Efficient Partitioning Based Hierarchical Agglomerative Clustering Using Graphics Accelerators With CUDA

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Provided by: Academy & Industry Research Collaboration Center
Topic: Data Management
Format: PDF
The authors explore the capabilities of today's high-end Graphics Processing Units (GPU) on desktop computers to efficiently perform Hierarchical Agglomerative Clustering (HAC) through partitioning of gene expressions. Their focus is to significantly reduce time and memory bottlenecks of the traditional HAC algorithm by parallelization and acceleration of computations without compromising the accuracy of clusters. They use Partially overlapping Partitions (PoP) to parallelize the HAC algorithm using the hardware capabilities of GPU with Compute Unified Device Architecture (CUDA).
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