Open Hub
Black Duck Open Hub

Tags : Browse Projects

Select a tag to browse associated projects and drill deeper into the tag cloud.

Sensei DB

Compare

  Analyzed about 18 hours ago

Distributed, realtime, semi-structured, searchable database. * Full-text search * Fast realtime updates while maintaining high query performance * Structured and faceted search * complex queries via a query language (BQL) and a REST/JSON api * Streaming updates from different Gateways such as ... [More] JDBC, JMS, and Kafka * Fast key-value lookup * High performance under concurrent heavy update and query volumes * Hadoop integration, e.g. Map-Reduce job to batch build index and push to Sensei clusters * Ability plug-in custom and complex faceting logic such as the social graph. [Less]

96K lines of code

0 current contributors

over 9 years since last commit

1 users on Open Hub

Inactive
0.0
 
0 Reviews
I Use This
Mostly written in Java
Licenses: apache_2
Tags cluster database distributed fulltext_search gateway high_performance java json key_value linkedin mapreduce realtime 3 more...

dpark

Compare

  Analyzed about 2 hours ago

Python clone of Spark, a MapReduce alike framework in Python

20K lines of code

2 current contributors

over 3 years since last commit

1 users on Open Hub

Inactive
0.0
 
0 Reviews
I Use This
Mostly written in Python
Licenses: No declared licenses
Tags bigdata cluster clustering distributed distributed_computing mapreduce parallel python

incubator-singa

Compare

  Analyzed about 13 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]

87.6K lines of code

28 current contributors

about 1 month since last commit

0 users on Open Hub

Moderate Activity
0.0
 
0 Reviews
I Use This
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...

Media Encoding Cluster

Compare

  No analysis available

Media Encoding Cluster this is the first Open Source, Plattform Independent Video Encoding Cluster currently for Linux and Windows

0 lines of code

0 current contributors

0 since last commit

0 users on Open Hub

Activity Not Available
5.0
 
0 Reviews
I Use This
Mostly written in language not available
Licenses: gpl
Tags audio c c++ cluster distributed encoding erlang ftpclient ftpserver media otp video 2 more...

The Ray Parallel Genome Assembler

Compare

  Analyzed about 3 hours ago

Ray assembler Ray is a parallel de novo genome assembler that utilises the message-passing interface everywhere and is implemented using peer-to-peer communication. Ray is implemented using the Ray Platform, a message-passing-interface programming framework. Ray is documented in ... [More] Documentation/ (many files) MANUAL_PAGE.txt (command-line options, same as Ray -help) README.md (general) INSTALL.txt (quick installation) Solutions (all bundled in a single Product called Ray) de novo genome assembly (original Ray Technology, re-branded to géno-Ray Technology) quantification of contig abundance (con-Ray Technology) de novo transcriptome assembly (trans-Ray Technology) quantification of transcript expression (express-Ray Technology) de novo meta-genome assembly (méta-Ray Technology) quantificati [Less]

41.2K lines of code

0 current contributors

over 6 years since last commit

0 users on Open Hub

Inactive
5.0
 
0 Reviews
I Use This
Mostly written in C++
Licenses: gpl3
Tags assembly cluster distributed genomics metagenomics mpi reads scalable sequences

rcl

Compare

  No analysis available

0 lines of code

0 current contributors

0 since last commit

0 users on Open Hub

Activity Not Available
0.0
 
0 Reviews
I Use This
Mostly written in language not available
Licenses: gpl3
Tags cluster clustering distributed distributed_computing gpgpu opencl

dlock-manager

Compare

  Analyzed about 10 hours ago

Distributed lock manager

1.29K lines of code

0 current contributors

over 7 years since last commit

0 users on Open Hub

Inactive
0.0
 
0 Reviews
I Use This
Mostly written in Go
Licenses: No declared licenses
Tags cluster distributed lock management tools

diqube

Compare

  Analyzed about 16 hours ago

diqube is a fast, distributed, in-memory column-store which enables you to analyze large amounts of read-only data easily

64.7K lines of code

0 current contributors

over 6 years since last commit

0 users on Open Hub

Inactive
0.0
 
0 Reviews
I Use This
Mostly written in Java
Licenses: AGPL3
Tags analytical bigdata cluster column-oriented column_oriented distributed in-memory in_memory interactive query

Apache Tez

Compare

Claimed by Apache Software Foundation Analyzed about 5 hours ago

Mirror of Apache Tez

222K lines of code

13 current contributors

16 days since last commit

0 users on Open Hub

Low Activity
5.0
 
0 Reviews
I Use This
Mostly written in Java
Licenses: apache_2
Tags apache bigdata cluster dag distributed distributed-computing distributed_computing hdfs java mapreduce sql tez 1 more...

veles

Compare

  Analyzed about 8 hours ago

Distributed machine learning platform. Distributed platform for rapid Deep learning application development Consists of: Platform - https://github.com/Samsung/veles Znicz Plugin - Neural Network engine Mastodon - Veles Java bridge for Hadoop etc. SoundFeatureExtraction - audio feature ... [More] extraction library Written on Python, uses OpenCL or CUDA, employs Flow-Based Programming, under Apache 2.0. 1 Deploy VELES on Notebook or Cluster with a single command 2 Create the model from >250 optimized units 3 Analyze and serve the dataset on the go using Loaders 4 Train it on PC or High Performance Cluster Interactively monitor the training process 5 Publish the results 6 Automatically extract the trained model as an application 7 Run it in the cloud [Less]

68.8K lines of code

0 current contributors

5 months since last commit

0 users on Open Hub

Very Low Activity
0.0
 
0 Reviews
I Use This
Mostly written in Python
Licenses: apache_2
Tags cloud cluster cuda deployment distributed gpu hadoop hpc neural_networks opencl production python 1 more...