<p>Combined execution times in s for serial and parallel implementations of k-nearest neighbor and range search as a function of input size (number of data chunks). Execution times were measured for the serial implementation running on a CPU (black) and for our parallel implementation using one of three GPU devices (blue, red, green) of varying computing power. Computation using a GPU was considerably faster than using a CPU (by factors 22, 33 and 50 respectively).</p
Multiple independent runs of an evolutionary algorithm in parallel are often used to increase the ef...
<p>Clustering can be considered the most important unsupervised learning<br>technique. Clustering is...
<p>Figure (a) and (b) show the computation times observed for different values of <i>k</i> (with a f...
High-performance computing is one of the most demanding technologies in today\u27s computational wor...
<p>Time performance of analyzing the GPU-accelerated method developed in this study versus a CPU-bas...
<p>The performance showed in terms of running times (in minutes) and speed-ups (x), on four differen...
Several organizations have large databases which are growing at a rapid rate day by day, which need ...
Data analyze has become very important with growth of information today. There is a need of real-tim...
In this age, a huge amount of data is generated every day by human interactions with services. Disco...
Many tasks in data mining and statistics are inherently parallel. While modern commodity desktop pro...
applications, the main time-consuming process is string matching due to the large size of lexicon. I...
Parallel programming is about performance, for otherwise we’d write a sequential program. A problem ...
Multiple independent runs of an evolutionary algorithm in parallel are often used to increase the ef...
Multiple independent runs of an evolutionary algorithm in parallel are often used to increase the ef...
Graphic processors are becoming faster and faster. Computational power within graphic processing uni...
Multiple independent runs of an evolutionary algorithm in parallel are often used to increase the ef...
<p>Clustering can be considered the most important unsupervised learning<br>technique. Clustering is...
<p>Figure (a) and (b) show the computation times observed for different values of <i>k</i> (with a f...
High-performance computing is one of the most demanding technologies in today\u27s computational wor...
<p>Time performance of analyzing the GPU-accelerated method developed in this study versus a CPU-bas...
<p>The performance showed in terms of running times (in minutes) and speed-ups (x), on four differen...
Several organizations have large databases which are growing at a rapid rate day by day, which need ...
Data analyze has become very important with growth of information today. There is a need of real-tim...
In this age, a huge amount of data is generated every day by human interactions with services. Disco...
Many tasks in data mining and statistics are inherently parallel. While modern commodity desktop pro...
applications, the main time-consuming process is string matching due to the large size of lexicon. I...
Parallel programming is about performance, for otherwise we’d write a sequential program. A problem ...
Multiple independent runs of an evolutionary algorithm in parallel are often used to increase the ef...
Multiple independent runs of an evolutionary algorithm in parallel are often used to increase the ef...
Graphic processors are becoming faster and faster. Computational power within graphic processing uni...
Multiple independent runs of an evolutionary algorithm in parallel are often used to increase the ef...
<p>Clustering can be considered the most important unsupervised learning<br>technique. Clustering is...
<p>Figure (a) and (b) show the computation times observed for different values of <i>k</i> (with a f...