high performance computing on graphics processing units: hgpu.org

hgpu.org » nVidia GeForce 7300 GT

A performance/cost evaluation for a GPU-based drug discovery application on volunteer computing

Gines David Guerrero Hernandez, Baldomero Imbernon, Horacio Perez-Sanchez, Francisco Sanz, Jose M. Garcia, Jose M. Cecilia

View

Tags: Bioinformatics, Biology, CUDA, Medicine, nVidia, nVidia GeForce 7300 GT, nVidia GeForce GTX 465, nVidia GeForce GTX 480, nVidia GeForce GTX 590, Tesla C2070, Tesla K20

May 21, 2014 by hgpu

Tabu Search on GPU

Adam Janiak, Wladyslaw A. Janiak, Maciej Lichtenstein

View

Tags: Algorithms, Computer science, DirectX, HLSL, nVidia, nVidia GeForce 7300 GT, nVidia GeForce 8600 GT, Search

December 23, 2010 by hgpu

SolidTextureNets

On Demand Solid Texture Synthesis Using Deep 3D Networks

md_poly: A Performance-Portable Polyhedral Compiler Based on Multi-Dimensional Homomorphisms

md_poly: A Performance-Portable Polyhedral Compiler Based on Multi-Dimensional Homomorphisms

deepnetts: Deep Netts Pure Java Deep Learning Engine

Towards High Performance Java-based Deep Learning Frameworks

PASTA: A Parallel Sparse Tensor Algorithm Benchmark Suite

A Parallel Sparse Tensor Benchmark Suite on CPUs and GPUs

TACO: The Tensor Algebra Compiler (taco) computes sparse tensor expressions on CPUs and GPUs

A Unified Iteration Space Transformation Framework for Sparse and Dense Tensor Algebra

OpenCL-FPGA-LLVM: LLVM-based automation of memory decoupling for OpenCL applications on FPGAs

LLVM-based automation of memory decoupling for OpenCL applications on FPGAs

CUDAnative.jl: Julia support for native CUDA programming

Abstractions for Programming Graphics Processors in High-Level Programming Languages

GPU explicit-state model checking

Porting tree-based hash table compression to GPGPU model checking

OCLADock: OpenCL Accelerated Molecular Docking

Accelerating Molecular Docking by Parallelized Heterogeneous Computing – A Case Study of Performance, Quality of Results, and Energy-Efficiency using CPUs, GPUs, and FPGAs

jax-md: Differentiable, Hardware Accelerated, Molecular Dynamics

JAX, M.D.: End-to-End Differentiable, Hardware Accelerated, Molecular Dynamics in Pure Python

See all packages

* * *

* * *

* * *

2020

April

27-29

Munich, Germany

8th International Workshop on OpenCL, including SYCLCon, 2019

2019

May

13-15

Boston, USA

7th International Workshop on OpenCL, 2019

2019

May

13

Boston, MA, USA

Distributed Heterogeneous Programming in C/C++ (DHPCC++), 2019

2019

May

14

Larnaca, Cyprus

3rd International Workshop on Theoretical Approaches to Performance Evaluation, Modeling and Simulation (TAPEMS), 2019

2019

July

26-28

Rome, Italy

International Conference on Frontiers of Neural Networks (ICFNN), 2019

+

HGPU group © 2010-2020 hgpu.org

All rights belong to the respective authors

Login | Sitemap | Feedback | Policy

Contact us: