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hgpu.org » Applications » Computer science

Deep Convolutional Neural Networks for Smile Recognition

Patrick O. Glauner

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Tags: Computer science, CUDA, Deep learning, Neural and Evolutionary Computing, Neural networks, nVidia, Tesla K40, Thesis

August 28, 2015 by hgpu

A Parallel Algorithm to Test Chordality of Graphs

Agnieszka Lupinska

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Tags: Computer science, CUDA, Data Structures and Algorithms, Graph theory, nVidia, nVidia GeForce GTX 560 Ti, Thesis

August 28, 2015 by hgpu

Accelerated Deep Learning using Intel Xeon Phi

Andre Viebke

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Tags: Computer science, Computer vision, Deep learning, Intel Xeon Phi, Machine learning, Neural networks, OpenMP, Thesis

August 27, 2015 by hgpu

CudaChain: A Practical GPU-accelerated 2D Convex Hull Algorithm

Gang Mei

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Tags: Computational Geometry, Computer science, CUDA, nVidia, nVidia GeForce GT 640, nVidia GeForce GTX 660 M, Sorting

August 27, 2015 by hgpu

gScan: Accelerating Graham Scan on the GPU

Gang Mei

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Tags: Algorithms, Computational Geometry, Computer science, CUDA, Filtering, nVidia, nVidia GeForce GT 640, nVidia GeForce GTX 660 M, Sorting

August 27, 2015 by hgpu

MemcachedGPU: Scaling-up Scale-out Key-value Stores

Tayler H. Hetherington, Mike O'Connor, Tor M. Aamodt

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Tags: Computer science, CUDA, Databases, Energy-efficient computing, nVidia, nVidia GeForce GTX 750 Ti, Package, Tesla K20, Tesla K40

August 27, 2015 by hgpu

Performance Evaluations of Document-Oriented Databases using GPU and Cache Structure

Shin Morishima, Hiroki Matsutani

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Tags: Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX 980, Performance

August 24, 2015 by hgpu

Viability of Feature Detection on Sony Xperia Z3 using OpenCL

Max Danielsson, Thomas Sievert

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Tags: Android, Computer science, Computer vision, Embedded high-performance computing, nVidia, nVidia GeForce GTX 660, OpenCL, Package, Thesis

August 24, 2015 by hgpu

Scheduling for new computing platforms with GPUs

Florence Monna

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Tags: Computer science, Data Structures and Algorithms, nVidia, OpenCL, Task scheduling, Tesla C2050, Thesis

August 24, 2015 by hgpu

Source-to-Source Automatic Program Transformations for GPU-like Hardware Accelerators

Mehdi Amini

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Tags: ATI, ATI Radeon HD 6970, Code generation, Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 670, OpenCL, Tesla C1060, Tesla C2070, Thesis

August 24, 2015 by hgpu

Semi-Global Filtering of Airborne LiDAR Data for Fast Extraction of Digital Terrain Models

Xiangyun Hu, Lizhi Ye, Shiyan Pang, Jie Shan

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Tags: Algorithms, Computer science, CUDA, Filtering, nVidia, nVidia GeForce GTX 690, Sensing

August 24, 2015 by hgpu

A CPU and GPU Heterogeneous Processing of Multimedia Data by using OpenCL

Heegon Kim, Sungju Lee, Yongwha Chung, Daihee Park

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Tags: Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 560, OpenCL

August 21, 2015 by hgpu

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

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

MSKernelBench & CUDAMaster

Making LLMs Optimize Multi-Scenario CUDA Kernels Like Experts

EvoScientist: Harness Vibe Research with Self-evolving AI Scientists

EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build & Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build & Test Pipeline of Code Repositories on ANY Language and ANY Platform

CONCUR: a benchmark designed to evaluate multithreaded Java code generated by LLMs

CONCUR: Benchmarking LLMs for Concurrent Code Generation

HIPRT: Ray Tracing using HIP

Ray Tracing using HIP

MXFP4 Training Support Codebase

Practical FP4 Training for Large-Scale MoE Models on Hopper GPUs

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high performance computing on graphics processing units: hgpu.org

Applications

Deep Convolutional Neural Networks for Smile Recognition

A Parallel Algorithm to Test Chordality of Graphs

Accelerated Deep Learning using Intel Xeon Phi

CudaChain: A Practical GPU-accelerated 2D Convex Hull Algorithm

gScan: Accelerating Graham Scan on the GPU

MemcachedGPU: Scaling-up Scale-out Key-value Stores

Performance Evaluations of Document-Oriented Databases using GPU and Cache Structure

Viability of Feature Detection on Sony Xperia Z3 using OpenCL

Scheduling for new computing platforms with GPUs

Source-to-Source Automatic Program Transformations for GPU-like Hardware Accelerators

Semi-Global Filtering of Airborne LiDAR Data for Fast Extraction of Digital Terrain Models

A CPU and GPU Heterogeneous Processing of Multimedia Data by using OpenCL

Recent source codes

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

MSKernelBench & CUDAMaster

EvoScientist: Harness Vibe Research with Self-evolving AI Scientists

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

CONCUR: a benchmark designed to evaluate multithreaded Java code generated by LLMs

HIPRT: Ray Tracing using HIP

MXFP4 Training Support Codebase

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