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Single Nucleotide Polymorphism (SNP) detection is a fundamental procedure of whole genome analysis. SOAPsnp, a classic tool for detection, would take more than one week to analyze one typical human genome, which limits the efficiency of downstream analyses. In this paper, we present mSNP, an optimized version of SOAPsnp, which leverages Intel Xeon Phi coprocessors for large-scale SNP detection. Firstly, we redesigned the essential data structures of SOAPsnp, which significantly reduces memory footprint and improves computing efficiency. Then we developed a coordinated parallel framework for a higher hardware utilization of both CPU and Xeon Phi. Also, we tailored the data structures and operations to utilize the wide VPU of Xeon Phi to improve data throughput. Last but not the least, we proposeed a read-based window division strategy to improve throughput and obtain better load balance. mSNP is the first SNP detection tool empowered by Xeon Phi. We achieved a 38x single thread speedup on CPU, without any loss in precision. Moreover, mSNP successfully scaled to 4,096 nodes on Tianhe-2. Our experiments demonstrate that mSNP is efficient and scalable for large-scale human genome SNP detection. [ABSTRACT FROM AUTHOR]

In many scenarios, particularly scientific AI applications, algorithm engineers widely adopt more complex convolution, e.g. 3D CNN, to improve the accuracy. Scientific AI applications with 3D-CNN, which tends to train with volumetric datasets, substantially increase the size of the input, which in turn potentially restricts the channel sizes (e.g. less than 64) under the constraints of limited device memory capacity. Since existing convolution implementations tend to split and parallelize computing the small channel convolution from channel dimension, they usually cannot fully exploit the performance of GPU accelerator, in particular that configured with the emerging tensor core. In this work, we target on enhancing the performance of small channel 3D convolution on the GPU platform configured with tensor cores. Our analysis shows that the channel size of convolution has a great effect on the performance of existing convolution implementations, that are memory-bound on tensor core. By leveraging the memory hierarchy characteristics and the WMMA API of tensor core, we propose and implement holistic optimizations for both promoting the data access efficiency and intensifying the utilization of computing units. Experiments show that our implementation can obtain 1.1x–5.4x speedup comparing to the cuDNN's implementations for the 3D convolutions on different GPU platforms. We also evaluate our implementations on two practical scientific AI applications and observe up to 1.7x and 2.0x overall speedups compared with using cuDNN on V100 GPU. [ABSTRACT FROM AUTHOR]

Influence Maximization aims to find the top-$(K)$ influential individuals to maximize the influence spread within a social network, which remains an important yet challenging problem. Proven to be NP-hard, the influence maximization problem attracts tremendous studies. Though there exist basic greedy algorithms which may provide good approximation to optimal result, they mainly suffer from low computational efficiency and excessively long execution time, limiting the application to large-scale social networks. In this paper, we present IMGPU, a novel framework to accelerate the influence maximization by leveraging the parallel processing capability of graphics processing unit (GPU). We first improve the existing greedy algorithms and design a bottom-up traversal algorithm with GPU implementation, which contains inherent parallelism. To best fit the proposed influence maximization algorithm with the GPU architecture, we further develop an adaptive $(K)$-level combination method to maximize the parallelism and reorganize the influence graph to minimize the potential divergence. We carry out comprehensive experiments with both real-world and sythetic social network traces and demonstrate that with IMGPU framework, we are able to outperform the state-of-the-art influence maximization algorithm up to a factor of 60, and show potential to scale up to extraordinarily large-scale networks. [ABSTRACT FROM AUTHOR]