high performance computing on graphics processing units: hgpu.org

hgpu.org » Performance

OpenACC-based Snow Simulation

Magnus Alvestad Mikalsen

View

Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, OpenACC, OpenCL, Performance, Tesla C2070, Tesla K20, Thesis

October 18, 2013 by hgpu

Understanding and Modeling the Synchronization Cost in the GPU Architecture

James T. Letendre

View

Download (PDF)

Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, Performance, Tesla C2075, Thesis

October 17, 2013 by hgpu

Empirical performance modeling of GPU kernels using active learning

Prasanna Balaprakash, Karl Rupp, Azamat Mametjanov, Robert B. Gramacy, Paul D. Hovland, Stefan M. Wild

View

Download (PDF)

Tags: Algorithms, ATI, ATI Radeon HD 7970, Computer science, CUDA, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 470, OpenCL, Performance, Tesla C2050, Tesla K20

October 17, 2013 by hgpu

Performance Comparison of GPU, DSP and FPGA implementations of image processing and computer vision algorithms in embedded systems

Egil Fykse

View

Download (PDF)

Source codes

Tags: Benchmarking, Computer science, Computer vision, CUDA, DSP, FPGA, Image processing, nVidia, nVidia GeForce GTX 660 Ti, nVidia Quadro 1000 M, OpenCV, Performance, Thesis

October 15, 2013 by hgpu

Performance Analysis of a Large Memory Application on Multiple Architectures

P.M.Kogge

View

Download (PDF)

Tags: Benchmarking, Heterogeneous systems, nVidia, nVidia Tesla, Performance

October 9, 2013 by hgpu

GPU-TLS: An Efficient Runtime for Speculative Loop Parallelization on GPUs

Chenggang Zhang, Guodong Han, Cho-Li Wang

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 580, Performance, Tesla M2050

September 27, 2013 by hgpu

Java with Auto-Parallelization on Graphics Coprocessing Architecture

Guodong Han, Chenggang Zhang, King Tin Lam, Cho-Li Wang

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Java, nVidia, Performance, Tesla M2050

September 27, 2013 by hgpu

Evaluation of autoparallelization toolkits for commodity graphics hardware

David Williams, Valeriu Codreanu, Po Yang, Baoquan Liu, Feng Dong, Burhan Yasar, Babak Mahdian, Alessandro Chiarini, Xia Zhao, Jos B.T.M. Roerdink

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 680, OpenACC, Performance

September 24, 2013 by hgpu

Optimization solutions for the segmented sum algorithmic function

Alexandru Pirjan

View

Download (PDF)

Tags: Computer science, CUDA, Memory, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, nVidia GeForce GTX 680, Performance

September 21, 2013 by hgpu

Optimal Configuration of GPU Cache Memory to Maximize the Performance

Leonid Djinevski, Sime Arsenovski, Sasko Ristov, Marjan Gusev

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Matrix multiplication, Memory, nVidia, nVidia GeForce GTX 680, Performance

September 16, 2013 by hgpu

On the Performance and Energy-efficiency of Multi-core SIMD CPUs and CUDA-enabled GPUs

Ronald Duarte, Resit Sendag, Frederick J. Vetter

View

Download (PDF)

Tags: Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 580, Performance

September 16, 2013 by hgpu

Algorithmic GPGPU Memory Optimization

Byunghyun Jang, Minsu Choi, Kyung Ki Kim

View

Download (PDF)

Tags: Algorithms, Computer science, Heterogeneous systems, Memory model, nVidia, nVidia GeForce GTX 285, OpenCL, Performance

September 15, 2013 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

OpenACC-based Snow Simulation

Understanding and Modeling the Synchronization Cost in the GPU Architecture

Empirical performance modeling of GPU kernels using active learning

Performance Comparison of GPU, DSP and FPGA implementations of image processing and computer vision algorithms in embedded systems

Performance Analysis of a Large Memory Application on Multiple Architectures

GPU-TLS: An Efficient Runtime for Speculative Loop Parallelization on GPUs

Java with Auto-Parallelization on Graphics Coprocessing Architecture

Evaluation of autoparallelization toolkits for commodity graphics hardware

Optimization solutions for the segmented sum algorithmic function

Optimal Configuration of GPU Cache Memory to Maximize the Performance

On the Performance and Energy-efficiency of Multi-core SIMD CPUs and CUDA-enabled GPUs

Algorithmic GPGPU Memory Optimization

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Most viewed papers (last 30 days)