high performance computing on graphics processing units: hgpu.org

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Task-based FMM for heterogeneous architectures

Emmanuel Agullo, Berenger Bramas, Olivier Coulaud, Eric Darve, Matthias Messner, Toru Takahashi

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Tags: Computer science, CUDA, Fast multipole method, Heterogeneous systems, Numerical simulation, nVidia, Tesla M2070, Tesla M2090

April 9, 2014 by hgpu

A two-level task scheduler on Multiple DSP system for OpenCL

Li Tian, Cai Meng, Fugen Zhou

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Tags: Computer science, DSP, nVidia, OpenCL, Task scheduling

April 9, 2014 by hgpu

Bayesian Sparse Unsupervised Learning for Probit Models of Binary Data

Ari Pakman, Ben Shababo, Liam Paninski

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Tags: Bayesian, Computer science, CUDA, nVidia, Python

April 9, 2014 by hgpu

Reducing the Disk IO Bandwidth Bottleneck through Fast Floating Point Compression using Accelerators

Ajith Padyana, Devi Sudheer, Pallav Kumar Baruah, Ashok Srinivasan

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Tags: Algorithms, Cell processor, Compression, Computer science, CUDA, nVidia, Tesla M2050

April 9, 2014 by hgpu

Literature Review: Parallel Computing on linear equations of linear elastic FEM stimulation with CUDA

Zheng Xia

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Tags: Computer science, CUDA, Differential equations, nVidia, Partial differential equations, PDEs, Review

April 9, 2014 by hgpu

Exploring the power of GPU’s for training Deep Belief Networks

Vivek Kulkarni

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Tags: Computer science, CUDA, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 570

April 9, 2014 by hgpu

A New Non-Blocking Approach on GPU Dynamical Memory Management

Yu-Shiang Lin, Chun-Yuan Lin, Jon-Yu Lee

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Tags: Computer science, CUDA, Memory model, nVidia, Performance, Tesla K20

April 7, 2014 by hgpu

State of the Art Report on Real-time Rendering with Hardware Tessellation

Henry Schafer, Matthias Niessner, Benjamin Keinert, Marc Stamminger, Charles Loop

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Tags: 3D Graphics and Realism, Computer science, DirectX, Interactive application, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 780, Rendering, Tessellation

April 7, 2014 by hgpu

Quantifying the Energy Efficiency of Object Recognition and Optical Flow

Michael Anderson, Forrest Iandola, Kurt Keutzer

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Tags: Algorithms, ATI, ATI Radeon HD 7990, Computer science, Neural networks, OpenCL, Optical flow

April 7, 2014 by hgpu

GPU-Accelerated Face Detection Algorithm

T.A. Mahmoud Fayez

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Tags: Algorithms, Biometrics, Computer science, Computer vision, FPGA, nVidia, nVidia GeForce GTX 560, OpenCL

April 7, 2014 by hgpu

A Study on Efficient Application Mapping on Parallel Computing Accelerators

Keisuke Dohi

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Tags: Algorithms, Computer science, CUDA, FDTD, Finite-difference time-domain, FPGA, MPI, nVidia, nVidia GeForce GTX 295, Smith-Waterman algorithm, Thesis

April 7, 2014 by hgpu

Acceleration of a Full-scale Industrial CFD Application with OP2

Istvan Z. Reguly, Gihan R. Mudalige, Carlo Bertolli, Michael B. Giles, Adam Betts, Paul H. J. Kelly, David Radford

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Tags: Computer science, CUDA, Fluid dynamics, MPI, nVidia, Performance, Tesla K20

April 7, 2014 by hgpu

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

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

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

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high performance computing on graphics processing units: hgpu.org

Task-based FMM for heterogeneous architectures

A two-level task scheduler on Multiple DSP system for OpenCL

Bayesian Sparse Unsupervised Learning for Probit Models of Binary Data

Reducing the Disk IO Bandwidth Bottleneck through Fast Floating Point Compression using Accelerators

Literature Review: Parallel Computing on linear equations of linear elastic FEM stimulation with CUDA

Exploring the power of GPU’s for training Deep Belief Networks

A New Non-Blocking Approach on GPU Dynamical Memory Management

State of the Art Report on Real-time Rendering with Hardware Tessellation

Quantifying the Energy Efficiency of Object Recognition and Optical Flow

GPU-Accelerated Face Detection Algorithm

A Study on Efficient Application Mapping on Parallel Computing Accelerators

Acceleration of a Full-scale Industrial CFD Application with OP2

Recent source codes

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

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

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

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

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

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