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Heterogeneous computing architecture for fast detection of SNP-SNP interactions.

Sluga D, Curk T, Zupan B, Lotric U - BMC Bioinformatics (2014)

Bottom Line: Their utility resulted in an order of magnitude shorter execution times when compared to the single-threaded CPU implementation.GPU implementation on a single Nvidia Tesla K20 runs twice as fast as that for the MIC architecture-based Xeon Phi P5110 coprocessor, but also requires considerably more programming effort.On the other hand the new MIC architecture, albeit lacking in performance reduces the programming effort and makes it up with a more general architecture suitable for a wider range of problems.

View Article: PubMed Central - HTML - PubMed

Affiliation: Faculty of Computer and Information Science, University of Ljubljana, Trzaska 25, SI 1000 Ljubljana, SI, Slovenia. uros.lotric@fri.uni-lj.si.

ABSTRACT

Background: The extent of data in a typical genome-wide association study (GWAS) poses considerable computational challenges to software tools for gene-gene interaction discovery. Exhaustive evaluation of all interactions among hundreds of thousands to millions of single nucleotide polymorphisms (SNPs) may require weeks or even months of computation. Massively parallel hardware within a modern Graphic Processing Unit (GPU) and Many Integrated Core (MIC) coprocessors can shorten the run time considerably. While the utility of GPU-based implementations in bioinformatics has been well studied, MIC architecture has been introduced only recently and may provide a number of comparative advantages that have yet to be explored and tested.

Results: We have developed a heterogeneous, GPU and Intel MIC-accelerated software module for SNP-SNP interaction discovery to replace the previously single-threaded computational core in the interactive web-based data exploration program SNPsyn. We report on differences between these two modern massively parallel architectures and their software environments. Their utility resulted in an order of magnitude shorter execution times when compared to the single-threaded CPU implementation. GPU implementation on a single Nvidia Tesla K20 runs twice as fast as that for the MIC architecture-based Xeon Phi P5110 coprocessor, but also requires considerably more programming effort.

Conclusions: General purpose GPUs are a mature platform with large amounts of computing power capable of tackling inherently parallel problems, but can prove demanding for the programmer. On the other hand the new MIC architecture, albeit lacking in performance reduces the programming effort and makes it up with a more general architecture suitable for a wider range of problems.

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SNPsyn software architecture. Computation of SNP-SNP interaction is coded in C++ for the CPU, CUDA and MIC architectures. The scheduler that invokes the three heterogeneous implementations is written in Python.
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Figure 2: SNPsyn software architecture. Computation of SNP-SNP interaction is coded in C++ for the CPU, CUDA and MIC architectures. The scheduler that invokes the three heterogeneous implementations is written in Python.

Mentions: The SNP-SNP interaction scoring scheduler, written in Python, partitions and distributes the computational tasks to all available, user-specified resources: CPUs, GPUs, and Xeon Phi coprocessors (Figure 2). It then merges the results from individual units into a final result file. Each thread (CPU, GPU or Xeon Phi) takes one pair of SNPs and performs all the calculations needed to compute the synergy score of the pair. The synergy of a pair of SNPs X and Y with respect to phenotype P is obtained by subtracting the information gains of individual SNPs from the information gain of the combined pair [13]:


Heterogeneous computing architecture for fast detection of SNP-SNP interactions.

Sluga D, Curk T, Zupan B, Lotric U - BMC Bioinformatics (2014)

SNPsyn software architecture. Computation of SNP-SNP interaction is coded in C++ for the CPU, CUDA and MIC architectures. The scheduler that invokes the three heterogeneous implementations is written in Python.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4230497&req=5

Figure 2: SNPsyn software architecture. Computation of SNP-SNP interaction is coded in C++ for the CPU, CUDA and MIC architectures. The scheduler that invokes the three heterogeneous implementations is written in Python.
Mentions: The SNP-SNP interaction scoring scheduler, written in Python, partitions and distributes the computational tasks to all available, user-specified resources: CPUs, GPUs, and Xeon Phi coprocessors (Figure 2). It then merges the results from individual units into a final result file. Each thread (CPU, GPU or Xeon Phi) takes one pair of SNPs and performs all the calculations needed to compute the synergy score of the pair. The synergy of a pair of SNPs X and Y with respect to phenotype P is obtained by subtracting the information gains of individual SNPs from the information gain of the combined pair [13]:

Bottom Line: Their utility resulted in an order of magnitude shorter execution times when compared to the single-threaded CPU implementation.GPU implementation on a single Nvidia Tesla K20 runs twice as fast as that for the MIC architecture-based Xeon Phi P5110 coprocessor, but also requires considerably more programming effort.On the other hand the new MIC architecture, albeit lacking in performance reduces the programming effort and makes it up with a more general architecture suitable for a wider range of problems.

View Article: PubMed Central - HTML - PubMed

Affiliation: Faculty of Computer and Information Science, University of Ljubljana, Trzaska 25, SI 1000 Ljubljana, SI, Slovenia. uros.lotric@fri.uni-lj.si.

ABSTRACT

Background: The extent of data in a typical genome-wide association study (GWAS) poses considerable computational challenges to software tools for gene-gene interaction discovery. Exhaustive evaluation of all interactions among hundreds of thousands to millions of single nucleotide polymorphisms (SNPs) may require weeks or even months of computation. Massively parallel hardware within a modern Graphic Processing Unit (GPU) and Many Integrated Core (MIC) coprocessors can shorten the run time considerably. While the utility of GPU-based implementations in bioinformatics has been well studied, MIC architecture has been introduced only recently and may provide a number of comparative advantages that have yet to be explored and tested.

Results: We have developed a heterogeneous, GPU and Intel MIC-accelerated software module for SNP-SNP interaction discovery to replace the previously single-threaded computational core in the interactive web-based data exploration program SNPsyn. We report on differences between these two modern massively parallel architectures and their software environments. Their utility resulted in an order of magnitude shorter execution times when compared to the single-threaded CPU implementation. GPU implementation on a single Nvidia Tesla K20 runs twice as fast as that for the MIC architecture-based Xeon Phi P5110 coprocessor, but also requires considerably more programming effort.

Conclusions: General purpose GPUs are a mature platform with large amounts of computing power capable of tackling inherently parallel problems, but can prove demanding for the programmer. On the other hand the new MIC architecture, albeit lacking in performance reduces the programming effort and makes it up with a more general architecture suitable for a wider range of problems.

Show MeSH