high performance computing on graphics processing units: hgpu.org

hgpu.org » Performance

High-Performance Monte Carlo Radiosity on GPU based on Scene Partitioning

Jose R. Sanjurjo, Margarita Amor, Montserrat Boo, Ramon Doallo

Tags: Algorithms, Computer science, Monte Carlo simulation, Performance

June 16, 2011 by hgpu

A Reconfigurable Processor for Phylogenetic Inference

Pei Liu, Ahmed Hemani, Kolin Paul

Tags: Computer science, Electronic design automation, FPGA, Performance

June 15, 2011 by hgpu

Full-resolution interactive CPU volume rendering with coherent BVH traversal

A. Knoll, S. Thelen, I. Wald, C.D. Hansen, H. Hagen, M.E. Papka

Tags: Computer science, Performance, Rendering

June 15, 2011 by hgpu

In Situ Power Analysis of General Purpose Graphical Processing Units

M.Z. Shaikh, M. Gregoire, W. Li, M. Wroblewski, S. Simon

Tags: Computer science, CUDA, Energy-efficient computing, nVidia, Performance

June 14, 2011 by hgpu

A comparative study of GPU programming models and architectures using neural networks

Vivek Pallipuram, Mohammad Bhuiyan, Melissa Smith

Tags: ATI, Computer science, CUDA, Neural networks, nVidia, OpenCL, Performance, Tesla C2050

June 14, 2011 by hgpu

Accelerating Parameter Sweep Applications Using CUDA

Masaya Motokubota, Fumihiko Ino, Kenichi Hagihara

Tags: Computer science, CUDA, Memory level parallelism, nVidia, Performance

June 14, 2011 by hgpu

Massively LDPC Decoding on Multicore Architectures

Gabriel Falcao, Leonel Sousa, Vitor Silva

Tags: Cell processor, Computer science, CUDA, nVidia, Parity-check codes, Performance

June 10, 2011 by hgpu

Accelerating Multi-layer Perceptron based short term demand forecasting using Graphics Processing Units

Ting He, ZhaoYang Dong, Ke Meng, Hua Wang, Y.T. Oh

Tags: Computer science, Performance, Power Systems Simulation

June 10, 2011 by hgpu

Parallel multi-level analytical global placement on graphics processing units

Jason Cong, Yi Zou

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Tags: Algorithms, CAD, Computer science, CUDA, nVidia, Performance, Tesla C1060

June 9, 2011 by hgpu

Dynamic load balancing on heterogeneous multicore/multiGPU systems

Alejandro Acosta, Robert Corujo, Vicente Blanco, Francisco Almeida

Tags: Computer science, GPU cluster, Heterogeneous systems, Performance, Task scheduling

June 8, 2011 by hgpu

Exploiting Computing Power on Graphics Processing Unit

Ziyi Liu, Wenjing Ma

Tags: Algorithms, Computer science, CUDA, Memory model, nVidia, Performance

June 5, 2011 by hgpu

Efficient Embarrassingly Parallel on Graphics Processor Unit

Chunye Gong, Jie Liu, Jin Qin, Qingfeng Hu, Zhenghu Gong

Tags: Benchmarking, Computer science, CUDA, nVidia, Performance, Pseudo-random number generators, Tesla T10

June 5, 2011 by hgpu

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

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

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

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

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

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high performance computing on graphics processing units: hgpu.org

High-Performance Monte Carlo Radiosity on GPU based on Scene Partitioning

A Reconfigurable Processor for Phylogenetic Inference

Full-resolution interactive CPU volume rendering with coherent BVH traversal

In Situ Power Analysis of General Purpose Graphical Processing Units

A comparative study of GPU programming models and architectures using neural networks

Accelerating Parameter Sweep Applications Using CUDA

Massively LDPC Decoding on Multicore Architectures

Accelerating Multi-layer Perceptron based short term demand forecasting using Graphics Processing Units

Parallel multi-level analytical global placement on graphics processing units

Dynamic load balancing on heterogeneous multicore/multiGPU systems

Exploiting Computing Power on Graphics Processing Unit

Efficient Embarrassingly Parallel on Graphics Processor Unit

Recent source codes

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

IntelliKit: Agent-first tooling for AMD hardware

DITRON: Distributed Compiler based on Triton for Parallel Systems

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

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

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

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