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We explore the capabilities of today’s high-end Graphics processing units (GPU) on desktop computers to\udefficiently perform hierarchical agglomerative clustering (HAC) through partitioning of gene expressions.\udOur focus is to significantly reduce time and memory bottlenecks of the traditional HAC algorithm by\udparallelization and acceleration of computations without compromising the accuracy of clusters. We use\udpartially overlapping partitions (PoP) to parallelize the HAC algorithm using the hardware capabilities of\udGPU with Compute Unified Device Architecture (CUDA). We compare the computational performance of\udGPU over the CPU and our experiments show that the computational performance of GPU is much faster\udthan the CPU. The traditional HAC and partitioning based HAC are up to 66 times and 442 times faster on\udthe GPU respectively, than the time taken by a CPU for the traditional HAC computations. Moreover, the\udPoP HAC on GPU requires only a fraction of the memory required by the traditional algorithm on the\udCPU. The novelties in our research includes boosting computational speed while utilizing GPU global\udmemory, identifying minimum distance pair in virtually a single-pass, avoiding the necessity to maintain\udhuge data in memories and complete the entire HAC computation within the GPU.\u

Efficient Partitioning Based Hierarchical Agglomerative Clustering Using Graphics Accelerators With Cuda