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Computer Science

D-Index
62
Citations
14601
World Ranking
2913
National Ranking
1433

Overview

Pradeep Dubey is affiliated with Intel in the United States and has contributed extensively to research in Agricultural and Biological Sciences, with a focus on Soil Science, Plant Science, Agronomy and Crop Science, Artificial Intelligence, and Ecology.

The scientist's research covers multiple key topics including:

  • Soil Carbon and Nitrogen Dynamics
  • Agriculture Sustainability and Environmental Impact
  • Rice Cultivation and Yield Improvement
  • Bioenergy Crop Production and Management
  • Climate Change Impacts on Agriculture
  • Crop Yield and Soil Fertility
  • Parallel Computing and Optimization Techniques

Pradeep Dubey has published in a variety of venues, commonly appearing in:

  • arXiv (Cornell University)
  • Land Degradation and Development
  • Journal of Environmental Management
  • Current Research in Environmental Sustainability
  • Ecological Indicators

Some of the recent papers authored or co-authored by Pradeep Dubey include:

  • Steering the restoration of degraded agroecosystems during the United Nations Decade on Ecosystem Restoration, 2020, Journal of Environmental Management
  • Planet friendly agriculture: Farming for people and the planet, 2021, Current Research in Environmental Sustainability
  • Low input sustainable agriculture: A viable climate-smart option for boosting food production in a warming world, 2020, Ecological Indicators
  • Combined effects of dry-wet irrigation, redox changes and microbial diversity on soil nutrient bioavailability in the rice field, 2023, Soil and Tillage Research
  • FP8 Formats for Deep Learning, 2022, arXiv (Cornell University)

The scientist collaborates frequently with peers in the field, including:

  • P.C. Abhilash
  • Ajeet Singh
  • Rajan Chaurasia
  • Sheikh Adil Edrisi
  • Deepranjan Sarkar

Best Publications

  • Larrabee: a many-core x86 architecture for visual computing

    Larry Seiler;Doug Carmean;Eric Sprangle;Tom Forsyth

  • Debunking the 100X GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU

    Victor W. Lee;Changkyu Kim;Jatin Chhugani;Michael Deisher

  • Efficient rijndael encryption implementation with composite field arithmetic

    A. Rudra;P.K. Dubey;C.S. Jutla;V. Kumar

  • AltiVec extension to PowerPC accelerates media processing

    K. Diefendorff;P.K. Dubey;R. Hochsprung;H. Scale

  • FAST: fast architecture sensitive tree search on modern CPUs and GPUs

    Changkyu Kim;Jatin Chhugani;Nadathur Satish;Eric Sedlar

  • Sort vs. Hash revisited: fast join implementation on modern multi-core CPUs

    Changkyu Kim;Tim Kaldewey;Victor W. Lee;Eric Sedlar

  • ClearPath: highly parallel collision avoidance for multi-agent simulation

    Stephen. J. Guy;Jatin Chhugani;Changkyu Kim;Nadathur Satish

  • 3.5-D Blocking Optimization for Stencil Computations on Modern CPUs and GPUs

    Anthony Nguyen;Nadathur Satish;Jatin Chhugani;Changkyu Kim

  • GraphMat: high performance graph analytics made productive

    Narayanan Sundaram;Nadathur Satish;Mostofa Ali Patwary;Subramanya R. Dulloor

  • How multimedia workloads will change processor design

    K. Diefendorff;P.K. Dubey

  • PLEdestrians: a least-effort approach to crowd simulation

    Stephen J. Guy;Jatin Chhugani;Sean Curtis;Pradeep Dubey

  • Fast sort on CPUs and GPUs: a case for bandwidth oblivious SIMD sort

    Nadathur Satish;Changkyu Kim;Jatin Chhugani;Anthony D. Nguyen

  • Efficient sparse matrix-vector multiplication on x86-based many-core processors

    Xing Liu;Mikhail Smelyanskiy;Edmond Chow;Pradeep Dubey

  • Efficient implementation of sorting on multi-core SIMD CPU architecture

    Jatin Chhugani;Anthony D. Nguyen;Victor W. Lee;William Macy

  • Second Life and the New Generation of Virtual Worlds

    S. Kumar;J. Chhugani;Changkyu Kim;Daehyun Kim

  • Platform 2015: Intel ® Processor and Platform Evolution for the Next Decade

    Shekhar Borkar;Pradeep Dubey;Kevin Kahn;David Kuck

  • ScaleDeep: A Scalable Compute Architecture for Learning and Evaluating Deep Networks

    Swagath Venkataramani;Ashish Ranjan;Subarno Banerjee;Dipankar Das

  • Design and Implementation of the Linpack Benchmark for Single and Multi-node Systems Based on Intel® Xeon Phi Coprocessor

    Alexander Heinecke;Karthikeyan Vaidyanathan;Mikhail Smelyanskiy;Alexander Kobotov

  • Navigating the maze of graph analytics frameworks using massive graph datasets

    Nadathur Satish;Narayanan Sundaram;Md. Mostofa Ali Patwary;Jiwon Seo

  • Method and system for controlled distribution of application code and content data within a computer network

    David John Craft;Pradeep K. Dubey;Harm Peter Hofstee;James Allan Kahle

  • A Study of BFLOAT16 for Deep Learning Training

    Dhiraj D. Kalamkar;Dheevatsa Mudigere;Naveen Mellempudi;Dipankar Das

  • GraphMat: High performance graph analytics made productive

    Narayanan Sundaram;Nadathur Rajagopalan Satish;Mostofa Ali Patwary;Subramanya R Dulloor

Frequent Co-Authors

Nadathur Satish
Nadathur Satish Facebook (United States)
Mikhail Smelyanskiy
Mikhail Smelyanskiy Nvidia (United States)
Changkyu Kim
Changkyu Kim Facebook (United States)
Michael J. Flynn
Michael J. Flynn Stanford University
Yen-Kuang Chen
Yen-Kuang Chen Alibaba Group (China)
Charanjit S. Jutla
Charanjit S. Jutla IBM (United States)
David G. Andersen
David G. Andersen Carnegie Mellon University
Srinivasan Parthasarathy
Srinivasan Parthasarathy The Ohio State University
Hai Li
Hai Li Duke University

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