With the GPU accelerators, the university achieved results 30 times faster when compared to conventional CPU-based systems. This performance boost enabled the team to simulate larger-scale neuron circuit models and develop new data analysis tools.
Benefits and impact
An example application of this research was in understanding the physiological and pharmacological behavior to predict the impact of drugs on brain dysfunctions. "Our primary goal was to seek mechanisms through which some of these conditions can be managed," said Diwakar.
With GPU based solution, it was easy to reconstruct millions of neurons and understand how they interact with several millions of synaptic connections. It also helped in understanding what happens to these circuits when certain drugs were used to modify or remedy certain behavioral conditions.
Ordinary CPUs could be used for simulations with a small number of detailed models of neurons, but in the case of very large scale simulations, GPUs make it more effective.
"We are planning to release these set of codes so that anyone may simulate brain circuits and their properties under therapeutic or pharmacological conditions. Besides that, B.Tech, M.Sc. and M.Tech students are also now interested in learning and using these coding strategies to develop their own programming case studies on such technologies," said Diwakar.
"The development of new understandings and perspectives are increasing the prediction and treatment of these debilitating neurological conditions that affect millions around the world," concluded Diwakar.
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