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hgpu.org » Chaotic Dynamics

Christopher D. Marcotte, Roman O. Grigoriev

Tags: Chaotic Dynamics, Computational Physics, Nonlinear Sciences, nVidia, nVidia GeForce GTX 680, OpenCL, Partial differential equations, PDEs, Physics

September 9, 2013 by hgpu

Saurabh Maniktala, Anisha Goel, A. B. Patki, R. C. Meharde

October 24, 2012 by hgpu

Scott A. Sarra, Clyde Meador

Tags: Chaotic Dynamics, CUDA, Extended precision, nVidia, nVidia GeForce GTX 280, ODEs, Ordinary differential equations, Package, Physics

December 15, 2011 by hgpu

A. Seibert, S. Denisov, Alexey V. Ponomarev, P. Hanggi

Tags: Chaotic Dynamics, Computational Physics, CUDA, Nonlinear Sciences, nVidia, Physics, Tesla M2050

December 3, 2011 by hgpu

J.R. Seaton, J.C Sprott

Tags: Algorithms, Chaotic Dynamics, Differential equations, Nonlinear Sciences, nVidia, Partial differential equations, PDEs, Physics, Tesla C870

December 1, 2011 by hgpu

Karsten Ahnert, Mario Mulansky

Tags: Chaotic Dynamics, Computational Physics, Computer science, CUDA, Differential equations, Mathematical Software, nVidia, ODEs, Ordinary differential equations, Package

October 18, 2011 by hgpu

A. Seibert, S. Denisov, Alexey V. Ponomarev, P. Hanggi

Tags: Chaotic Dynamics, Computational Physics, CUDA, Nonlinear Sciences, nVidia, Physics, Tesla M2050

July 22, 2011 by hgpu

* * *

- Efficient softmax approximation for GPUs
- Benchmarking State-of-the-Art Deep Learning Software Tools
- Massively parallel simulations of relativistic fluid dynamics on graphics processing units with CUDA
- 3D Object Recognition with Convolutional Neural Networks
- DeepPy: Pythonic deep learning
- cf4ocl: a C framework for OpenCL
- Deep Learning on FPGAs
- Exploring Task Parallelism for Heterogeneous Systems Using Multicore Task Management API
- A Performance Model and Optimization Strategies for Automatic GPU Code Generation of PDE Systems Described by a Domain-Specific Language
- DawnCC: a Source-to-Source Automatic Parallelizer of C and C++ Programs

* * *

Optimizing Performance of Recurrent Neural Networks on GPUs

CMA-ES for Hyperparameter Optimization of Deep Neural Networks

A smooth particle hydrodynamics code to model collisions between solid, self-gravitating objects

CUED-RNNLM - An Open-Source Toolkit for Efficient Training and Evaluation of Recurrent Neural Network Language Models

Parallel Programming Models for Dense Linear Algebra on Heterogeneous Systems

Fluid Simulation by the Smoothed Particle Hydrodynamics Method: A Survey

Algorithmic and Software System Support to Accelerate Data Processing in CPU-GPU Hybrid Computing Environments

GPIC - GPU Power Iteration Cluster

Towards Path Tracing in Games

Faster and Cheaper: Parallelizing Large-Scale Matrix Factorization on GPUs

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