Limits...
Accelerating fibre orientation estimation from diffusion weighted magnetic resonance imaging using GPUs.

Hernández M, Guerrero GD, Cecilia JM, García JM, Inuggi A, Jbabdi S, Behrens TE, Sotiropoulos SN - PLoS ONE (2013)

Bottom Line: With the performance of central processing units (CPUs) having effectively reached a limit, parallel processing offers an alternative for applications with high computational demands.We show that the parameter estimation, performed through Markov Chain Monte Carlo (MCMC), is accelerated by at least two orders of magnitude, when comparing a single GPU with the respective sequential single-core CPU version.We also illustrate similar speed-up factors (up to 120x) when comparing a multi-GPU with a multi-CPU implementation.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, University of Murcia, Murcia, Spain. moises.hernandez@um.es

ABSTRACT
With the performance of central processing units (CPUs) having effectively reached a limit, parallel processing offers an alternative for applications with high computational demands. Modern graphics processing units (GPUs) are massively parallel processors that can execute simultaneously thousands of light-weight processes. In this study, we propose and implement a parallel GPU-based design of a popular method that is used for the analysis of brain magnetic resonance imaging (MRI). More specifically, we are concerned with a model-based approach for extracting tissue structural information from diffusion-weighted (DW) MRI data. DW-MRI offers, through tractography approaches, the only way to study brain structural connectivity, non-invasively and in-vivo. We parallelise the Bayesian inference framework for the ball & stick model, as it is implemented in the tractography toolbox of the popular FSL software package (University of Oxford). For our implementation, we utilise the Compute Unified Device Architecture (CUDA) programming model. We show that the parameter estimation, performed through Markov Chain Monte Carlo (MCMC), is accelerated by at least two orders of magnitude, when comparing a single GPU with the respective sequential single-core CPU version. We also illustrate similar speed-up factors (up to 120x) when comparing a multi-GPU with a multi-CPU implementation.

Show MeSH
Total execution times (in log scale) of the bedpostX application in a single-core CPU and a Tesla C2050 GPU for the whole dataset (30 slices), as the number of fibres L is increased.Results are shown for different number K of gradient directions (64, 128 and 256) and when  MCMC iterations were utilised (3000 burn-in iterations).
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3643787&req=5

pone-0061892-g010: Total execution times (in log scale) of the bedpostX application in a single-core CPU and a Tesla C2050 GPU for the whole dataset (30 slices), as the number of fibres L is increased.Results are shown for different number K of gradient directions (64, 128 and 256) and when MCMC iterations were utilised (3000 burn-in iterations).

Mentions: Figure 10 shows the execution times of bedpostx in a cluster of either CPUs or GPUs. The reported times are for the high-resolution dataset of 102 slices as the number of fibres L is increased. The experiments were performed in the supercomputer previously described in Section. Concretely, we used 102 CPU cores and 102 GPUs. Table 4 summarizes the speed-up factors obtained for bedpostX.


Accelerating fibre orientation estimation from diffusion weighted magnetic resonance imaging using GPUs.

Hernández M, Guerrero GD, Cecilia JM, García JM, Inuggi A, Jbabdi S, Behrens TE, Sotiropoulos SN - PLoS ONE (2013)

Total execution times (in log scale) of the bedpostX application in a single-core CPU and a Tesla C2050 GPU for the whole dataset (30 slices), as the number of fibres L is increased.Results are shown for different number K of gradient directions (64, 128 and 256) and when  MCMC iterations were utilised (3000 burn-in iterations).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3643787&req=5

pone-0061892-g010: Total execution times (in log scale) of the bedpostX application in a single-core CPU and a Tesla C2050 GPU for the whole dataset (30 slices), as the number of fibres L is increased.Results are shown for different number K of gradient directions (64, 128 and 256) and when MCMC iterations were utilised (3000 burn-in iterations).
Mentions: Figure 10 shows the execution times of bedpostx in a cluster of either CPUs or GPUs. The reported times are for the high-resolution dataset of 102 slices as the number of fibres L is increased. The experiments were performed in the supercomputer previously described in Section. Concretely, we used 102 CPU cores and 102 GPUs. Table 4 summarizes the speed-up factors obtained for bedpostX.

Bottom Line: With the performance of central processing units (CPUs) having effectively reached a limit, parallel processing offers an alternative for applications with high computational demands.We show that the parameter estimation, performed through Markov Chain Monte Carlo (MCMC), is accelerated by at least two orders of magnitude, when comparing a single GPU with the respective sequential single-core CPU version.We also illustrate similar speed-up factors (up to 120x) when comparing a multi-GPU with a multi-CPU implementation.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, University of Murcia, Murcia, Spain. moises.hernandez@um.es

ABSTRACT
With the performance of central processing units (CPUs) having effectively reached a limit, parallel processing offers an alternative for applications with high computational demands. Modern graphics processing units (GPUs) are massively parallel processors that can execute simultaneously thousands of light-weight processes. In this study, we propose and implement a parallel GPU-based design of a popular method that is used for the analysis of brain magnetic resonance imaging (MRI). More specifically, we are concerned with a model-based approach for extracting tissue structural information from diffusion-weighted (DW) MRI data. DW-MRI offers, through tractography approaches, the only way to study brain structural connectivity, non-invasively and in-vivo. We parallelise the Bayesian inference framework for the ball & stick model, as it is implemented in the tractography toolbox of the popular FSL software package (University of Oxford). For our implementation, we utilise the Compute Unified Device Architecture (CUDA) programming model. We show that the parameter estimation, performed through Markov Chain Monte Carlo (MCMC), is accelerated by at least two orders of magnitude, when comparing a single GPU with the respective sequential single-core CPU version. We also illustrate similar speed-up factors (up to 120x) when comparing a multi-GPU with a multi-CPU implementation.

Show MeSH