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neurokernel

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  Analyzed about 21 hours ago

The Neurokernel Project aims to build an open software platform for the emulation of the entire brain of the fruit fly Drosophila melanogaster on multiple Graphics Processing Units (GPUs).

52.2K lines of code

0 current contributors

about 1 month since last commit

1 users on Open Hub

Very Low Activity
5.0
 
0 Reviews
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Mostly written in Python
Licenses: BSD-3-Clause
Tags CUDA GPU neuroscience python simulation

MXNet

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Claimed by Apache Software Foundation Analyzed about 4 hours ago

Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, and more MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix the flavours of symbolic programming and ... [More] imperative programming together to maximize the efficiency and your productivity. In its core, a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer is build on top, which makes symbolic execution fast and memory efficient. The library is portable and lightweight, and is ready scales to multiple GPUs, and multiple machines. [Less]

424K lines of code

208 current contributors

almost 3 years since last commit

1 users on Open Hub

Inactive
0.0
 
0 Reviews
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Mostly written in C++
Licenses: apache_2
Tags bigdata cloud_computing cluster cpu CUDA deep_learning deep_neural_networks distributed efficiency flow_graphs framework gpu 8 more...

GPUE

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  Analyzed about 18 hours ago

GPU enabled Gross-Pitaevskii Equation numerical solver for rapidly rotating Bose-Einstein condensates.

3.95K lines of code

0 current contributors

over 8 years since last commit

1 users on Open Hub

Inactive
0.0
 
0 Reviews
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Mostly written in C++
Licenses: BSD-3-Clause
Tags bec c++ condensate CUDA fft gpe gpgpu gpu gpucomputing hpc numerical numericalsimulations 6 more...

scikit-cuda

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  Analyzed about 1 hour ago

scikit-cuda provides Python interfaces to many of the functions in the CUDA device/runtime, CUBLAS, CUFFT, and CUSOLVER libraries distributed as part of NVIDIA's CUDA Programming Toolkit, as well as interfaces to select functions in the free and standard versions of the CULA Dense Toolkit. Both ... [More] low-level wrapper functions similar to their C counterparts and high-level functions comparable to those in NumPy and Scipy are provided. [Less]

20.6K lines of code

3 current contributors

about 2 years since last commit

1 users on Open Hub

Inactive
0.0
 
0 Reviews
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Mostly written in Python
Licenses: BSD-3-Clause
Tags CUDA GPU numerical_computing nvidia python

cuda_dsmc

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  Analyzed about 2 hours ago

CUDA based implementation of G A Bird's DSMC algorithm.

19.7K lines of code

0 current contributors

over 8 years since last commit

1 users on Open Hub

Inactive
0.0
 
0 Reviews
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Mostly written in C++
Licenses: No declared licenses
Tags CUDA DSMC

Savant

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  No analysis available

Python Computer Vision & Video Analytics Framework With Batteries Included

0 lines of code

0 current contributors

0 since last commit

0 users on Open Hub

Activity Not Available
0.0
 
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Mostly written in language not available
Licenses: apache_2
Tags artificial_intelligence artificialintelligence computervision CUDA deep-learning deep_learning deeplearning deepstream machinelearning neuralnetworks objecttracking opencv 3 more...

incubator-singa

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  Analyzed about 17 hours ago

SINGA is a general distributed deep learning platform for training big deep learning models over large datasets. It is designed with an intuitive programming model based on the layer abstraction. A variety of popular deep learning models are supported, namely feed-forward models including ... [More] convolutional neural networks (CNN), energy models like restricted Boltzmann machine (RBM), and recurrent neural networks (RNN). Many built-in layers are provided for users. SINGA architecture is sufficiently flexible to run synchronous, asynchronous and hybrid training frameworks. SINGA also supports different neural net partitioning schemes to parallelize the training of large models, namely partitioning on batch dimension, feature dimension or hybrid partitioning. [Less]

94.3K lines of code

28 current contributors

7 months since last commit

0 users on Open Hub

Low Activity
0.0
 
0 Reviews
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Mostly written in C++
Licenses: apache_2
Tags artificial_neural_networks cluster CNN CUDA cudnn deep_learning distributed gpu large_datasets large_scale machine_learning multiple_gpu 5 more...

IDSIA Brainstorm

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  Analyzed about 10 hours ago

Fast, flexible and fun neural networks. Combining lessons from previous projects with new design elements, and written entirely in Python, Brainstorm has been designed to work on multiple platforms with multiple computing backends.

11.8K lines of code

0 current contributors

over 8 years since last commit

0 users on Open Hub

Inactive
0.0
 
0 Reviews
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Mostly written in Python
Licenses: mit
Tags artificial_neural_networks CUDA deep_learning highway_neural_networks machine_learning python

GPU-RSCode

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  Analyzed about 8 hours ago

Accelerate Reed-Solomon coding for Fault-Tolerance in RAID-like system

20.7K lines of code

0 current contributors

about 8 years since last commit

0 users on Open Hub

Inactive
0.0
 
0 Reviews
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Mostly written in C
Licenses: No declared licenses
Tags C++ CUDA