications. Moreover, for data intensive computing applications, a majority of system energy is consumed moving data between compute and off-chip memory, which are often physically separate with limited …
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Relational Computing Using HPC Resources: Services and Optimizations
… analysing and managing large volumes of data. Such massive datasets cannot be handled efficiently by using traditional standalone database management systems, owing to their limitation in the degree of computational efficiency and bandwidth to scale to large volumes of data. In this …