The k- means algorithm is a widely used Machine learning algorithm for clustering. This paper introduces a parallel k- means algorithm implementation for image classification. We implemented several parallel programs ...
详细信息
ISBN:
(纸本)9798350349153;9798350349160
The k- means algorithm is a widely used Machine learning algorithm for clustering. This paper introduces a parallel k- means algorithm implementation for image classification. We implemented several parallel programs using OpenMP and MPI libraries and tested them on three MNIST variants datasets: MNIST, Fashion MNIST, and Extended MNIST. The algorithm parameters and scheduling methods are varied during experiments. The implementations use both libraries, run on multiple computers, and utilize all available processor cores. The proposed implementations achieve a speedup of up to 10.2 while using 12 processor cores.
In this paper, we present CGMgraph, the first integrated library of parallel graph methods for PC clusters based on Coarse Grained Multicomputer (CGM) algorithms. CGMgraph implements parallel methods for various graph...
详细信息
In this paper, we present CGMgraph, the first integrated library of parallel graph methods for PC clusters based on Coarse Grained Multicomputer (CGM) algorithms. CGMgraph implements parallel methods for various graph problems. Our implementations of deterministic list ranking, Euler tour, connected components, spanning forest, and bipartite graph detection are, to our knowledge, the first efficient implementations for PC clusters. Our library also includes CGMlib, a library of basic CGM tools such as sorting, prefix sum, one-to-all broadcast, all-to-one gather, h-Relation, all-to-all broadcast, array balancing, and CGM partitioning. Both libraries are available for download at http: //www. scs. carleton. ca/similar tocgm. In the experimental part of this paper, we demonstrate the performance of our methods on four different architectures: a gigabit connected high performance PC cluster, a smaller PC cluster connected via fast ethernet, a network of workstations, and a shared memory machine. Our experiments show that our library provides good parallel speedup and scalability on all four platforms. The communication overhead is, in most cases, small and does not grow significantly with an increasing number of processors. This is a very important feature of CGM algorithms which makes them very efficient in practice.
暂无评论