Results 281 to 290 of about 16,671 (310)

GPU accelerated dislocation dynamics

Journal of Computational Physics, 2014
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Francesco Ferroni, Edmund Tarleton, Steven Fitzgerald   +2 more
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On GPU’s viability as a middleware accelerator

Cluster Computing, 2009
Today Graphics Processing Units (GPUs) are a largely underexploited resource on existing desktops and a possible cost-effective enhancement to high-performance systems. To date, most applications that exploit GPUs are specialized scientific applications.
Samer Al-Kiswany, Abdullah Gharaibeh, Elizeu Santos-Neto, Matei Ripeanu   +3 more
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GPU-accelerated path rendering

ACM Transactions on Graphics, 2012
For thirty years, resolution-independent 2D standards (e.g. PostScript, SVG) have depended on CPU-based algorithms for the filling and stroking of paths. Advances in graphics hardware have largely ignored accelerating resolution-independent 2D graphics rendered from paths.
Mark J. Kilgard, Jeff Bolz
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Accelerating Matrix Processing with GPUs

2017 IEEE 24th Symposium on Computer Arithmetic (ARITH), 2017
Matrix operations are common and expensive computations in a variety of applications. They occur frequently in high-performance computing, graphics, graph processing, and machine learning applications. This paper discusses how to map a variety of important matrix computations, including sparse matrix-vector multiplication (SpMV), sparse triangle solve (
Nicholas Malaya, Shuai Che, Joseph L. Greathouse, René van Oostrum, Michael J. Schulte   +4 more
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A survey of GPU accelerated SVM

Proceedings of the 2014 ACM Southeast Regional Conference, 2014
Support Vector Machines (SVM) is a set of machine learning algorithms that have been widely used in diverse domains. As the volume of data generated by humans and machines increases year by year, the traditional training algorithms for SVM become infeasible for large scale datasets. Mathematical optimization approaches and computing parallel techniques
Yunmei Lu, Yun Zhu, Meng Han, Jing (Selena) He, Yanqing Zhang 0001   +4 more
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GPU Accelerated Genetic Clustering

2012
Genetic and evolutionary algorithms have been used to find clusters in data with success. Unfortunately, evolutionary clustering suffers from the high computational costs when it comes to fitness function evaluation. The GPU computing is a recent programming and development paradigm introducing high performance parallel computing to general audience ...
Pavel Krömer, Jan Platos, Václav Snásel   +2 more
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A GPU accelerated storage system

Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing, 2010
Massively multicore processors, like, for example, Graphics Processing Units (GPUs), provide, at a comparable price, a one order of magnitude higher peak performance than traditional CPUs. This drop in the cost of computation, as any order-of-magnitude drop in the cost per unit of performance for a class of system components, triggers the opportunity ...
Abdullah Gharaibeh, Samer Al-Kiswany, Sathish Gopalakrishnan, Matei Ripeanu   +3 more
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GPU Acceleration of the caffa3d.MB Model

2012
This work presents a study of porting Strongly Implicit Procedure (SIP) solver to GPU in order to improve its computational efficiency. The SIP heptadiagonal linear system solver was evaluated to be the most time consuming stage in finite volume flow solver caffa3d.MB .
Pablo Igounet, Pablo Alfaro, Gabriel Usera, Pablo Ezzatti   +3 more
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Accelerating Secret Sharing on GPU

2019
A (k, n) threshold secret sharing scheme encrypts a secret s into n parts (called shares), which are distributed to n participants, such that any k participants can recover s using their shares, any group of less than k ones cannot. A robust threshold sharing scheme provides not only the perfect security, but also the tolerance of a possible loss of up
Shyong Jian Shyu, Ying Zhen Tsai
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Accelerating the Kalman Filter on a GPU

2011 IEEE 17th International Conference on Parallel and Distributed Systems, 2011
For linear dynamic systems with hidden states, the Kalman filter can estimate the system state and its error covariance considering the uncertainties in transition and observation models. In each iteration of applying the Kalman filter, the two phases of predict and update contain a total of 18 matrix operations which include addition, subtraction ...
Min-Yu Huang, Shih-Chieh Wei, Bormin Huang, Yang-Lang Chang   +3 more
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