a<p>Convergence steps: the iteration steps when the algorithm is converged.</p>b<p>Cluster Number: the number of clusters which the AP algorithm detects.</p>c<p>Errot rates: the 2-norm of the difference value of message matrices from two algorithms.</p>d<p>Preference value: the input value of AP algorithm.</p>e<p>Non-Parallel algorithm: the non-parallel version of algorithm which get from the original publication of AP algorithm.</p>f<p>Parallel algorithm: the parallel version of algorithm.</p>g<p>Responsibility message: the responsibility message of AP algorithm.</p>h<p>Availability message: the availability message of AP algorithm.</p
The experiment\u27s result shows the average error rate of that the REP algorithm will produce the s...
: The parallel implementation of the Least Mean Square (LMS) and Recursive Least Square (RLS) adapt...
Modern high performance computing is dependent on parallel processing systems. Most current benchmar...
He number of compute nodes used in the analysis. The bar graphs at the bottom of each plot illustrat...
Parallel efficiency comparison in the algorithm in this paper and DP K-means.</p
The k-means algorithm is a widely used clustering tech-nique. Here we will examine the performance o...
This session explores, through the use of formal methods, the “intuition” used in creating a paralle...
This paper discusses a scalability metric based on the cost effectiveness of parallel algorithms. Un...
HPC applications are often very complex and their behavior depends on a wide range of factors from a...
<p>Convergence speed and algorithm reliability comparisons on Examples 1–6 with noise perturbation; ...
Master's thesis in Computer ScienceK-means is the most commonly known partitioning algorithm used fo...
High Performance Computing and Supercomputing is the high end area of the computing science that stu...
The article presents a comparative analysis of the implementation of parallel algorithms on the cent...
Many parallel algorithm design models have been proposed for abstracting a large class of parallel a...
this paper argues that a useful metric for parallel algorithm analysis should be consistent, quantit...
The experiment\u27s result shows the average error rate of that the REP algorithm will produce the s...
: The parallel implementation of the Least Mean Square (LMS) and Recursive Least Square (RLS) adapt...
Modern high performance computing is dependent on parallel processing systems. Most current benchmar...
He number of compute nodes used in the analysis. The bar graphs at the bottom of each plot illustrat...
Parallel efficiency comparison in the algorithm in this paper and DP K-means.</p
The k-means algorithm is a widely used clustering tech-nique. Here we will examine the performance o...
This session explores, through the use of formal methods, the “intuition” used in creating a paralle...
This paper discusses a scalability metric based on the cost effectiveness of parallel algorithms. Un...
HPC applications are often very complex and their behavior depends on a wide range of factors from a...
<p>Convergence speed and algorithm reliability comparisons on Examples 1–6 with noise perturbation; ...
Master's thesis in Computer ScienceK-means is the most commonly known partitioning algorithm used fo...
High Performance Computing and Supercomputing is the high end area of the computing science that stu...
The article presents a comparative analysis of the implementation of parallel algorithms on the cent...
Many parallel algorithm design models have been proposed for abstracting a large class of parallel a...
this paper argues that a useful metric for parallel algorithm analysis should be consistent, quantit...
The experiment\u27s result shows the average error rate of that the REP algorithm will produce the s...
: The parallel implementation of the Least Mean Square (LMS) and Recursive Least Square (RLS) adapt...
Modern high performance computing is dependent on parallel processing systems. Most current benchmar...