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Wiki Markup\[http://wiki.apache.org/hama Hama \] is a parallel matrix computational package, which provides an library of matrix operations for the large-scale processing development environment and \[http://wiki.apache.org/hadoop/HadoopMapReduce Map/Reduce\] framework for the large-scale Numerical Analysis and Data Mining, which need the intensive computation power of matrix inversion, e.g. linear regression, PCA, SVM and etc. It will be also useful for many scientific applications, e.g. physics computations, linear algebra, computational fluid dynamics, statistics, graphic rendering and many more. a distributed computing framework based on BSP (Bulk Synchronous Parallel) computing techniques for massive scientific computations (e.g., matrix, graph, network, ..., etc).

General Information

• Hama Official Website
• Hama Official Blog
• Presentations and Articles about Hama and BSP
• PoweredBy, a list of sites and applications powered by Hama
• Hama Architecture
  • Input Partitioning
  • Graph Module
• BSP Programming Model
• Performance Benchmarks
  

User Documentation

• Getting Started with Hama
  • CompatibilityTable
  • Configuration Properties
• Getting Started with Hama on Mesos
• Getting Started with Hama on YARN
• Running Hama over InfiniBand
• Hama Pipes (Native C/C++ BSP Bridge)
• Build Dynamic Graphs on Hama
• Hama Aggregators
• Command Line Interfaces for Hama shell script.
• How to debug your own Applications
• Configuring third party JAR with native library
• FAQ list
• Hama Streaming
• Examples
  • Serialize Printing Of "Hello BSP"
  • BSP based Pi Estimator
  • Single Source Shortest Paths
  • Pagerank
  • SemiClustering
  • KMeans Clustering
• Advanced Documentation
  • Synchronization Service
  • Messaging Service
  • Develop your own BSP
    

Developer Documentation

• Guide for Hama Contributors
  • How To Become A Committer
• Guide for Hama Committers
• Guide for Hama PMC memers
• Roadmap, listing release plans
• Building, Testing, CI
• How to release
• Developer FAQ
• Hama Streaming Protocol
• Guidelines provides information that developers can follow.
  

Machine Learning

• Supervised
• Semi-Supervised Learning
• Unsupervised
  

New Sub-project Proposals

• Guidelines for Sub-project Proposal