high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » Applications » Computer science

Texturing and Modeling, Third Edition: A Procedural Approach (The Morgan Kaufmann Series in Computer Graphics)

David S. Ebert, F. Kenton Musgrave, Darwyn Peachey, Ken Perlin, Steve Worley

View

Download (PDF)

Tags: 3D Graphics and Realism, Book, Computer science, nVidia

January 26, 2011 by hgpu

Weak execution ordering – exploiting iterative methods on many-core GPUs

Jianmin Chen, Zhuo Huang, Feiqi Su, Jih-Kwon Peir, Jeff Ho, Lu Peng

View

Download (PDF)

Tags: Computer vision, CUDA, Image processing, nVidia, Partial differential equations, PDEs, Tesla C1060

January 26, 2011 by hgpu

A Performance Study for Iterative Stencil Loops on GPUs with Ghost Zone Optimizations

Jiayuan Meng, Kevin Skadron

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Performance

January 26, 2011 by hgpu

Architectural Support for the Stream Execution Model on General-Purpose Processors

Jayanth Gummaraju, Mattan Erez, Joel Coburn, Mendel Rosenblum, William J. Dally

View

Download (PDF)

Tags: Algorithms, Architecture, Computer science, Performance

January 26, 2011 by hgpu

Hierarchical Agglomerative Clustering Using Graphics Processor with Compute Unified Device Architecture

S. A. Arul Shalom, Manoranjan Dash, Minh Tue, Nithin Wilson

Tags: Computer science, CUDA, GPU cluster, nVidia

January 26, 2011 by hgpu

Fast Schedulability Analysis Using Commodity Graphics Hardware

Jimin Feng, Samarjit Chakraborty, Bertil Schmidt, Weiguo Liu, Unmesh D. Bordoloi

View

Download (PDF)

Tags: Cg, Computer science, nVidia, nVidia GeForce 8800 GTX, OpenGL, Optimization

January 25, 2011 by hgpu

Parallel multiclass classification using SVMs on GPUs

Sergio H. Lopez, John R. Williams, Abel Sanchez

Tags: Computer science, Programming techniques

January 25, 2011 by hgpu

A New Era in Scientific Computing: Domain Decomposition Methods in Hybrid CPU-GPU Architectures

M. Papadrakakis, G. Stavroulakis, A. Karatarakis

Tags: Computational Engineering, Computer science, Finite difference, Finite element method

January 25, 2011 by hgpu

On the Visualization of Social and other Scale-Free Networks

Yuntao Jia, Jared Hoberock, Michael Garland, John C. Hart

View

Download (PDF)

Tags: Algorithms, Computer science, Visualization

January 24, 2011 by hgpu

MLS-based scalar fields over triangle meshes and their application in mesh processing

Jingyi Jin, Michael Garland, Edgar A. Ramos

Tags: 3D Graphics and Realism, Algorithms, Computer science, Triangular meshes

January 24, 2011 by hgpu

CUDASA: Compute Unified Device and Systems Architecture

M. Strengert, C. Muller, C. Dachsbacher, and T. Ertl

View

Download (PDF)

Tags: Computer science, CUDA, GPU cluster, High-level Languages, nVidia, nVidia GeForce 8800 GT, nVidia GeForce 8800 GTX, nVidia Quadro FX 5600

January 24, 2011 by hgpu

Exploiting More Parallelism from Applications Having Generalized Reductions on GPU Architectures

Xiao-Long Wu, Nady Obeid, Wen-Mei Hwu

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, nVidia, Optimization

January 24, 2011 by hgpu

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

Agentic Code Optimization via Compiler-LLM Cooperation

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

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

True 4-Bit Quantized CNN Training on CPU

True 4-Bit Quantized Convolutional Neural Network Training on CPU: Achieving Full-Precision Parity

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

Hunting CUDA Bugs at Scale with cuFuzz

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Applications

Texturing and Modeling, Third Edition: A Procedural Approach (The Morgan Kaufmann Series in Computer Graphics)

Weak execution ordering – exploiting iterative methods on many-core GPUs

A Performance Study for Iterative Stencil Loops on GPUs with Ghost Zone Optimizations

Architectural Support for the Stream Execution Model on General-Purpose Processors

Hierarchical Agglomerative Clustering Using Graphics Processor with Compute Unified Device Architecture

Fast Schedulability Analysis Using Commodity Graphics Hardware

Parallel multiclass classification using SVMs on GPUs

A New Era in Scientific Computing: Domain Decomposition Methods in Hybrid CPU-GPU Architectures

On the Visualization of Social and other Scale-Free Networks

MLS-based scalar fields over triangle meshes and their application in mesh processing

CUDASA: Compute Unified Device and Systems Architecture

Exploiting More Parallelism from Applications Having Generalized Reductions on GPU Architectures

Recent source codes

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Agentic Code Optimization via Compiler-LLM Cooperation

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

Device Virtual Machine (DVM)

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

Most viewed papers (last 30 days)