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Computer Science

D-Index
38
Citations
9240
World Ranking
10016
National Ranking
4222

Overview

Nadathur Satish is affiliated with Facebook in the United States and has contributed to research primarily within the fields of Engineering and Materials Science. Their work spans several subfields, including Mechanical Engineering, Ceramics and Composites, Mechanics of Materials, Materials Chemistry, and Aerospace Engineering.

Their research focuses on topics such as Aluminum Alloys Composites Properties, Advanced Materials and Composites, Advanced Ceramic Materials Synthesis, Welding Techniques and Residual Stresses, Metal Forming Simulation Techniques, Metallurgy and Material Forming, and Metallurgy and Material Science.

Recent papers authored or co-authored by Satish include:

  • "Effect Of Cutting Parameters On Tool Tip Temperature And Cutting Forces Of Copper Alloy" (2023), published in the International Journal of Mechanical Engineering and Research
  • "Effect of Brass Content and Speed of Cut on Machining Outcomes of Copper-2% Silver Alloy" (2024), published in the Journal of The Institution of Engineers (India) Series D

Satish frequently collaborates with other researchers. Notable co-authors include:

  • B. Sunil Kumar (2 publications)
  • Mrudula Prashanth (2 publications)
  • Raghavendra Subramanya (1 publication)
  • A. Joshi (1 publication)
  • B. S. Ajaykumar (1 publication)

Their works have appeared in various publication venues, predominantly:

  • International Journal of Mechanical Engineering and Research
  • Journal of The Institution of Engineers (India) Series D

Best Publications

  • 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

  • Designing efficient sorting algorithms for manycore GPUs

    Nadathur Satish;Mark Harris;Michael Garland

  • Scalable Bayesian Optimization Using Deep Neural Networks

    Jasper Snoek;Oren Rippel;Oren Rippel;Kevin Swersky;Ryan Kiros

  • 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

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

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

  • DySER: Unifying Functionality and Parallelism Specialization for Energy-Efficient Computing

    V. Govindaraju;Chen-Han Ho;T. Nowatzki;J. Chhugani

  • Graphicionado: a high-performance and energy-efficient accelerator for graph analytics

    Tae Jun Ham;Lisa Wu;Narayanan Sundaram;Nadathur Satish

  • Debunking the 100X GPU vs. CPU myth

    Unknown

  • Data tiering in heterogeneous memory systems

    Subramanya R. Dulloor;Amitabha Roy;Zheguang Zhao;Narayanan Sundaram

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

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

  • Streaming similarity search over one billion tweets using parallel locality-sensitive hashing

    Narayanan Sundaram;Aizana Turmukhametova;Nadathur Satish;Todd Mostak

  • IMP: indirect memory prefetcher

    Xiangyao Yu;Christopher J. Hughes;Nadathur Satish;Srinivas Devadas

  • Fast updates on read-optimized databases using multi-core CPUs

    Jens Krueger;Changkyu Kim;Martin Grund;Nadathur Satish

  • PALM: parallel architecture-friendly latch-free modifications to B+ trees on many-core processors

    Jason Sewall;Jatin Chhugani;Changkyu Kim;Nadathur Satish

  • Can traditional programming bridge the Ninja performance gap for parallel computing applications

    Nadathur Satish;Changkyu Kim;Jatin Chhugani;Hideki Saito

  • Efficient Parallelization of H.264 Decoding with Macro Block Level Scheduling

    Jike Chong;N. Satish;B. Catanzaro;K. Ravindran

  • GraphMat: High performance graph analytics made productive

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

Frequent Co-Authors

Pradeep Dubey
Pradeep Dubey Intel (United States)
Changkyu Kim
Changkyu Kim Facebook (United States)
Kurt Keutzer
Kurt Keutzer University of California, Berkeley
Mikhail Smelyanskiy
Mikhail Smelyanskiy Nvidia (United States)
Bryan Catanzaro
Bryan Catanzaro Nvidia (United States)
Eric Sedlar
Eric Sedlar Oracle (United States)
Tatiana Shpeisman
Tatiana Shpeisman Intel (United States)
Daniel J. Eisenstein
Daniel J. Eisenstein Harvard University

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