high performance computing on graphics processing units: hgpu.org

hgpu.org » nVidia

GPU-Disasm: A GPU-based x86 Disassembler

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

View

Download (PDF)

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

View

Download (PDF)

Source codes

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

View

Download (PDF)

Source codes

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

View

Download (PDF)

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

August 10, 2015 by hgpu

Visual, Spatial and Temporal Quality in Video-Based Reconstruction of People: Achieving, Prototyping and Evaluating

Robert Andrew Aspin

View

Download (PDF)

Tags: Image processing, nVidia, nVidia GeForce 8800 GTX, OpenGL, Rendering, Tessellation, 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

View

Download (PDF)

Source codes

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

August 7, 2015 by hgpu

Towards Distortion-Predictable Embedding of Neural Networks

Axel Angel

View

Download (PDF)

Source codes

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

View

Download (PDF)

Source codes

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

August 7, 2015 by hgpu

Behavioral Spherical Harmonics for Long-Range Agents’ Interaction

Biagio Cosenza

View

Download (PDF)

Tags: Algorithms, Computer science, nVidia, nVidia GeForce GTX 960, OpenCL, OpenGL

August 7, 2015 by hgpu

Real-Time Pedestrian Detection With Deep Networks Cascades

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

View

Download (PDF)

Source codes

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

August 6, 2015 by hgpu

Exploiting two-level parallelism by aggregating computing resources in task-based applications over accelerator-based machines

Terry Cojean, Abdou Guermouche, Andra Hugo, Raymond Namyst, Pierre-Andre Wacrenier

View

Download (PDF)

Tags: Computer science, CUDA, Factorization, Heterogeneous systems, Linear Algebra, nVidia, OpenMP, Task scheduling, Tesla K40

August 5, 2015 by hgpu

Semantic Pose using Deep Networks Trained on Synthetic RGB-D

Jeremie Papon, Markus Schoeler

View

Download (PDF)

Tags: CNN, Computer science, Computer vision, Deep learning, Neural networks, nVidia, nVidia GeForce GTX Titan X, Rendering

August 5, 2015 by hgpu

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

Agentic Code Optimization via Compiler-LLM Cooperation

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

* * *

high performance computing on graphics processing units: hgpu.org

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

Visual, Spatial and Temporal Quality in Video-Based Reconstruction of People: Achieving, Prototyping and Evaluating

DenseCut: Densely Connected CRFs for Realtime GrabCut

Towards Distortion-Predictable Embedding of Neural Networks

Optimising Reconfigurable Systems for Real-time Applications

Behavioral Spherical Harmonics for Long-Range Agents’ Interaction

Real-Time Pedestrian Detection With Deep Networks Cascades

Exploiting two-level parallelism by aggregating computing resources in task-based applications over accelerator-based machines

Semantic Pose using Deep Networks Trained on Synthetic RGB-D

Recent source codes

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

Agentic Code Optimization via Compiler-LLM Cooperation

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

True 4-Bit Quantized CNN Training on CPU

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

Most viewed papers (last 30 days)