high performance computing on graphics processing units: hgpu.org

hgpu.org » Tesla C2050

Minerals detection for hyperspectral images using adapted linear unmixing: LinMin

Schmidt Frederic, Legendre Maxime, Le Mouelic Stephane

View

Tags: Algorithms, Astrophysics, CUDA, Earth and Planetary Astrophysics, nVidia, Tesla C2050

February 12, 2014 by hgpu

Accurate Cross-Architecture Performance Modeling for Sparse Matrix-Vector Multiplication (SpMV) on GPUs

Ping Guo, Liqiang Wang

View

Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 295, Performance, Sparse matrix, Tesla C2050, Tesla K20, Tesla M2090

February 11, 2014 by hgpu

Genetically Improved CUDA C++ Software

W. B. Langdon, M. Harman

View

Tags: Computer science, CUDA, Genetic programming, nVidia, nVidia GeForce GTX 295, nVidia GeForce GTX 580, nVidia Quadro NVS 290, Package, Tesla C2050, Tesla K20, Tesla T10

February 11, 2014 by hgpu

GPU-based high-performance computing for radiation therapy

Xun Jia, Peter Ziegenhein, Steve B Jiang

View

Tags: CUDA, Medicine, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 295, nVidia GeForce GTX 480, nVidia GeForce GTX 570, nVidia GeForce GTX 580, nVidia GeForce GTX 590, Physics, Radiotherapy, Review, Tesla C1060, Tesla C2050, Tesla C2070

February 9, 2014 by hgpu

Auto-Generation and Auto-Tuning of 3D Stencil Codes on Homogeneous and Heterogeneous GPU Clusters

Yongpeng Zhang, Frank Mueller

View

Tags: Computer science, CUDA, GPU cluster, Heterogeneous systems, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, Optimization, Tesla C1060, Tesla C2050

February 5, 2014 by hgpu

A Scalable Hybrid FPGA/GPU FX Correlator

J. Kocz, L.J. Greenhill, B.R. Barsdell, G. Bernardi, A. Jameson, J. Craig, D. Price, G.B. Taylor, F. Schinzel, D. Werthimer

View

Tags: Astrophysics, CUDA, FPGA, Instrumentation and Methods for Astrophysics, nVidia, Signal processing, Tesla C2050

February 4, 2014 by hgpu

Impact of communication times on mixed CPU/GPU applications scheduling using KAAPI

David Beniamine

View

Tags: Computer science, CUDA, nVidia, nVidia Quadro NVS 300, Task scheduling, Tesla C2050, Thesis

January 29, 2014 by hgpu

GPU acceleration of Newton’s method for large systems of polynomial equations in double double and quad double arithmetic

Jan Verschelde, Xiangcheng Yu

View

Tags: Algorithms, CUDA, Extended precision, Linear Algebra, Mathematics, nVidia, Tesla C2050, Tesla K20

January 26, 2014 by hgpu

Efficient Parallel Video Processing Techniques on GPU: From Framework to Implementation

Huayou Su, Chunyuan Zhang, Mei Wen, Nan Wu

View

Tags: Algorithms, CUDA, H.264/AVC, Image processing, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 460, Tesla C2050, Video encoding

January 17, 2014 by hgpu

MRPB: Memory Request Prioritization for Massively Parallel Processors

Wenhao Jia, Kelly A. Shaw, Margaret Martonosi

View

Tags: Computer science, CUDA, GPGPU-sim, nVidia, Performance, Tesla C2050

January 16, 2014 by hgpu

Optimal Alignment of Three Sequences On A GPU

Junjie Li, Sanjay Ranka, Sartaj Sahni

View

Tags: Algorithms, Bioinformatics, Biology, CUDA, nVidia, Tesla C2050

January 14, 2014 by hgpu

GPUs for real-time processing in HEP trigger systems

R. Ammendola, A. Biagioni, L. Deri, M. Fiorini, O. Frezza, G. Lamanna, F. Lo Cicero, A. Lonardo, A. Messina, M. Sozzi, F. Pantaleo, P.S. Paolucci, D. Rossetti, F. Simula, L. Tosoratto, P. Vicini

View

Tags: CUDA, Data acquisition, High Energy Physics - Experiment, nVidia, nVidia GeForce GTX 680, Physics, Tesla C1060, Tesla C2050

January 14, 2014 by hgpu

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

GPU plugin for PySCF

Python-Based Quantum Chemistry Calculations with GPU Acceleration

QArray

QArray: a GPU-accelerated constant capacitance model simulator for large quantum dot arrays

Celerity: High-level C++ for Accelerator Clusters

Balancing Tracking Granularity and Parallelism in Many-Task Systems: The Horizons Approach

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

Parallel Gaussian process with kernel approximation in CUDA

See all packages

* * *

* * *

HGPU group © 2010-2024 hgpu.org

All rights belong to the respective authors

Login | Sitemap | Feedback | Policy

Contact us: