Linchuan Chen
Because of the bottleneck in the increase of clock frequency, multi-cores emerged as a way of improving the overall performance of CPUs. In the recent decade, many-cores begin to play a more and more important role in scientific computing. The highly cost-effective nature of many-cores makes them extremely suitable for data-intensive computations. Specifically, many-cores are […]
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Jie Wang, Yanshuo Yu, Hang Cui, Shenglai Yang
GPU programming model for general purpose computing is complex and difficult to be maintained. A MapReduce acceleration framework named MRCUDA is designed and implemented in this paper. There are four loosely coupled stages in MRCUDA, including Pre-Processing, Map, Group and Reduce, which can support flexible configurations for different applications. In order to take full advantage […]
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Adam Polak
The clustering coefficient and the transitivity ratio are concepts often used in network analysis, which creates a need for fast practical algorithms for counting triangles in large graphs. Previous research in this area focused on sequential algorithms, MapReduce parallelization, and fast approximations. In this paper we propose a parallel triangle counting algorithm for CUDA GPU. […]
Edgar Isaac Hiroshi Leon Saiki
In this age, a huge amount of data is generated every day by human interactions with services. Discovering the patterns of these data are very important to take business decisions. Due to the size of this data, it requires very high intensive computation power. Thus, many frameworks have been developed using Central Processing Units (CPU) […]
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Qi Lyu, Jun Zhu
Long Short-Term Memory (LSTM) is a deep recurrent neural network architecture with high computational complexity. Contrary to the standard practice to train LSTM online with stochastic gradient descent (SGD) methods, we propose a matrix-based batch learning method for LSTM with full Backpropagation Through Time (BPTT). We further solve the state drifting issues as well as […]
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Xiangyu Li
MapReduce is a programming model capable of processing massive data in parallel across hundreds of computing nodes in a cluster. It hides many of the complicated details of parallel computing and provides a straightforward interface for programmers to adapt their algorithms to improve productivity. Many MapReduce-based applications have utilized the power of this model, including […]
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Ramy Medhat, Yogi Joshi, Borzoo Bonakdarpour, Sebastian Fischmeister
Runtime verification is an effective automated method for specification-based offline testing and analysis as well as online monitoring of complex systems. The specification language is often a variant of regular expressions or a popular temporal logic, such as LTL. This paper presents a novel and efficient parallel algorithm for verifying a more expressive version of […]
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Kato Mivule, Benjamin Harvey, Crystal Cobb, Hoda El Sayed
The advent of high performance computing (HPC) and graphics processing units (GPU), present an enormous computation resource for Large data transactions (big data) that require parallel processing for robust and prompt data analysis. While a number of HPC frameworks have been proposed, parallel programming models present a number of challenges, for instance, how to fully […]
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Hisham Mohamed
This thesis studies the scalability of the similarity search problem in large-scale multidimensional data. Similarity search, translating into the neighbour search problem, finds many applications for information retrieval, visualization, machine learning and data mining. The current exponential growth of data motivates the need for approximate and scalable algorithms. In most of existing algorithms and data-structures, […]
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Jie Zhu, Hai Jiang, Juanjuan Li, Erikson Hardesty, Kuan-Ching Li, Zhongwen Li
As the size of high performance applications increases, four major challenges including heterogeneity, programmability, fault resilience, and energy efficiency have arisen in the underlying distributed systems. To tackle with all of them without sacrificing performance, traditional approaches in resource utilization, task scheduling and programming paradigm should be reconsidered. While Hadoop has handled data-intensive applications well […]
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Aamir Shafi, Aleem Akhtar, Ansar Javed, Bryan Carpenter
This paper presents an overview of the "Applied Parallel Computing" course taught to final year Software Engineering undergraduate students in Spring 2014 at NUST, Pakistan. The main objective of the course was to introduce practical parallel programming tools and techniques for shared and distributed memory concurrent systems. A unique aspect of the course was that […]
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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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