Accelerating compartmental modeling on a graphical processing unit

Ben-Shalom, Roy; Liberman, Gilad; Korngreen, Alon

doi:10.3389/fninf.2013.00004

Cited by 16 publications

(23 citation statements)

References 36 publications

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“…Largescale network simulations may require High Performance Computers (HPCs), or Graphical Processing Units (GPUs) [13], [14]. Still more problematic are simulations that use highly specialized systems such as neuromorphic chips [15], [16], or SpiNNaker chips [17], [18], hardwares that are not commercially available.…”

Section: Introductionmentioning

confidence: 99%

Reproducibility in Computational Neuroscience Models and Simulations

McDougal

Bulanova

Lytton

2016

IEEE Trans. Biomed. Eng.

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Objective Like all scientific research, computational neuroscience research must be reproducible. Big data science, including simulation research, cannot depend exclusively on journal articles as the method to provide the sharing and transparency required for reproducibility. Methods Ensuring model reproducibility requires the use of multiple standard software practices and tools, including version control, strong commenting and documentation, and code modularity. Results Building on these standard practices, model sharing sites and tools have been developed that fit into several categories: 1. standardized neural simulators, 2. shared computational resources, 3. declarative model descriptors, ontologies and standardized annotations; 4. model sharing repositories and sharing standards. Conclusion A number of complementary innovations have been proposed to enhance sharing, transparency and reproducibility. The individual user can be encouraged to make use of version control, commenting, documentation and modularity in development of models. The community can help by requiring model sharing as a condition of publication and funding. Significance Model management will become increasingly important as multiscale models become larger, more detailed and correspondingly more difficult to manage by any single investigator or single laboratory. Additional big data management complexity will come as the models become more useful in interpreting experiments, thus increasing the need to ensure clear alignment between modeling data, both parameters and results, and experiment.

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Section: Introductionmentioning

confidence: 99%

Reproducibility in Computational Neuroscience Models and Simulations

McDougal

Bulanova

Lytton

2016

IEEE Trans. Biomed. Eng.

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“…The client-server architecture of NEUROiD allows the compute intensive operations to be performed on a high-performance server. We are cognizant of the challenges involved in simulating large biologically realistic networks and intend to employ suitable CPU/GPU parallelization techniques, such as (Ben-Shalom et al, 2013; Hoang et al, 2013; Vooturi et al, 2017) in future releases of NEUROiD to improve the simulation time. Integration of other parameter search techniques such as Van Geit et al (2016) and Sutton et al (1999) lie within the scope of future work to help the users in efficient curation of models.…”

Section: Discussionmentioning

confidence: 99%

Curated Model Development Using NEUROiD: A Web-Based NEUROmotor Integration and Design Platform

Iyengar

Pithapuram

Singh

et al. 2019

Front. Neuroinform.

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Decades of research on neuromotor circuits and systems has provided valuable information on neuronal control of movement. Computational models of several elements of the neuromotor system have been developed at various scales, from sub-cellular to system. While several small models abound, their structured integration is the key to building larger and more biologically realistic models which can predict the behavior of the system in different scenarios. This effort calls for integration of elements across neuroscience and musculoskeletal biomechanics. There is also a need for development of methods and tools for structured integration that yield larger in silico models demonstrating a set of desired system responses. We take a small step in this direction with the NEUROmotor integration and Design (NEUROiD) platform. NEUROiD helps integrate results from motor systems anatomy, physiology, and biomechanics into an integrated neuromotor system model. Simulation and visualization of the model across multiple scales is supported. Standard electrophysiological operations such as slicing, current injection, recording of membrane potential, and local field potential are part of NEUROiD. The platform allows traceability of model parameters to primary literature. We illustrate the power and utility of NEUROiD by building a simple ankle model and its controlling neural circuitry by curating a set of published components. NEUROiD allows researchers to utilize remote high-performance computers for simulation, while controlling the model using a web browser.

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“…The solving of one Hines system can be also seen as a set of independent and non-independent triangular systems, which could be solved by using the Thomas algorithm. Previous works [12] have explored the use of other algorithms based on the Stone's method [13]. Unlike Thomas algorithm, this method is parallel.…”

Section: Motivationmentioning

confidence: 99%

“…Unlike the work presented in [12], where a relatively low number of neurons (128) is computed using single precision operations, in this work we are able to execute a very high number of neurons (up to hundreds of thousands) using double precision operations. We have used the Hines algorithm, which is the optimum method in terms of number of operations, avoiding high expensive computational operations, such as synchronizations and atomic accesses.…”

Section: Motivationmentioning

confidence: 99%

Simulating the behavior of the Human Brain on GPUs

Valero-Lara

Martínez-Pérez

Sirvent

et al. 2018

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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The simulation of the behavior of the Human Brain is one of the most important challenges in computing today. The main problem consists of finding efficient ways to manipulate and compute the huge volume of data that this kind of simulations need, using the current technology. In this sense, this work is focused on one of the main steps of such simulation, which consists of computing the Voltage on neurons’ morphology. This is carried out using the Hines Algorithm and, although this algorithm is the optimum method in terms of number of operations, it is in need of non-trivial modifications to be efficiently parallelized on GPUs. We proposed several optimizations to accelerate this algorithm on GPU-based architectures, exploring the limitations of both, method and architecture, to be able to solve efficiently a high number of Hines systems (neurons). Each of the optimizations are deeply analyzed and described. Two different approaches are studied, one for mono-morphology simulations (batch of neurons with the same shape) and one for multi-morphology simulations (batch of neurons where every neuron has a different shape). In mono-morphology simulations we obtain a good performance using just a single kernel to compute all the neurons. However this turns out to be inefficient on multi-morphology simulations. Unlike the previous scenario, in multi-morphology simulations a much more complex implementation is necessary to obtain a good performance. In this case, we must execute more than one single GPU kernel. In every execution (kernel call) one specific part of the batch of the neurons is solved. These parts can be seen as multiple and independent tridiagonal systems. Although the present paper is focused on the simulation of the behavior of the Human Brain, some of these techniques, in particular those related to the solving of tridiagonal systems, can be also used for multiple oil and gas simulations. Our studies have proven that the optimizations proposed in the present work can achieve high performance on those computations with a high number of neurons, being our GPU implementations about 4× and 8× faster than the OpenMP multicore implementation (16 cores), using one and two NVIDIA K80 GPUs respectively. Also, it is important to highlight that these optimizations can continue scaling, even when dealing with a very high number of neurons.

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Accelerating compartmental modeling on a graphical processing unit

Cited by 16 publications

References 36 publications

Reproducibility in Computational Neuroscience Models and Simulations

Reproducibility in Computational Neuroscience Models and Simulations

Curated Model Development Using NEUROiD: A Web-Based NEUROmotor Integration and Design Platform

Simulating the behavior of the Human Brain on GPUs

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