High Performance Computing
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The HPC Challenge (HPCC), a suite of seven benchmarks, is fast evolving as a standard for evaluating the performance of supercomputers across a spectrum of real-world applications. The HPC team at IRL is actively involved in performance optimization and tuning of the HPC Challenge benchmarks on high-end systems, such as the Blue Gene supercomputers.
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Algorithms in Medical Imaging domain are computationally intensive and require processing of large amount of data. This project aims at leveraging graphics processors (GPU) and multi-core processors (such as Cell, Intel/AMD processors) for accelerating some important medical imaging algorithms.
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This effort aims to design and implement distributed scalable algorithms for text indexing and search for massively parallel systems. It involves theoretical and experimental analysis of scalability and performance for Blue Gene/L and massive multi-core systems that will be available in near future.
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The objective of this project is to efficiently solve a system of ill-conditioned large sparse linear equations, through iterative techniques in distributed memory systems like Blue Gene.
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Satisfiability or SAT is a standard NP-complete problem with applications in design automation and artificial intelligence (AI). In this project, we are studying alternatives to standard DPLL based algorithms for large-scale parallel implementation of SAT solvers.
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The goal of this project is to study OS jitter and its impact on the performance of tightly coupled parallel applications running on large HPC clusters. These applications consist of multiple phases of computation where each computation phase is followed by some sort of synchronization across nodes. The work in this project has so far focused on theoretical modeling of noise, validation of the theoretical model using data from large production HPC clusters and identification of sources of OS jitter on a single node.
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