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NVIDIA cuda software and gpu parallel computing architecture

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David KirkAuthors Info & Claims

ISMM '07: Proceedings of the 6th international symposium on Memory management

Pages 103 - 104

https://doi.org/10.1145/1296907.1296909

Published: 21 October 2007 Publication History

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Abstract

In the past, graphics processors were special purpose hardwired application accelerators, suitable only for conventional rasterization-style graphics applications. Modern GPUs are now fully programmable, massively parallel floating point processors. This talk will describe NVIDIA's massively multithreaded computing architecture and CUDA software for GPU computing. The architecture is a scalable, highly parallel architecture that delivers high throughput for data-intensive processing. Although not truly general-purpose processors, GPUs can now be used for a wide variety of compute-intensive applications beyond graphic.

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Monopoli MBiondi MNannipieri PMoranti SFanucci L(2025)RADSAFiE: A Netlist-Level Fault Injection User Interface Application for FPGA-Based Digital SystemsIEEE Access10.1109/ACCESS.2025.353993213(28809-28823)Online publication date: 2025
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Mpouziotas DKarvelis PStylios C(2024)Advanced Computer Vision Methods for Tracking Wild Birds from Drone FootageDrones10.3390/drones80602598:6(259)Online publication date: 12-Jun-2024
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Index Terms

NVIDIA cuda software and gpu parallel computing architecture
1. Computing methodologies
  1. Computer graphics
    1. Graphics systems and interfaces
      1. Graphics processors
  2. Parallel computing methodologies

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Published In

ISMM '07: Proceedings of the 6th international symposium on Memory management

October 2007

192 pages

ISBN:9781595938930

DOI:10.1145/1296907

General Chair:
Greg Morrisett
Not supplied
,
Program Chair:
Mooly Sagiv
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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 October 2007

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multithreaded computing architecture

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ISMM07

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ISMM07: International Symposium on Memory Management

October 21 - 22, 2007

Quebec, Montreal, Canada

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Overall Acceptance Rate 72 of 156 submissions, 46%

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Condor JSpeierer SBode LBozic AGreen SDidyk PJarabo A(2025)Don't Splat your Gaussians: Volumetric Ray-Traced Primitives for Modeling and Rendering Scattering and Emissive MediaACM Transactions on Graphics10.1145/371185344:1(1-17)Online publication date: 3-Feb-2025
https://dl.acm.org/doi/10.1145/3711853
Monopoli MBiondi MNannipieri PMoranti SFanucci L(2025)RADSAFiE: A Netlist-Level Fault Injection User Interface Application for FPGA-Based Digital SystemsIEEE Access10.1109/ACCESS.2025.353993213(28809-28823)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3539932
Mpouziotas DKarvelis PStylios C(2024)Advanced Computer Vision Methods for Tracking Wild Birds from Drone FootageDrones10.3390/drones80602598:6(259)Online publication date: 12-Jun-2024
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Pennicard DRahmani VGraafsma H(2024)Data reduction and processing for photon science detectorsFrontiers in Physics10.3389/fphy.2024.128585412Online publication date: 5-Feb-2024
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Firsching MOttenweller MLeisner JRüger S(2024)X-ray transmission imaging of waste printed circuit boards for value estimation in recycling using machine learningWaste Management & Research: The Journal for a Sustainable Circular Economy10.1177/0734242X24125708442:9(759-766)Online publication date: 20-Jun-2024
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Stein CHellmuth CConty Estevez ALecocq PGritz L(2024)Spear: Across the Streaming Multiprocessors: Porting a Production Renderer to the GPUProceedings of the 2024 Digital Production Symposium10.1145/3665320.3670988(1-9)Online publication date: 24-Jul-2024
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He JLiu HWu YZheng ZZhu T(2024)A Preliminary Study on Accelerating Simulation Optimization With GPU Implementation2024 Winter Simulation Conference (WSC)10.1109/WSC63780.2024.10838919(3458-3469)Online publication date: 15-Dec-2024
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Malenza GCesare VSantimaria MBirke RVecchiato ABecciani UAldinucci M(2024)Performance portability via C++ PSTL, SYCL, OpenMP, and HIP: the Gaia AVU-GSR case studyProceedings of the SC '24 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis10.1109/SCW63240.2024.00157(1152-1163)Online publication date: 17-Nov-2024
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Scalable Parallel Programming with CUDA: Is CUDA the parallel programming model that application developers have been waiting for?

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