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Many-core GPU computing with NVIDIA CUDA

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Mark HarrisAuthors Info & Claims

ICS '08: Proceedings of the 22nd annual international conference on Supercomputing

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https://doi.org/10.1145/1375527.1375528

Published: 07 June 2008 Publication History

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Abstract

In the past, graphics processors were special-purpose hardwired application accelerators, suitable only for conventional graphics applications. Modern GPUs are fully programmable, massively parallel floating point processors. In this talk I will describe NVIDIA's scalable, highly parallel many-core GPU architecture and how CUDA software for GPU computing delivers high throughput for data-intensive processing. I will discuss how CUDA is reinvigorating research on data-parallel algorithms, reducing time to scientific discovery, and enabling a variety of compute-intensive industrial applications of GPUs beyond computer graphics.

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Park TKang SJang MKim SPark Y(2023)Orchestrating Large-Scale SpGEMMs using Dynamic Block Distribution and Data Transfer Minimization on Heterogeneous Systems2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00189(2456-2459)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00189
Zhang JZeng YStarly B(2021)Recurrent neural networks with long term temporal dependencies in machine tool wear diagnosis and prognosisSN Applied Sciences10.1007/s42452-021-04427-53:4Online publication date: 9-Mar-2021
https://doi.org/10.1007/s42452-021-04427-5
Cassavia NFlesca SIanni MMasciari EPulice C(2018)Distributed computing by leveraging and rewarding idling user resources from P2P networksJournal of Parallel and Distributed Computing10.1016/j.jpdc.2018.07.017122(81-94)Online publication date: Dec-2018
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Index Terms

Many-core GPU computing with NVIDIA CUDA
1. Computer systems organization
  1. Architectures
    1. Parallel architectures
2. Software and its engineering
  1. Software notations and tools
    1. General programming languages
      1. Language types

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Information & Contributors

Information

Published In

ICS '08: Proceedings of the 22nd annual international conference on Supercomputing

June 2008

390 pages

ISBN:9781605581583

DOI:10.1145/1375527

General Chairs:
Theo Papatheodorou
University of Patras, Greece
,
Utpal Banerjee
Intel (retired), USA
,
Program Chairs:
Avi Mendelson
Intel, Israel
,
Kyle Gallivan
Florida State University, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 June 2008

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Keynote

Conference

ICS08

Sponsor:

ICS08: International Conference on Supercomputing

June 7 - 12, 2008

Island of Kos, Greece

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Overall Acceptance Rate 629 of 2,180 submissions, 29%

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Cited By

View all

Park TKang SJang MKim SPark Y(2023)Orchestrating Large-Scale SpGEMMs using Dynamic Block Distribution and Data Transfer Minimization on Heterogeneous Systems2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00189(2456-2459)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00189
Zhang JZeng YStarly B(2021)Recurrent neural networks with long term temporal dependencies in machine tool wear diagnosis and prognosisSN Applied Sciences10.1007/s42452-021-04427-53:4Online publication date: 9-Mar-2021
https://doi.org/10.1007/s42452-021-04427-5
Cassavia NFlesca SIanni MMasciari EPulice C(2018)Distributed computing by leveraging and rewarding idling user resources from P2P networksJournal of Parallel and Distributed Computing10.1016/j.jpdc.2018.07.017122(81-94)Online publication date: Dec-2018
https://doi.org/10.1016/j.jpdc.2018.07.017
Cassavia NFlesca SIanni MMasciari EPapuzzo GPulice C(2018)High Performance Computing by the CrowdIntelligent Methods and Big Data in Industrial Applications10.1007/978-3-319-77604-0_7(91-101)Online publication date: 19-May-2018
https://doi.org/10.1007/978-3-319-77604-0_7
Rizki PLee HLee MOh S(2017)High-performance parallel approaches for three-dimensional light detection and ranging point clouds griddingJournal of Applied Remote Sensing10.1117/1.JRS.11.01601111:1(016011)Online publication date: 24-Jan-2017
https://doi.org/10.1117/1.JRS.11.016011
Cheng XFang LYang LCui S(2017)Mobile Big Data: The Fuel for Data-Driven WirelessIEEE Internet of Things Journal10.1109/JIOT.2017.27141894:5(1489-1516)Online publication date: Oct-2017
https://doi.org/10.1109/JIOT.2017.2714189
Paradiso VAmgarou KLanaute NSchoepff VAmoyal GMahe CBeltramello OLiénard E(2017)A panoramic coded aperture gamma camera for radioactive hotspots localizationJournal of Instrumentation10.1088/1748-0221/12/11/P1101012:11(P11010-P11010)Online publication date: 13-Nov-2017
https://doi.org/10.1088/1748-0221/12/11/P11010
Jannesari ATichy W(2014)Library-Independent Data Race DetectionIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2013.20925:10(2606-2616)Online publication date: Oct-2014
https://doi.org/10.1109/TPDS.2013.209
Hechtman BChe SHower DTian YBeckmann BHill MReinhardt SWood D(2014)QuickRelease: A throughput-oriented approach to release consistency on GPUs2014 IEEE 20th International Symposium on High Performance Computer Architecture (HPCA)10.1109/HPCA.2014.6835930(189-200)Online publication date: Mar-2014
https://doi.org/10.1109/HPCA.2014.6835930
Yang JFeng CZhao D(2013)A CUDA-based reverse gridding algorithm for MR reconstructionMagnetic Resonance Imaging10.1016/j.mri.2012.06.03831:2(313-323)Online publication date: Mar-2013
https://doi.org/10.1016/j.mri.2012.06.038
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