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Analysis of GPU Parallel Computing based on Matlab

Mingzhe Wang, Bo Wang, Qiu He, Xiuxiu Liu, Kunshuai Zhu

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GT 540 M, Performance

May 28, 2015 by hgpu

DjiNN and Tonic: DNN as a Service and Its Implications for Future Warehouse Scale Computers

Johann Hauswald, Yiping Kang, Michael A. Laurenzano, Quan Chen, Cheng Li, Trevor Mudge, Ronald G. Dreslinski, Jason Mars, Lingjia Tang

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Tags: CUDA, Deep learning, Image processing, Machine learning, Neural networks, nVidia, Speech recognition, Tesla K40

May 25, 2015 by hgpu

On the Choice of Tensor Estimation for Corner Detection, Optical Flow and Denoising

Freddie Astrom, Michael Felsberg

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Tags: CUDA, Image processing, nVidia, nVidia GeForce GTX 670, OpenGL, Optical flow

May 25, 2015 by hgpu

ACCTuner: OpenACC Auto-Tuner For Accelerated Scientific Applications

Fatemah Ramzy AlZayer

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Tags: Algorithms, Computer science, Machine learning, Nearest neighbour, nVidia, OpenACC, Tesla K20, Tesla K40, Thesis

May 25, 2015 by hgpu

Solving incompressible Navier-Stokes equations on heterogeneous parallel architectures

Yushan Wang

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Tags: CUDA, Fluid dynamics, Heterogeneous systems, MPI, Navier-Stokes equations, NSEs, nVidia, Tesla K20, Thesis

May 25, 2015 by hgpu

Key derivation functions and their GPU implementation

Ondrej Mosnacek

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Tags: Benchmarking, Computer science, nVidia, OpenCL, Package, Security, Tesla K20, Thesis

May 22, 2015 by hgpu

Parallel and Improved PageRank Algorithm for GPU-CPU Collaborative Environment

Prasann Choudhari, Eikshith Baikampadi, Paresh Patil, Sanket Gadekar

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Tags: Algorithms, Computer science, CUDA, nVidia

May 22, 2015 by hgpu

Accelerating SWHE based PIRs using GPUs

Wei Dai, Yarkin Doroz, Berk Sunar

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Tags: Computer science, CUDA, Information Retrieval, nVidia, nVidia GeForce GTX 690, Security

May 22, 2015 by hgpu

A Performance and Scalability Analysis of the Tsunami Simulation EasyWave for Different Multi-Core Architectures and Programming Models

Steffen Christgau, Johannes Spazier, Bettina Schnor

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Tags: CUDA, Earth and Space Sciences, Geoscience, Intel Xeon Phi, MPI, nVidia, OpenMP, Package, Performance, Tesla K40

May 20, 2015 by hgpu

Physically Based Rendering: Implementation of Path Tracer

Vinh Truong

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Tags: 3D Graphics and Realism, Computer science, nVidia, nVidia GeForce GTX 560 Ti, OpenCL, OpenGL, Raytracing, Rendering, Thesis

May 20, 2015 by hgpu

U-Net: Convolutional Networks for Biomedical Image Segmentation

Olaf Ronneberger, Philipp Fischer, Thomas Brox

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Tags: CNN, Computer science, CUDA, Deep learning, Image processing, Medicine, Microscopy, Neural networks, nVidia, nVidia GeForce GTX Titan, Package

May 20, 2015 by hgpu

An Efficient, Automatic Approach to High Performance Heterogeneous Computing

Gordon Inggs, David B. Thomas, Wayne Luk

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Tags: AMD FirePro W5000, ATI, Computational finance, Computer science, Finance, FPGA, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia Grid K2, nVidia Quadro K4000, OpenCL, Option pricing, Performance

May 20, 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

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high performance computing on graphics processing units: hgpu.org

Analysis of GPU Parallel Computing based on Matlab

DjiNN and Tonic: DNN as a Service and Its Implications for Future Warehouse Scale Computers

On the Choice of Tensor Estimation for Corner Detection, Optical Flow and Denoising

ACCTuner: OpenACC Auto-Tuner For Accelerated Scientific Applications

Solving incompressible Navier-Stokes equations on heterogeneous parallel architectures

Key derivation functions and their GPU implementation

Parallel and Improved PageRank Algorithm for GPU-CPU Collaborative Environment

Accelerating SWHE based PIRs using GPUs

A Performance and Scalability Analysis of the Tsunami Simulation EasyWave for Different Multi-Core Architectures and Programming Models

Physically Based Rendering: Implementation of Path Tracer

U-Net: Convolutional Networks for Biomedical Image Segmentation

An Efficient, Automatic Approach to High Performance Heterogeneous Computing

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

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