Search Results - "Shankar, Mallikarjun Arjun"

DeepThermo: Deep Learning Accelerated Parallel Monte Carlo Sampling for Thermodynamics Evaluation of High Entropy Alloys by Yin, Junqi, Wang, Feiyi, Shankar, Mallikarjun Arjun

“…Since the introduction of Metropolis Monte Carlo (MC) sampling, it and its variants have become standard tools used for thermodynamics evaluations of physical…”
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Comparative evaluation of deep learning workloads for leadership-class systems by Yin, Junqi, Tsaris, Aristeidis, Dash, Sajal, Miller, Ross, Wang, Feiyi, Shankar, Mallikarjun (Arjun)

“…Deep learning (DL) workloads and their performance at scale are becoming important factors to consider as we design, develop and deploy next-generation…”
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FORGE: Pre-Training Open Foundation Models for Science by Yin, Junqi, Dash, Sajal, Wang, Feiyi, Shankar, Mallikarjun Arjun

“…Large language models (LLMs) are poised to revolutionize the way we conduct scientific research. However, both model complexity and pre-training cost are…”
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Strategies for Integrating Deep Learning Surrogate Models with HPC Simulation Applications by Yin, Junqi, Wang, Feiyi, Shankar, Mallikarjun

“…The emerging trend of the convergence of high performance computing (HPC), machine learning/deep learning (ML/DL), and big data analytics presents a host of…”
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Data optimization for large batch distributed training of deep neural networks by Gahlot, Shubhankar, Yin, Junqi, Shankar, Mallikarjun Arjun

“…Distributed training in deep learning (DL) is common practice as data and models grow. The current practice for distributed training of deep neural networks…”
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Strategies to Deploy and Scale Deep Learning on the Summit Supercomputer by Yin, Junqi, Gahlot, Shubhankar, Laanait, Nouamane, Maheshwari, Ketan, Morrison, Jack, Dash, Sajal, Shankar, Mallikarjun

“…The rapid growth and wide applicability of Deep Learning (DL) frameworks poses challenges to computing centers which need to deploy and support the software,…”
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Enabling discovery data science through cross-facility workflows by Antypas, K. B., Bard, D. J., Blaschke, J. P., Shane Canon, R., Enders, Bjoern, Shankar, Mallikarjun Arjun, Somnath, Suhas, Stansberry, Dale, Uram, Thomas D., Wilkinson, Sean R.

“…Experimental and observational instruments for scientific research (such as light sources, genome sequencers, accelerators, telescopes and electron…”
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