Compute pairwise manhattan distance and pearson correlation coefficient of data points with gpu (pdf download available)

[Show abstract] [Hide abstract] ABSTRACT: Algorithmic skeletons simplify software development: they abstract typical patterns of parallelism and provide their efficient implementations, allowing the application developer to focus on the structure of algorithms, rather than on implementation details. Data recovery hardware tools This becomes especially important for modern parallel systems with multiple graphics processing units (GPUs) whose programming is complex and error-prone, because state-of-the-art programming approaches like CUDA and OpenCL lack high-level abstractions. Os x data recovery We define a new algorithmic skeleton for allpairs computations which occur in real-world applications, ranging from bioinformatics to physics. R studio data recovery download We develop the skeleton’s generic parallel implementation for multi-GPU Systems in OpenCL.


Data recovery western digital To enable the automatic use of the fast GPU memory, we identify and implement an optimized version of the allpairs skeleton with a customizing function that follows a certain memory access pattern. Top 5 data recovery software We use matrix multiplication as an application study for the allpairs skeleton and its two implementations and demonstrate that the skeleton greatly simplifies programming, saving up to 90 % of lines of code as compared to OpenCL. Data recovery mac The performance of our optimized implementation is up to 6.8 times higher as compared with the generic implementation and is competitive to the performance of a manually written optimized OpenCL code.

[Show abstract] [Hide abstract] ABSTRACT: We explore the capabilities of today’s high-end Graphics processing units (GPU) on desktop computers toefficientlyperform hierarchical agglomerative clustering (HAC) through partitioning of gene expressions.Our focus is to significantly reduce time and memory bottlenecks of the traditional HAC algorithm byparallelization and acceleration of computations without compromising the accuracy of clusters. Database website We usepartially overlapping partitions (PoP) to parallelize the HAC algorithm using the hardware capabilities ofGPU with Compute Unified Device Architecture (CUDA). Data recovery denver We compare the computational performance ofGPU overthe CPU and our experiments show that the computational performance of GPU is much fasterthan the CPU. Data recovery galaxy s4 The traditional HAC and partitioning based HAC are up to 66 times and 442 times faster onthe GPU respectively, than the time taken by a CPU for the traditional HAC computations. R studio data recovery free full version Moreover, thePoP HAC on GPU requires only a fraction of the memory required by the traditional algorithm on theCPU. Data recovery equipment The novelties in our research includes boosting computational speed while utilizing GPU globalmemory, identifying minimum distance pair in virtually a single-pass, avoiding the necessity to maintainhuge data in memories and complete the entire HAC computation within the GPU.

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