has been widely utilized in many applications, such as machine learning, image pattern recognition, and compressed sensing. However, the RVM algorithm is computationally expensive. We seek to accelerate the RVM algorithm computation for time sensi-tive applications by utilizing massively parallel accelerators such as GPUs. In this paper, the computation procedure of the RVM algorithm is fully analyzed. Recursive Cholesky decomposition, the key step in the RVM algorithm, is implemented on GPUs. The GPU performance is compared with a CPU using LAPACK and a hybrid system using the MAGMA library. Results show that our GPU implementation in both single and double precision is approximately 4 times faster than the CPU using LAPACK and faster than...
The computing power of current Graphical Processing Units (GPUs) has increased rapidly over the year...
\u3cp\u3eThe support vector machine (SVM) is a supervised learning algorithm used for recognizing pa...
General purpose graphical processing units were proven to be useful for accelerating computationally...
Recent developments in programmable, highly par-allel Graphics Processing Units (GPUs) have enabled ...
Part 2: Parallel and Multi-Core TechnologiesInternational audienceLinear RankSVM is one of the widel...
Huge image collections are becoming available lately. In this scenario, the use of Content-Based Ima...
Abstract—Currently, state of the art libraries, like MAGMA, focus on very large linear algebra probl...
General purpose programming on the graphics processing units (GPGPU) has received a lot of attention...
[[abstract]]Graphics processing units (GPUs) have attracted a lot of attention due to their cost-eff...
For appropriate matrix ensembles, greedy algorithms have proven to be an efficient means of solving ...
This paper presents a GPU-assisted version of the LIBSVM library for Support Vector Machines. SVMs a...
International audienceWe are interested in the intensive use of Factorial Correspondence Analysis (F...
Abstract. Robust Point Matching (RPM) is a common image registration algo-rithm, yet its large compu...
The Fast Multipole Method allows the rapid evaluation of sums of radial basis functions centered at ...
Latent Semantic Analysis (LSA) aims to reduce the dimensions of large term-document datasets using S...
The computing power of current Graphical Processing Units (GPUs) has increased rapidly over the year...
\u3cp\u3eThe support vector machine (SVM) is a supervised learning algorithm used for recognizing pa...
General purpose graphical processing units were proven to be useful for accelerating computationally...
Recent developments in programmable, highly par-allel Graphics Processing Units (GPUs) have enabled ...
Part 2: Parallel and Multi-Core TechnologiesInternational audienceLinear RankSVM is one of the widel...
Huge image collections are becoming available lately. In this scenario, the use of Content-Based Ima...
Abstract—Currently, state of the art libraries, like MAGMA, focus on very large linear algebra probl...
General purpose programming on the graphics processing units (GPGPU) has received a lot of attention...
[[abstract]]Graphics processing units (GPUs) have attracted a lot of attention due to their cost-eff...
For appropriate matrix ensembles, greedy algorithms have proven to be an efficient means of solving ...
This paper presents a GPU-assisted version of the LIBSVM library for Support Vector Machines. SVMs a...
International audienceWe are interested in the intensive use of Factorial Correspondence Analysis (F...
Abstract. Robust Point Matching (RPM) is a common image registration algo-rithm, yet its large compu...
The Fast Multipole Method allows the rapid evaluation of sums of radial basis functions centered at ...
Latent Semantic Analysis (LSA) aims to reduce the dimensions of large term-document datasets using S...
The computing power of current Graphical Processing Units (GPUs) has increased rapidly over the year...
\u3cp\u3eThe support vector machine (SVM) is a supervised learning algorithm used for recognizing pa...
General purpose graphical processing units were proven to be useful for accelerating computationally...