MrBayes on a Graphics Processing Unit

نویسندگان

  • Jianfu Zhou
  • Xiaoguang Liu
  • Douglas S. Stones
  • Qiang Xie
  • Gang Wang
چکیده

MOTIVATION Bayesian phylogenetic inference can be used to propose a 'tree of life' for a collection of species whose DNA sequences are known. While there are many packages available that implement Bayesian phylogenetic inference, such as the popular MrBayes, running these programs poses significant computational challenges. Parallelized versions of the Metropolis coupled Markov chain Monte Carlo (MC³) algorithm in MrBayes have been presented that can run on various platforms, such as a graphics processing unit (GPU). The GPU has been used as a cost-effective means for computational research in many fields. However, until now, some limitations have prevented the GPU from being used to run MrBayes MC³ effectively. RESULTS We give an appraisal of the possibility of realistically implementing MrBayes MC³ in parallel on an ordinary four-core desktop computer with a GPU. An earlier proposed algorithm for running MrBayes MC³ in parallel on a GPU has some significant drawbacks (e.g. too much CPU-GPU communication) which we resolve. We implement these improvements on the NVIDIA GeForce GTX 480 as most other GPUs are unsuitable for running MrBayes MC³ due to a range of reasons, such as having insufficient support for double precision floating-point arithmetic. Experiments indicate that run-time can be decreased by a factor of up to 5.4 by adding a single GPU (versus state-of-the-art multicore parallel algorithms). We can also achieve a speedup (versus serial MrBayes MC³) of more than 40 on a sufficiently large dataset using two GPUs. AVAILABILITY GPU MrBayes (i.e. the proposed implementation of MrBayes MC³ for the GPU) is available from http://mrbayes-gpu.sourceforge.net/.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

GPU MrBayes V3.1: MrBayes on Graphics Processing Units for Protein Sequence Data.

We present a modified GPU (graphics processing unit) version of MrBayes, called ta(MC)(3) (GPU MrBayes V3.1), for Bayesian phylogenetic inference on protein data sets. Our main contributions are 1) utilizing 64-bit variables, thereby enabling ta(MC)(3) to process larger data sets than MrBayes; and 2) to use Kahan summation to improve accuracy, convergence rates, and consequently runtime. Versus...

متن کامل

Ultra-Fast Image Reconstruction of Tomosynthesis Mammography Using GPU

Digital Breast Tomosynthesis (DBT) is a technology that creates three dimensional (3D) images of breast tissue. Tomosynthesis mammography detects lesions that are not detectable with other imaging systems. If image reconstruction time is in the order of seconds, we can use Tomosynthesis systems to perform Tomosynthesis-guided Interventional procedures. This research has been designed to study u...

متن کامل

MrBayes tgMC3: A Tight GPU Implementation of MrBayes

MrBayes is model-based phylogenetic inference tool using Bayesian statistics. However, model-based assessment of phylogenetic trees adds to the computational burden of tree-searching, and so poses significant computational challenges. Graphics Processing Units (GPUs) have been proposed as high performance, low cost acceleration platforms and several parallelized versions of the Metropolis Coupl...

متن کامل

Parallel Implementation of Particle Swarm Optimization Variants Using Graphics Processing Unit Platform

There are different variants of Particle Swarm Optimization (PSO) algorithm such as Adaptive Particle Swarm Optimization (APSO) and Particle Swarm Optimization with an Aging Leader and Challengers (ALC-PSO). These algorithms improve the performance of PSO in terms of finding the best solution and accelerating the convergence speed. However, these algorithms are computationally intensive. The go...

متن کامل

Efficient Implementation of MrBayes on Multi-GPU

MrBayes, using Metropolis-coupled Markov chain Monte Carlo (MCMCMC or (MC)(3)), is a popular program for Bayesian inference. As a leading method of using DNA data to infer phylogeny, the (MC)(3) Bayesian algorithm and its improved and parallel versions are now not fast enough for biologists to analyze massive real-world DNA data. Recently, graphics processor unit (GPU) has shown its power as a ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • Bioinformatics

دوره 27 9  شماره 

صفحات  -

تاریخ انتشار 2011