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GPU Pro 6: Advanced Rendering Techniques

Wolfgang Engel (editor)

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Tags: 3D Graphics and Realism, Book, Computer science, CUDA, DirectCompute, DirectX, Games, nVidia, OpenCL, OpenGL, Rendering, Visualization

August 12, 2015 by hgpu

Performance analysis of parallel gravitational N-body codes on large GPU cluster

Siyi Huang, Rainer Spurzem, Peter Berczik

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Tags: Astrophysics, CUDA, GPU cluster, Gravitation, Instrumentation and Methods for Astrophysics, N-body simulation, nVidia, Performance, Tesla M2050, Tesla M2070

August 12, 2015 by hgpu

Efficient Numerical Evaluation of Feynman Integral

Zhao Li, Jian Wang, Qi-Shu Yan, Xiaoran Zhao

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Tags: CUDA, High Energy Physics - Phenomenology, nVidia, Physics, QCD, Tesla K20

August 12, 2015 by hgpu

Optimizing strassen matrix multiply on GPUs

Ayaz ul Hasan Khan, Mayez Al-Mouhamed, Allam Fatayer

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Data parallelism, Matrix multiplication, nVidia, Tesla K20

August 11, 2015 by hgpu

GPU-Disasm: A GPU-based x86 Disassembler

Evangelos Ladakis, Giorgos Vasiliadis, Michalis Polychronakis, Sotiris Ioannidis, Georgios Portokalidis

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 770, Security

August 11, 2015 by hgpu

Places205-VGGNet Models for Scene Recognition

Limin Wang, Sheng Guo, Weilin Huang, Yu Qiao

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Tags: Caffe, Computer science, Computer vision, CUDA, Deep learning, nVidia, nVidia GeForce GTX Titan X, Package

August 10, 2015 by hgpu

Practical Algorithms for Finding Extremal Sets

Martin Marinov, Nicholas Nash, David Gregg

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Tags: Computer science, CUDA, Data Structures and Algorithms, nVidia, nVidia GeForce GTX 580, Package

August 10, 2015 by hgpu

CRINK: Automatic CUDA code generation for affine C programs

Akanksha Singh

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Tags: Code generation, Computer science, CUDA, nVidia, Tesla C1060, Thesis

August 10, 2015 by hgpu

DenseCut: Densely Connected CRFs for Realtime GrabCut

M. M. Cheng, V. A. Prisacariu, S. Zheng, P. H. S. Torr, C. Rother

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Tags: Computer vision, CUDA, Image processing, nVidia, Package, Tesla K40

August 7, 2015 by hgpu

Towards Distortion-Predictable Embedding of Neural Networks

Axel Angel

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Tags: Caffe, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, Package, Thesis

August 7, 2015 by hgpu

Optimising Reconfigurable Systems for Real-time Applications

Thomas Chun Pong Chau

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Tags: Algorithms, Computer science, CUDA, FPGA, Machine learning, Mixed precision, nVidia, Package, Tesla C2070, Thesis

August 7, 2015 by hgpu

Real-Time Pedestrian Detection With Deep Networks Cascades

Anelia Angelova, Alex Krizhevsky, Vincent Vanhoucke, Abhijit Ogale, Dave Ferguson

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Tags: Algorithms, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, Package, Tesla K20

August 6, 2015 by hgpu

Tutoring LLM into a Better CUDA Optimizer

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

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high performance computing on graphics processing units: hgpu.org

GPU Pro 6: Advanced Rendering Techniques

Performance analysis of parallel gravitational N-body codes on large GPU cluster

Efficient Numerical Evaluation of Feynman Integral

Optimizing strassen matrix multiply on GPUs

GPU-Disasm: A GPU-based x86 Disassembler

Places205-VGGNet Models for Scene Recognition

Practical Algorithms for Finding Extremal Sets

CRINK: Automatic CUDA code generation for affine C programs

DenseCut: Densely Connected CRFs for Realtime GrabCut

Towards Distortion-Predictable Embedding of Neural Networks

Optimising Reconfigurable Systems for Real-time Applications

Real-Time Pedestrian Detection With Deep Networks Cascades

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Atlas CLI: Machine Learning (ML) Lifecycle & Transparency Manager

transformers_tvm: Implementation of Encoder Decoder transformer on TVM

INT v.s. FP: A framework to compare low-bit integer and float-point formats

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NCCLX: collective communication framework

Tutoring LLM into a Better CUDA Optimizer

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Kernel Library for LLM Serving

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

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