Graphics processing units in bioinformatics, computational biology and systems biology

Nobile, Marco S.; Cazzaniga, Paolo; Tangherloni, Andrea; Besozzi, Daniela

doi:10.1093/bib/bbw058

Cited by 98 publications

(85 citation statements)

References 129 publications

(129 reference statements)

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“…As the requirement of performing BD simulations, over larger and larger systems, increases, the number of software packages for this kind of calculations also multiplies. To gain the maximum performance, algorithms are developed for the graphical processing units (GPU), such as the coarse‐grained molecular dynamics program by Nagoya Cooperation (COGNAC) mostly applied to study polymers, the Brownian dynamics box (BD_BOX) package, which is intended as a general purpose BD engine, the algorithm of Carter et al for the BD simulations of colloids, the program by Nobile et al tailored to biomolecules, and the Browndye software by Huber for simulating the diffusional encounter of two large biological molecules …”

Section: Introductionmentioning

confidence: 99%

DiTe2: Calculating the diffusion tensor for flexible molecules

2018

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We report on an extended hydrodynamic modeling of the friction tensorial properties of flexible molecules including all types of natural, Z‐Matrix like, internal coordinates. We implement the new methodology by extending and updating the software DiTe [Barone et al. J. Comput. Chem. 30, 2 (2009)]. DiTe (DIffusion TEnsor) implements a hydrodynamic modeling of the generalized translational, rotational, and configurational friction and diffusion tensors of flexible molecules in which flexibility is described in terms of dihedral angles. The new tool, DiTe2, has been renewed to include also stretching and bending types of internal mobility. Furthermore, DiTe2 is able to calculate the friction and diffusion tensors along collective (or reaction) coordinates defined as linear combinations of the internal natural ones. A number of tests are reported to show the new features of DiTe2. As leitmotiv for the tests, the calmodulin protein is taken into consideration, described both at all‐atom and coarse‐grained levels. © 2018 Wiley Periodicals, Inc.

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Section: Introductionmentioning

confidence: 99%

DiTe2: Calculating the diffusion tensor for flexible molecules

2018

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“…Current GPUs have thousands of cores, making them a cheap and comparably easy to program and use alternative to cluster computers. GPUs become increasingly popular for scientific computing (Nobile et al, 2017) their performance keeps improving. Even a cheap GPU allows us to calculate forces orders of magnitude faster than previous simulation packages (see Supplemental Material for hardware recommendations).…”

Section: Discussionmentioning

confidence: 99%

ya||a: GPU-powered Spheroid Models for Mesenchyme and Epithelium

Germann

Marín-Riera

Sharpe

2019

Preprint

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ya||a is yet another parallel agent-based model for morphogenesis. It is several orders of magnitude faster than conventional models, because it runs on GPUs and because it has been designed for performance: Previously only complex and therefore computationally expensive models could simulate both mesenchyme and epithelium. We chose to extend the simple spheroid model by the addition of spin-like polarities to simulate epithelial sheets and tissue polarity. We also incorporate recently developed models for protrusions and migration. ya||a is written in concise, plain CUDA/C++ and available at github.com/germannp/yalla under the MIT license.

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“…28,29 Cardiac electrical dynamics simulations is no exception to this trend. 28,29 Cardiac electrical dynamics simulations is no exception to this trend.…”

Section: Related Workmentioning

confidence: 99%

“…Over the last decade, GPU computational power increased in such a way that they may be used in place of supercomputers for specific scientific computational problems. 28,29 Cardiac electrical dynamics simulations is no exception to this trend. [30][31][32] Several authors have tested GPU performance previously for different cardiac tissue models.…”

Section: Related Workmentioning

confidence: 99%

Accelerating simulations of cardiac electrical dynamics through a multi‐GPU platform and an optimized data structure

Vasconcellos

Clua

Fenton

et al. 2019

Concurrency and Computation

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Simulations of cardiac electrophysiological models in tissue, particularly in 3D require the solutions of billions of differential equations even for just a couple of milliseconds, thus highly demanding in computational resources. In fact, even studies in small domains with very complex models may take several hours to reproduce seconds of electrical cardiac behavior.Today's Graphics Processor Units (GPUs) are becoming a way to accelerate such simulations, and give the added possibilities to run them locally without the need for supercomputers.Nevertheless, when using GPUs, bottlenecks related to global memory access caused by the spatial discretization of the large tissue domains being simulated, become a big challenge. For simulations in a single GPU, we propose a strategy to accelerate the computation of the diffusion term through a data-structure and memory access pattern designed to maximize coalescent memory transactions and minimize branch divergence, achieving results approximately 1.4 times faster than a standard GPU method. We also combine this data structure with a designed communication strategy to take advantage in the case of simulations in multi-GPU platforms.We demonstrate that, in the multi-GPU approach performs, simulations in 3D tissue can be just 4× slower than real time. KEYWORDScardiac electrophysiology models, GPU Computing, memory access optimization, parallel cardiac dynamics simulations INTRODUCTIONThe large increase of computational power over the last years shifted the bottleneck of different algorithms to the memory bandwidth and memory management. 1 One typical solution employed by hardware assemblers to minimize this issue is hardware hierarchical memory and memory locality optimization.Computational systems organize hierarchical memory system into levels. In the on-chip level, the registers are the fastest memory, with a high cost per byte and low capacity. Next, there are different cache levels according to the hardware architecture, typically called L1, L2, and so on.The main memory is the next level; here, the cost per byte is less than cache or registers, but latency is high. The last level is the secondary memory that has the highest latency with the lowest cost per byte. Overall, the cost per byte of each level determines the capacity and latency, which directly impact in performance.As each level of the hierarchical memory system has a different storage capacity and data is usually kept at the lowest memory level, computational systems must choose for each level which data will be prioritized to stay in memory and which will be removed when that memory level fills up. To do so, the computer memory system employs two fundamental principles, ie, temporal and spatial locality. 2 In general, these strategies aim to keep the most recently used data in the same memory level, since having to access higher memory levels drastically increases the time of the search.Based on the memory hierarchical principles, some researchers have tried to minimize memory system bottlenecks through s...

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Graphics processing units in bioinformatics, computational biology and systems biology

Cited by 98 publications

References 129 publications

DiTe2: Calculating the diffusion tensor for flexible molecules

DiTe2: Calculating the diffusion tensor for flexible molecules

ya||a: GPU-powered Spheroid Models for Mesenchyme and Epithelium

Accelerating simulations of cardiac electrical dynamics through a multi‐GPU platform and an optimized data structure

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