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Piko : A Framework for

Piko : A Framework for

  • eatsyouc
  • 90 Slides

Authoring Programmable . Graphics . Pipelines. Anj...

RASFX: A VLBI GPU-based software

RASFX: A VLBI GPU-based software

  • medshair
  • 32 Slides

correlator. Voytsekh Ken. Igor . Surkis. Dmitry Pa...

CS/EE 217 GPU Architecture and Parallel Programming

CS/EE 217 GPU Architecture and Parallel Programming

  • mojartd
  • 10 Slides

Midterm Review. Material on exam. Lectures 1 to . ...

Acceleration of Frequent

Acceleration of Frequent

  • welnews
  • 22 Slides

Itemset. Mining on FPGA Using . SDAccel. and . V...

RIDOS Simona Giordanengo

RIDOS Simona Giordanengo

  • shangmaxi
  • 5 Slides

INFN Torino. Comparison. FFP1 «. measured. » dos...

Energy-Efficient Data Compression for Modern Memory Systems

Energy-Efficient Data Compression for Modern Memory Systems

  • katrgolden
  • 13 Slides

Gennady Pekhimenko. ACM Student Research Competiti...

CS 179 Lecture 6 Synchronization, Matrix Transpose,

CS 179 Lecture 6 Synchronization, Matrix Transpose,

  • mastervisa
  • 29 Slides

Profiling, AWS Cluster. Synchronization. Ideal cas...

State of CyberGIS Shaowen Wang

State of CyberGIS Shaowen Wang

  • dailyno
  • 38 Slides

CyberInfrastructure and Geospatial Information Lab...

CUDA - 101 Basics Overview

CUDA - 101 Basics Overview

  • broadcastworld
  • 27 Slides

What is CUDA?. Data Parallelism. Host-Device model...

Student: Petra Loncar FESB, University

Student: Petra Loncar FESB, University

  • maxasp
  • 10 Slides

of. Split. Performance. . comparison. for NVIDI...

PA0 – Hello CUDA CUDA Compute Unified Device Architecture

PA0 – Hello CUDA CUDA Compute Unified Device Architecture

  • bubbleba
  • 15 Slides

Defines much more than an API. A language . Hardwa...

Triggering Events with GPUs at ATLAS

Triggering Events with GPUs at ATLAS

  • bikersphobia
  • 18 Slides

S. Kama. , . J. Augusto . Soares. , J. . Baines, M...

A Scalable Heterogeneous Parallelization Framework for

A Scalable Heterogeneous Parallelization Framework for

  • pinperc
  • 19 Slides

Iterative Local Searches. Martin . Burtscher. 1. ...

A Scalable Heterogeneous Parallelization Framework for

A Scalable Heterogeneous Parallelization Framework for

  • mercynaybor
  • 19 Slides

Iterative Local Searches. Martin . Burtscher. 1. ...

Heterogeneous Computing at USC Dept. of Computer Science and Engineering University of South Carolina

Heterogeneous Computing at USC Dept. of Computer Science and Engineering University of South Carolina

  • vizettan
  • 15 Slides

Dept. of Computer Science and Engineering. Univers...

Aho-Corasick String Mataching

Aho-Corasick String Mataching

  • leventiser
  • 39 Slides

on Shared and Distributed-Memory Parallel Architec...

corestoaGraphicsProcessingUnit(GPU),andweusetheGPUtorunourracedetectio

corestoaGraphicsProcessingUnit(GPU),andweusetheGPUtorunourracedetectio

  • messide
  • 6 Slides

AlgorithmRecordEvent(e)(Executedbyapplicationthrea...

CTO, SK Telecom

CTO, SK Telecom

  • oneill
  • 7 Slides

“NUGU ” SK Telecom AI Assistant 2017. 2. 27...

29 2018 Silicon Valley

29 2018 Silicon Valley

  • eleanor
  • 41 Slides

March 26 - * CUDA Learning Environment Steven Dalt...

MODALITES DE RECLAMATION DES FACTURES AU GROUPE PUBLIC UNIFIE

MODALITES DE RECLAMATION DES FACTURES AU GROUPE PUBLIC UNIFIE

  • victoria
  • 2 Slides

SERVICE ASSISTANCE FACTURES FOURNISSEURS MARS 2020...

Genome alignment Usman Roshan

Genome alignment Usman Roshan

  • okelly
  • 11 Slides

Applications. Genome sequencing on the rise. Whole...

