Parallel Algorithms for Hierarchical Clustering and Cluster Validity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parallel algorithms for hierarchical clustering
Parallel Computing
Linear algebra operators for GPU implementation of numerical algorithms
ACM SIGGRAPH 2003 Papers
Brook for GPUs: stream computing on graphics hardware
ACM SIGGRAPH 2004 Papers
GPU Cluster for High Performance Computing
Proceedings of the 2004 ACM/IEEE conference on Supercomputing
Efficient K-Means Clustering Using Accelerated Graphics Processors
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
An approach for fast hierarchical agglomerative clustering using graphics processors with CUDA
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Dense affinity propagation on clusters of GPUs
PPAM'11 Proceedings of the 9th international conference on Parallel Processing and Applied Mathematics - Volume Part I
Computers in Biology and Medicine
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Hierarchical clustering is becoming a de facto standard for analyzing gene expression data. But high computational complexity limits its application in high throughput processing of massive microarray data. An implementation based on commodity graphics hardware is proposed to accelerate this process by employing the parallelism and programmability in graphics pipeline. Significant acceleration is achieved by careful design and implementations, especially in the distance calculation part. The performance comparison between CPU and GPU implementation gives inspiring results.