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Marwan Abdellah
For embarrassingly parallel algorithms, a Graphics Processing Unit (GPU) outperforms a traditional CPU on price-per-flop and price-per-watt by at least one order of magnitude. This had led to the mapping of signal and image processing algorithms, and consequently their applications, to run entirely on GPUs. This paper presents CUFFTSHIFT, a ready-to-use GPU-accelerated library, that implements […]
Piotr Przymus
In recent years, processing and exploration of time series has experienced a noticeable interest. Growing volumes of data and needs of efficient processing pushed the research in new directions, including hardware based solutions. Graphics Processing Units (GPU) have significantly more applications than just rendering images. They are also used in general purpose computing to solve […]
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Yunpeng Cao
To monitor bad information spreading in microblog system, large-scale data from microblog must be processed in real time. This needs high cost-effective parallel schemes. A parallel processing method on GPUs was put forward to monitor massive microblog. The proposed scheme can fully exploit the GPU feature to schedule massive threads for data-intensive tasks. The detailed […]
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Chang Won Lee, Tae-Young Choe
Although integral histogram enables histogram computation of a sub-area within constant time, construction of the integral histogram requires O(nm) steps for n x m sized image. Such construction time can be reduced using parallel prefix sum algorithm. Mark Harris proposed an efficient parallel prefix sum and implemented it using CUDA GPGPU. Mark Harris’ algorithm has […]
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Felix Weninger, Johannes Bergmann, Bjorn Schuller
In this article, we introduce CURRENNT, an open-source parallel implementation of deep recurrent neural networks (RNNs) supporting graphics processing units (GPUs) through NVIDIA’s Computed Unified Device Architecture (CUDA). CURRENNT supports uni- and bidirectional RNNs with Long Short-Term Memory (LSTM) memory cells which overcome the vanishing gradient problem. To our knowledge, CURRENNT is the first publicly […]
Jing Wu
An emerging trend in processor architecture seems to indicate the doubling of the number of cores per chip every two years with same or decreased clock speed. Of particular interest to this thesis is the class of many-core processors, which are becoming more attractive due to their high performance, low cost, and low power consumption. […]
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Moritz Kreutzer, Georg Hager, Gerhard Wellein, Andreas Pieper, Andreas Alvermann, Holger Fehske
The Kernel Polynomial Method (KPM) is a well-established scheme in quantum physics and quantum chemistry to determine the eigenvalue density and spectral properties of large sparse matrices. In this work we demonstrate the high optimization potential and feasibility of peta-scale heterogeneous CPU-GPU implementations of the KPM. At the node level we show that it is […]
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Bruce Merry
Sorting and scanning are two fundamental primitives for constructing highly parallel algorithms. A number of libraries now provide implementations of these primitives for GPUs, but there is relatively little information about the performance of these implementations. We benchmark seven libraries for 32-bit integer scan and sort, and sorting 32-bit values by 32-bit integer keys.We show […]
M. P. Wachowiak, B. B. Sarlo, A. E. Lambe Foster
Much work has recently been reported in parallel GPU-based particle swarm optimization (PSO). Motivated by the encouraging results of these investigations, while also recognizing the limitations of GPU-based methods for big problems using a large amount of data, this paper explores the efficacy of employing other types of parallel hardware for PSO. Most commodity systems […]
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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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Amrit Panda
Stream processing has emerged as an important model of computation especially in the context of multimedia and communication sub-systems of embedded System-on-Chip (SoC) architectures. The dataflow nature of streaming applications allows them to be most naturally expressed as a set of kernels iteratively operating on continuous streams of data. The kernels are computationally intensive and […]
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Ilker Gurcan
Tracking objects in a video stream is an important problem in robot learning (learning an object’s visual features from different perspectives as it moves, rotates, scales, and is subjected to some morphological changes such as erosion), defense, public security and many other various domains. In this thesis, we focus on a recently proposed tracking framework […]
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