Intelligent trigger for Hyper-K

Intelligent trigger for Hyper-K

  • julia
  • 22 Slides

Akitaka Ariga. University of Bern, Switzerland. Re...

GPU and machine learning solutions for comparative genomics

GPU and machine learning solutions for comparative genomics

  • CottonTails
  • 26 Slides

Usman Roshan. Department of Computer Science. New ...

Training DNNs with up to 5x less memory via optimal tensor

Training DNNs with up to 5x less memory via optimal tensor

  • SunnySeahorse
  • 30 Slides

rematerialization. Paras Jain. Joint work with: . ...

Nov 19, 2018 Xi Chen University of Kentucky

Nov 19, 2018 Xi Chen University of Kentucky

  • AngelEyes
  • 20 Slides

Lexington, Kentucky . Gregory Gutmann . Tokyo Inst...

[DOWLOAD]-GPU Zen: Advanced Rendering Techniques

[DOWLOAD]-GPU Zen: Advanced Rendering Techniques

  • janoahjamaahl
  • 100 Slides

The Desired Brand Effect Stand Out in a Saturated ...

[PDF]-Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA: Effective techniques for processing complex image data in real time using GPUs

[PDF]-Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA: Effective techniques for processing complex image data in real time using GPUs

  • janoahjamaahl
  • 100 Slides

The Desired Brand Effect Stand Out in a Saturated ...

[BEST]-GPU Zen 2: Advanced Rendering Techniques

[BEST]-GPU Zen 2: Advanced Rendering Techniques

  • janoahjamaahl
  • 100 Slides

The Desired Brand Effect Stand Out in a Saturated ...

[READING BOOK]-GPU Zen: Advanced Rendering Techniques

[READING BOOK]-GPU Zen: Advanced Rendering Techniques

  • gersontaniish
  • 100 Slides

The Desired Brand Effect Stand Out in a Saturated ...

[PDF]-GPU Zen 2: Advanced Rendering Techniques

[PDF]-GPU Zen 2: Advanced Rendering Techniques

  • kinnickbladen
  • 100 Slides

The Desired Brand Effect Stand Out in a Saturated ...

1 Lecture: 3D CNNs, Gradient Compression

1 Lecture: 3D CNNs, Gradient Compression

  • ella
  • 12 Slides

Topics: . Diffy. , Morph, Gradient Compression. 3D...

Barbara Chapman October 13, 2022

Barbara Chapman October 13, 2022

  • vivian
  • 4 Slides

Performance portability: An implementer’s Perspe...

CS 732: Advance Machine Learning

CS 732: Advance Machine Learning

  • jade
  • 9 Slides

Usman Roshan. Department of Computer Science. NJIT...

Tingjun Yang, Maria Acosta, Phil Harris, Ben Hawks, Burt Holzman, Jeff Krupa, Kevin Pedro,

Tingjun Yang, Maria Acosta, Phil Harris, Ben Hawks, Burt Holzman, Jeff Krupa, Kevin Pedro,

  • finley
  • 23 Slides

Nhan. Tran, Mike Wang. Neutrino Physics and Machi...

CPUs, GPUs, accelerators and memory

CPUs, GPUs, accelerators and memory

  • joanne
  • 34 Slides

Andrea Sciabà. On behalf of the Technology Watch ...

A Software Defined Radio for CCSDS 131.2-B protocol: exploiting Graphics Processing Unit accelerato

A Software Defined Radio for CCSDS 131.2-B protocol: exploiting Graphics Processing Unit accelerato

  • belinda
  • 33 Slides

Roberto Ciardi. , Gianluca Giuffrida, Matteo Berto...

Chimera: Collaborative Preemption for Multitasking on a Shared GPU

Chimera: Collaborative Preemption for Multitasking on a Shared GPU

  • holly
  • 28 Slides

Jason Jong Kyu Park. 1. , . Yongjun. Park. 2. , a...

CULZSS LZSS Lossless Data Compression on CUDA

CULZSS LZSS Lossless Data Compression on CUDA

  • bety
  • 25 Slides

Adnan. . Ozsoy. & Martin . Swany. DAMSL - D...

Implementing Hierarchical Bayesian Networks on the GPU

Implementing Hierarchical Bayesian Networks on the GPU

  • maisie
  • 18 Slides

László Dobos, Tamás Budavári, Carrie Filion,. ...

Visual Annotation of Clinically Important Anatomical Landmarks for

Visual Annotation of Clinically Important Anatomical Landmarks for

  • patricia
  • 21 Slides

VitreoRetinal. Surgery . Vincent Ng. Xin. Yuan W...

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