Pramod Kumbhar

High Performance Computing
Pramod Kumbhar is an Engineer in the High Performance Computing Section in the Computing Division. His focus is on the development of the NEURON/CoreNEURON simulator within the Blue Brain Project. His work involves parallelisation, performance optimisation and scaling of scientific codes on supercomputing architectures like IBM BlueGene, Intel MIC, IBM Power and GPUs.

Pramod has strong hands-on experience with variety of performance analysis tools at scale and micro-architecture level performance tuning. He also has a keen interest in domain specific languages (DSL) and modern compiler technologies.

Before joining the Blue Brain Project, Pramod worked at the Jülich Research Centre, Germany.

Pramod received a Master’s Degree in High Performance Computing from the Edinburgh Parallel Computer Centre (EPCC) at the University of Edinburgh, Scotland.

Selected Publications

P. Kumbhar, M. Hines, A. Ovcharenko, D. Mallon, J. King, F. Sainz, F. Schürmann and F. Delalondre. Leveraging a Cluster-Booster Architecture for Brain-Scale Simulations. International Supercomputing Conference 2016

P. Kumbhar, M. Hines. CoreNeuron Neuronal Network Simulator Optimization Opportunities and Early Experience. GPU Technology Conference 2016.

T. Ewart, S. Yates, F. Cremonesi, P. Kumbhar, F. Schürmann and F. Delalondre. Performance Evaluation of the IBM POWER8 Architecture to Support Computational Neuroscientific Application Using Morphologically Detailed Neurons. Supercomputing Conference (Workshop) 2015

A. Ovcharenko, P. Kumbhar, M. Hines, F. Cremonesi, T. Ewart, S. Yates, F. Schürmann and F. Delalondre. Simulating Morphologically Detailed Neuronal Networks at Extreme Scale. Exascale Applications and Software Conference 2015

F. Schürmann et al. Rebasing I/O for Scientific Computing: Leveraging Storage Class Memory in an IBM BlueGene/Q Supercomputer. International Supercomputing Conference 2014

C. Hebel, S. Rudolph, A. Mester, J. Huisman, P. Kumbhar, H. Vereecken and J. Kruk. Three-dimensional imaging of subsurface structural patterns using quantitative large-scale multiconfiguration electromagnetic induction data. Water Resources Research 2014