Efficient Partitioning Based Hierarchical Agglomerative Clustering Using Graphics Accelerators With Cuda

Abstract

We 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. Our focus is to significantly reduce time and memory bottlenecks of the traditional HAC algorithm by parallelization and acceleration of computations without com promising the accuracy of clusters. We use partially overlapping partitions (PoP) to parallelize the HAC algorithm using the hardware capabilities of GPU with Compute Unified Device Architecture (CUDA). We compare the computational performance of GPU over the CPU and our experiments show that the computational performance of GPU is much faster than the CPU. The traditional HAC and partitioning based HAC are up to 66 times and 442 times faster on the GPU respectively, than the time taken by a CPU for the tra ditional HAC computations. Moreover, the PoP HAC on GPU requires only a fraction of the memory required by the traditional algorithm on the CPU. The novelties in our research includes boosting computational speed while utilizing GPU global memory, identifying minimum distance pair in virtually a single -pass, avoiding the necessity to maintain huge data in memories and complete the entire HAC computation within the GPU .