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首页> 外文会议>IEEE International Symposium on Computer Architecture and High Performance Computing >Parallel Pairwise Correlation Computation on Intel Xeon Phi Clusters
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Parallel Pairwise Correlation Computation on Intel Xeon Phi Clusters

机译：英特尔至强融核群集上的并行成对相关计算

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Co-expression network is a critical technique for the identification of inter-gene interactions, which usually relies on all-pairs correlation (or similar measure) computation between gene expression profiles across multiple samples. Pearson's correlation coefficient (PCC) is one widely used technique for gene co-expression network construction. However, all-pairs PCC computation is computationally demanding for large numbers of gene expression profiles, thus motivating our acceleration of its execution using high-performance computing. In this paper, we present LightPCC, the first parallel and distributed all-pairs PCC computation on Intel Xeon Phi (Phi) clusters. It achieves high speed by exploring the SIMD-instruction-level and thread-level parallelism within Phis as well as accelerator-level parallelism among multiple Phis. To facilitate balanced workload distribution, we have proposed a general framework for symmetric all-pairs computation by building bijective functions between job identifier and coordinate space for the first time. We have evaluated LightPCC and compared it to two CPU-based counterparts: a sequential C++ implementation in ALGLIB and an implementation based on a parallel general matrix-matrix multiplication routine in Intel Math Kernel Library (MKL) (all use double precision), using a set of gene expression datasets. Performance evaluation revealed that with one 5110P Phi and 16 Phis, LightPCC runs up to 20.6× and 218.2× faster than ALGLIB, and up to 6.8× and 71.4× faster than single-threaded MKL, respectively. In addition, LightPCC demonstrated good parallel scalability in terms of number of Phis. Source code of LightPCC is publicly available at http://lightpcc.sourceforge.net.
机译：共表达网络是鉴定基因间相互作用的一项关键技术，通常依赖于跨多个样本的基因表达谱之间的全对相关性（或类似度量）计算。皮尔逊相关系数（PCC）是一种广泛用于基因共表达网络构建的技术。但是，全对PCC计算在计算上需要大量的基因表达谱，因此激发了我们使用高性能计算的执行速度。在本文中，我们介绍了LightPCC，这是Intel Xeon Phi（Phi）群集上的第一个并行和分布式全对PCC计算。通过探索Phis中的SIMD指令级和线程级并行性以及多个Phis中的加速器级并行性，它可以实现高速。为了促进平衡的工作量分配，我们首次通过在作业标识符和坐标空间之间建立双射函数，为对称全对计算提出了一个通用框架。我们对LightPCC进行了评估，并将其与两个基于CPU的同类产品进行了比较：ALGLIB中的顺序C ++实现和Intel Math Kernel Library（MKL）中基于并行通用矩阵矩阵乘法例程的实现（均使用双精度），使用基因表达数据集。性能评估表明，使用5110P Phi和16 Phis时，LightPCC的运行速度比ALGLIB快20.6倍和218.2倍，比单线程MKL快6.8倍和71.4倍。此外，就Phis的数量而言，LightPCC展示了良好的并行可伸缩性。 LightPCC的源代码可从http://lightpcc.sourceforge.net上公开获得。

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