Lerato J. Mohapi, Simon Winberg, Michael Inggs
In this paper, we present a domain-specific language, referred to as OptiSDR, that matches high level digital signal processing (DSP) routines for software defined radio (SDR) to their generic parallel executable patterns targeted to heterogeneous computing architectures (HCAs). These HCAs includes a combination of hybrid GPU-CPU and DSP-FPGA architectures that are programmed using different programming […]
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Fadel Adoe, Yingke Chen, Prashant Doshi
Planning under uncertainty in multiagent settings is highly intractable because of history and plan space complexities. Probabilistic graphical models exploit the structure of the problem domain to mitigate the computational burden. In this paper, we introduce the first parallelization of planning in multiagent settings on a CPU-GPU heterogeneous system. In particular, we focus on the […]
Rutuja U. Gosavi, Payal S. Kulkarni
Game tree search is a classical problem in the field of game theory and artificial intelligence. Focus of the system is on how to leverage massive parallelism capabilities of GPUs to accelerate the speed of game tree algorithms and propose a concise and general parallel game tree algorithm on GPUs. Comparison can be done for […]
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Manish Pandey, Sanjay Sharma
All-pairs shortest path problem(APSP) finds a large number of practical applications in real world. We owe to present a highly parallel and recursive solution for solving APSP problem based on Kleene’s algorithm. The proposed parallel approach for APSP is implemented using an open standard framework OpenCL which provides a development environment for utilizing massive parallel […]
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Farouk Mansouri, Sylvain Huet, Dominique Houzet
The biomedical imagery, the numeric communications, the acoustic signal processing and many others digital signal processing applications (DSP) are present more and more everyday in the numeric world. They process growing data volume which is represented with more and more accuracy, and using complex algorithms with time constraints to satisfying. Consequently, a high requirement of […]
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Jihye Kwon, Kang-Wook Kim, Sangyoun Paik, Jihwa Lee, Chang-Gun Lee
Past researches on multicore scheduling assume that a computational unit has already been parallelized into a prefixed number of threads. However, with recent technologies such as OpenCL, a computational unit can be parallelized in many different ways with runtime selectable numbers of threads. This paper proposes an optimal algorithm for parallelizing and scheduling a set […]
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Paul Irofti
Dictionary training for sparse representations involves dealing with large chunks of data and complex algorithms that determine time consuming implementations. SBO is an iterative dictionary learning algorithm based on constructing unions of orthonormal bases via singular value decomposition, that represents each data item through a single best fit orthobase. In this paper we present a […]
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Hongsheng Li, Rui Zhao, Xiaogang Wang
We present highly efficient algorithms for performing forward and backward propagation of Convolutional Neural Network (CNN) for pixelwise classification on images. For pixelwise classification tasks, such as image segmentation and object detection, surrounding image patches are fed into CNN for predicting the classes of centered pixels via forward propagation and for updating CNN parameters via […]
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Cain Cresswell-Miley
Training classifiers can be seen as an optimization problem. With this view, we have developed a method to train a type of nearest centroid classifier with PSO. Results showed an improvement on most of the datasets tested. Additionally, we have developed a method to utilize the developed classifier with datasets containing both numeric and categorical […]
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Wenhao Jia
In response to the ever growing demand for computing power, heterogeneous parallelism has emerged as a widespread computing paradigm in the past decade or so. In particular, massively parallel processors such as graphics processing units (GPUs) have become the prevalent throughput computing elements in heterogeneous systems, offering high performance and power efficiency for general-purpose workloads. […]
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Jae-Hyun Seo, Eun-Sol Ko, Yong-Hyuk Kim
Generally genetic algorithm (GA) has disadvantage of taking a lot of computation time, and it is worth reducing the execution time while keeping good quality and result. Comparative experiments are conducted with one CPU and four GPUs using CUDA (Compute Unified Device Architecture) and generational GA. We implement the fitness functions of the GA which […]
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Jose Ritomar Carneiro Torquato, Esteban Walter Gonzalez Clua
Graphics Processing Units have been created with the objective of accelerating the construction and processing of graphic images. In its historical evolution line, concerned with the large computational capacity inherent, these devices started to be used for general purposes. However, the design of the GPUs don’t work well with divergent algorithms, mainly conditionals and repetitions. […]
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