high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » Applications » Computer science

Reducing overheads of dynamic scheduling on heterogeneous chips

Francisco Corbera, Andres Rodriguez, Rafael Asenjo, Angeles Navarro, Antonio Vilches, Maria J. Garzaran

View

Download (PDF)

Tags: Computer science, Heterogeneous systems, OpenCL, Operating systems

January 15, 2015 by hgpu

Batched Matrix Computations on Hardware Accelerators Based on GPUs

Azzam Haidar, Tingxing Dong, Piotr Luszczek, Stanimire Tomov, Jack Dongarra

View

Download (PDF)

Tags: Algorithms, Computer science, CUBLAS, CUDA, Factorization, nVidia, Tesla K40

January 15, 2015 by hgpu

High Performance GPU-based Fourier Volume Rendering

Marwan Abdellah, Ayman Eldeib, Amr Sharawi

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GT 640, nVidia Quadro K5000, OpenGL, Rendering, Visualization

January 15, 2015 by hgpu

Linear Performance-Breakdown Model: A Framework for GPU kernel programs performance analysis

Chapa Martell Mario Alberto, Hiroyuki Sato

View

Download (PDF)

Tags: Computer science, nVidia, nVidia GeForce GTX 660, OpenCL, Performance

January 13, 2015 by hgpu

Development of an Algorithm for Extracting Parallelism and Pipeline Structure from Stream-based Processing flow with Spanning Tree

Shinichi Yamagiwa, Guyue Wang, Koichi Wada

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, OpenCL, Tesla C2050

January 13, 2015 by hgpu

A Time Optimal Parallel Algorithm for the Dynamic Programming on the Hierarchical Memory Machine

Koji Nakano

View

Download (PDF)

Tags: Computer science, CUDA, Memory model, nVidia, nVidia GeForce GTX 680

January 13, 2015 by hgpu

Thorough Evaluation of GPU Shared Memory Load and Store Instructions

Satoshi Okamoto, Yasuaki Ito, Koji Nakano, Jacir L. Bordim

View

Download (PDF)

Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 780 Ti, Performance, PTX

January 13, 2015 by hgpu

A Survey Of Architectural Approaches for Managing Embedded DRAM and Non-volatile On-chip Caches

Sparsh Mittal; Jeffrey S. Vetter; Dong Li

View

Download (PDF)

Tags: cache, Compiler, cpu, Energy-efficient computing, GPU, Hardware Architecture, Memory model

January 12, 2015 by sparsh0mittal

Face Recognition: A Tutorial on Computational Aspects

Alexander Alling, Nathaniel Powers, Tolga Soyata

View

Download (PDF)

Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 760, nVidia GeForce GTX 780, Tutorial

January 10, 2015 by hgpu

Dynamic Feature-Adaptive Subdivision

H. Schafer, J. Raab, B. Keinert, M. Meyer, M. Stamminger, M. Niessner

View

Download (PDF)

Tags: Computational Geometry, Computer science, DirectX, Interactive application, nVidia, nVidia GeForce GTX 980, Rendering

January 10, 2015 by hgpu

Exploring GPU Memory Performance Using Digital Image Processing Algorithms

Puya Memarzia, Farshad Khunjush

View

Download (PDF)

Tags: Benchmarking, Computer science, CUDA, Image processing, nVidia, nVidia GeForce GTX 570

January 10, 2015 by hgpu

Image Super-Resolution Using Deep Convolutional Networks

Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang

View

Download (PDF)

Source codes

Tags: CNN, Computer science, CUDA, Machine learning, Neural networks, nVidia, Package

January 10, 2015 by hgpu

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

hls4ml: Machine learning on FPGAs using HLS

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

ThunderKittens: Tile primitives for speedy kernels

ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels

NVIDIA Nemotron Parse 1.1

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

Iris: First-Class Multi-GPU Programming Experience in Triton

HipKittens: Fast and Furious AMD Kernels

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Applications

Reducing overheads of dynamic scheduling on heterogeneous chips

Batched Matrix Computations on Hardware Accelerators Based on GPUs

High Performance GPU-based Fourier Volume Rendering

Linear Performance-Breakdown Model: A Framework for GPU kernel programs performance analysis

Development of an Algorithm for Extracting Parallelism and Pipeline Structure from Stream-based Processing flow with Spanning Tree

A Time Optimal Parallel Algorithm for the Dynamic Programming on the Hierarchical Memory Machine

Thorough Evaluation of GPU Shared Memory Load and Store Instructions

A Survey Of Architectural Approaches for Managing Embedded DRAM and Non-volatile On-chip Caches

Face Recognition: A Tutorial on Computational Aspects

Dynamic Feature-Adaptive Subdivision

Exploring GPU Memory Performance Using Digital Image Processing Algorithms

Image Super-Resolution Using Deep Convolutional Networks

Recent source codes

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

hls4ml: Machine learning on FPGAs using HLS

ThunderKittens: Tile primitives for speedy kernels

NVIDIA Nemotron Parse 1.1

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

mt4g: Memory Topology 4 GPUs

Falcon: GPU-Based Floating-point Adaptive Lossless Compression

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

Most viewed papers (last 30 days)