Vivek K. Pallipuram

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Recently, General Purpose Graphical Processing Units (GP-GPUs) have been identified as an intriguing technology to accelerate numerous data-parallel algorithms. Several GPU architectures and programming models are beginning to emerge and establish their niche in the High-Performance Computing (HPC) community. New massively parallel architectures such as the(More)
Recently, there has been strong interest in large-scale simulations of biological spiking neural networks (SNN) to model the human brain mechanisms and capture its inference capabilities. Among various spiking neuron models, the Hodgkin-Huxley model is the oldest and most compute intensive, whereas the more recent Izhikevich model is very compute efficient.(More)
The quality of an image is highly critical for applications such as robotic vision, surveillance, medical imaging, etc. The images captured in real-time are seldom noise free and therefore require noise removal for further processing. Out of several proposed noise removal schemes, an isotropic diffusion filtering is known to achieve highly precise results.(More)
During recent years General-Purpose Graphical Processing Units (GP-GPUs) have entered the field of High-Performance Computing (HPC) as one of the primary architectural focuses for many research groups working with complex scientific applications. Nvidia's Tesla C2050, codenamed Fermi, and AMD's Radeon 5870 are two devices positioned to meet the(More)
Field-programmable gate arrays (FPGAs) can provide an efficient programmable resource for implementing hardware-based spiking neural networks (SNN). In this paper we present a hardware-software design that makes it possible to simulate large-scale (2 million neurons) biologically plausible SNNs on an FPGA-based system. We have chosen three SNN models from(More)
Today, scientific workflows on high-end non-dedicated clusters increasingly resemble directed acyclic graphs (DAGs). The execution trace analysis of the associated DAG-based workflows can provide valuable insights into the system behavior in general, and the occurrences of events like idle times in particular, thereby opening avenues for optimized resource(More)
While pursuing high performance and cost effectiveness for directed acyclic graph (DAG)-structured scientific workflow executions in the cloud, it is critical to identify appropriate resource instances and their quantity. This paper presents a testing engine that employs a resource-selection heuristic, which statically analyzes the DAG structure to guide(More)
—In the past forty years, the high-performance computing (HPC) community has been developing powerful and rigorous tools for predicting the performance of supercomputers from log traces. In this paper, we transform one of these approaches previously used for predicting idle resources in high-end clusters into a method for capturing extreme climate events in(More)
There has been a strong interest in the neuroscience community to model a mammalian brain in order to study its architecture and functional principles. Spiking Neural Network (SNN) models have been widely employed to simulate the mammalian brain, capturing its functionality and inference capabilities. The biologically accurate models from this class include(More)