Parallel Mutual Information Based Construction of Whole-Genome Networks on the Intel (R) Xeon Phi (TM) Coprocessor

Abstract

Construction of whole-genome networks from large-scale gene expression data is an important problem in systems biology. While several techniques have been developed, most cannot handle network reconstruction at the wholegenome scale, and the few that can, require large clusters. In this paper, we present a solution on the Intel R Xeon Phi TM coprocessor, taking advantage of its multi-level parallelism including many x86-based cores, multiple threads per core, and vector processing units. We also present a solution on the Intel R Xeon R processor. Our solution is based on TINGe, a fast parallel network reconstruction technique that uses mutual information and permutation testing for assessing statistical significance. We demonstrate the first ever inference of a plant whole genome regulatory network on a single chip by constructing a 15,575 gene network of the plant Arabidopsis thaliana from 3,137 microarray experiments in only 22 minutes. In addition, our optimization for parallelizing mutual information computation on the Intel Xeon Phi coprocessor holds out lessons that are applicable to other domains.

Parallel Algorithms for Inferring Gene Regulatory Networks: A Review

“…For a continuous value, this function returns a vector of size b with k continuous non-negative weights that indicate to which bins the value should be assigned. Based on this idea, four parallel reverse engineering [48, 65-67] have been developed which will be discussed in detail below.…”

“…In their performance evaluations on Arabidopsis thaliana of size 15222 genes and 3137 observations, the method inferred GRN in 30 minutes on a 2048-CPU Blue Gene/L and 2 hours and 25 minutes on a 8 node Cell blade cluster. Since, TINGe was successful, Misra et al [65] implemented it on the Intel Xeon Phi single-chip coprocessor and Chockalingam et al [67] developed a distributed version of TINGe on the Amazon EC2 cloud computing platform by using Hadoop framework.…”

Computing Platforms for Big Biological Data Analytics: Perspectives and Challenges

“…Algorithms must be redesigned to take advantage from VPUs using intrinsics. Researchers have been working on particular optimization for biological algorithms on Xeon Phi, such as Smith-Waterman sequence alignment [28] and construction of whole-genome networks [81] . Second, those co-processors usually have their own limitations, which should be taken into account when designing algorithms.…”

mAMBER: Accelerating Explicit Solvent Molecular Dynamic with Intel Xeon Phi Many-Integrated Core Coprocessors

“…It contains 50 cores clocked at 1GHz or higher, and 512-bit SIMD(Single Instruction Multiple Data) capabilities, and two out of ten supercomputers in TOP500 (which ranks the world's 500 most powerful supercomputers) are equipped with MIC (Tianhe-2 48,000 boards, and Stampede 6,400 boards). MIC has demonstrated its potential in accelerating various computation, such as sparse matrix-vector multiplication [6], 1D FFT computations [7], molecular dynamics [8], computational biology [9], Linpack Benchmark calculation [10], et al There are two major approaches to utilize MIC in an application, namely native model and offload model. In the native model, the MIC coprocessor is regarded the same as CPU, one copy of application runs on the processor and coprocessor simultaneously, like two compute nodes in the network.…”