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Oren Segal, Martin Margala, Sai Rahul Chalamalasetti, Mitch Wright
This work presents an effort to bridge the gap between abstract high level programming and OpenCL by extending an existing high level Java programming framework (APARAPI), based on OpenCL, so that it can be used to program FPGAs at a high level of abstraction and increased ease of programmability. We run several real world algorithms […]
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Rui Li, Yue Zheng
To improve the performance of large-scale rendering, it requires not only a good view of data structure, but also less disk and network access, especially for achieving the realistic visual effects. This paper presents an optimization method of global illumination rendering for large datasets. We improved the previous rendering algorithm based on Monte Carlo ray […]
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Khari A. Armih
High performance architectures are increasingly heterogeneous with shared and distributed memory components, and accelerators like GPUs. Programming such architectures is complicated and performance portability is a major issue as the architectures evolve. This thesis explores the potential for algorithmic skeletons integrating a dynamically parametrised static cost model, to deliver portable performance for mostly regular data […]
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Che-Lun Hun, Guan-Jie Hua
With the rapid growth of next generation sequencing technologies, such as Slex, more and more data have been discovered and published. To analysis such huge data the computational performance is an important issue. Recently, many tools, such as SOAP, have been implemented on Hadoop and GPU parallel computing architectures. BLASTP is an important tool, implemented […]
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Jianting Zhang, Simin You, Le Gruenwald
City-wide GPS recorded taxi trip data contains rich information for traffic and travel analysis to facilitate transportation planning and urban studies. However, traditional data management techniques are largely incapable of processing big taxi trip data at the scale of hundreds of millions. In this study, we aim at utilizing the General Purpose computing on Graphics […]
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Ekaterina I. Gonina
Developing efficient parallel implementations and fully utilizing the available resources of parallel platforms is now required for software applications to scale to new generations of processors. Yet, parallel programming remains challenging to programmers due to the requisite low-level knowledge of the underlying hardware and parallel computing constructs. These restrictions in turn impede experimentation with various […]
Benjamin Y. Cho, Won Seob Jeong, Doohwan Oh, Won Woo Ro
Considerable research has been conducted recently on near-data processing techniques as real-world tasks increasingly involve large-scale and high-dimensional data sets. The advent of solid-state drives (SSDs) has spurred further research because of their processing capability and high internal bandwidth. However, the data processing capability of conventional SSD systems have not been impressive. In particular, they […]
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Feng Ji
GPU has become a popular parallel accelerator in modern heterogeneous systems for its great parallelism and superior energy efficiency. However, it also extremely complicates programing the memory system in such heterogeneous systems, due to the non-continuous memory spaces on CPU and GPU, and a two-level memory hierarchy on a GPU itself. The complexity of this […]
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Mengjun Xie, Kyoung-Don Kang, Can Basaran
MapReduce greatly decrease the complexity of developing applications for parallel data processing. To considerably improve the performance of MapReduce applications, we design a new MapReduce framework, called Moim, which 1) effectively utilizes both CPUs and GPUs (general purpose Graphics Processing Units), 2) overlaps CPU and GPU computations, 3) enhances load balancing in the map and […]
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Mian Lu, Lei Zhang, Huynh Phung Huynh, Zhongliang Ong, Yun Liang, Bingsheng He, Rick Siow Mong Goh, Richard Huynh
With the ease-of-programming, flexibility and yet efficiency, MapReduce has become one of the most popular frameworks for building big-data applications. MapReduce was originally designed for distributed-computing, and has been extended to various architectures, e,g, multi-core CPUs, GPUs and FPGAs. In this work, we focus on optimizing the MapReduce framework on Xeon Phi, which is the […]
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Peter Wittek, Sandor Daranyi
Scientific computations have been using GPU-enabled computers successfully, often relying on distributed nodes to overcome the limitations of device memory. Only a handful of text mining applications benefit from such infrastructure. Since the initial steps of text mining are typically data intensive, and the ease of deployment of algorithms is an important factor in developing […]
Lesley Northam, Khuzaima Daudjee, Rob Smits, Joe Istead
We present the Hadoop Online Ray Tracer (HORT), a scalable ray tracing framework for general, pay-as-you-go, cloud computing services. Using MapReduce, HORT partitions the computational workload and scene data differently than other distributed memory ray tracing frameworks. We show that this unique partitioning significantly bounds the data replication costs and inter-process communication. Consequently HORT is […]
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