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)
—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)
—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)
Heterogeneous analytical models are valuable tools that facilitate optimal application tuning via runtime prediction; however, they require several man-hours of effort to understand and employ for meaningful performance prediction. Consequently, developers face the challenge of selecting adequate performance models that best fit their design goals and level(More)
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