Jiuxin Zhao

Hong Kong University of Science and Technology

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Mian Lu

Agency for Science, Technology and Research

Qiong Luo

Hong Kong University of Science and Technology

Ge Bai

Guangdong University of Technology

Bingqiang Wang

Peng Cheng Laboratory

Jie Tang

Sichuan University of Science and Engineering

Junkai Wu

University of Hong Kong

Yuwei Tan

Wageningen University & Research

Shaohua Fu

Beijing Jiaotong University

Zhang Lin

Chinese National Engineering Research Center for Control and Treatment of Heavy Metal Pollution

Qiong Luo

Women's Hospital, School of Medicine, Zhejiang University

Cooperative Institutions

Hong Kong University of Science and Technology

University of Hong Kong

Chinese Academy of Sciences

South China University of Technology

Sun Yat-sen University

Guangdong University of Technology

Beijing Jiaotong University

Hunan Normal University

Institute of Physics

Hubei Provincial Center for Disease Control and Prevention

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Integrating GPU-Accelerated Sequence Alignment and SNP Detection for Genome Resequencing Analysis

Lecture notes in computer science (2012)

Mian Lu Yuwei Tan Jiuxin Zhao Ge Bai Qiong Luo

Memory footprint

Speedup

Sequence (biology)

10.1007/978-3-642-31235-9_8

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Citations (4)

Accelerating minor allele frequency computation with graphics processors

Mian Lu Jiuxin Zhao Qiong Luo Bingqiang Wang

The computation of minor allele frequency (MAF) is at the core of a Genome-Wide Association Study (GWAS). Due to the high computation intensity and high precision requirement, so far the scale of MAF computation analysis is up to hundreds of individuals. To enable the computation for thousands of individuals, we have developed GAMA, a high performance MAF computation program with GPU acceleration. Specifically, we design a parallel reduction algorithm that matches the GPU's data-parallel architecture. To implement the new algorithm efficiently on the GPU, we utilize the fast, on-chip local memory shared within each GPU multiprocessor effectively. To avoid user-level thread synchronization, we exploit the GPU thread-warp based scheduling. Furthermore, we address the floating point underflow issue through a logarithm transformation. As a result, GAMA enables MAF computation for up to a thousand individuals for the first time. On a server equipped with an NVIDIA Tesla C2070 GPU and two Intel Xeon E5520 2.27 GHz CPUs, GAMA outperforms a state-of-the-art single-threaded MAF computation tool and our optimized parallel implementation (16-threaded) on the CPU by around 47 and 3.5 times, respectively.

Xeon

Speedup

Xeon Phi

10.1145/2351316.2351328

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Citations (4)

GPU-Accelerated Bidirected De Bruijn Graph Construction for Genome Assembly

Lecture notes in computer science (2013)

Mian Lu Qiong Luo Bingqiang Wang Junkai Wu Jiuxin Zhao

De Bruijn graph

Graphics processing unit

Speedup

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Citations (3)

Accelerating Topic Model Training on a Single Machine

Lecture notes in computer science (2013)

Mian Lu Ge Bai Qiong Luo Jie Tang Jiuxin Zhao

Speedup

Graphics processing unit

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GSNP: A DNA Single-Nucleotide Polymorphism Detection System with GPU Acceleration

International Conference on Parallel Processing (2011)

Mian Lu Jiuxin Zhao Qiong Luo Bingqiang Wang Shaohua Fu

We have developed GSNP, a software package with GPU acceleration, for single-nucleotide polymorphism detection on DNA sequences generated from second-generation sequencing equipment. Compared with SOAPsnp, a popular, high-performance CPU-based SNP detection tool, GSNP has several distinguishing features: First, we design a sparse data representation format to reduce memory access as well as branch divergence. Second, we develop a multipass sorting network to efficiently sort a large number of small arrays on the GPU. Third, we compute a table of frequently used scores once to avoid repeated, expensive computation and to reduce random memory access. Fourth, we apply customized compression schemes to the output data to improve the I/O performance. As a result, on a server equipped with an Intel Xeon E5630 2.53 GHZ CPU and an NVIDIA Tesla M2050 GPU, it took GSNP about two hours to analyze a whole human genome dataset whereas the CPU-based, single-threaded SOAPsnp took three days for the same task on the same machine.

Xeon

10.1109/icpp.2011.51

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Citations (16)

Integrating GPU-Accelerated Sequence Alignment and SNP Detection for Genome Resequencing Analysis

Lecture notes in computer science (2012)

Mian Lu Yuwei Tan Jiuxin Zhao Ge Bai Qiong Luo

Memory footprint

Sequence (biology)

Source

Cite

Citations (1)