Apache Hama

Sunday June 14, 2015

Apache Hama announces v0.7 Release!

Apache Hama team is pleased to announce the release of Hama v0.7 with new features and improvements.

Hama is a High-Performance BSP computing engine, which can be used to perform compute-intensive general scientific BSP applications, Google’s Pregel-like graph applications, and machine learning algorithms.

What are the major changes from the last release?

The important new feature of this release is that support the Mesos and Yet Another Resource Negotiator (YARN), so you’re able to submit your BSP applications to the existing open source and enterprise clusters e.g., CDH, HDP, and Mesosphere without any installation. In addition, we reinforced machine learning package by adding algorithms such as Max-Flow, K-Core, ANN, ..., etc.

There are also big improvements in the queue and messaging systems. We now use own outgoing/incoming message manager instead of using Java's built-in queues. It stores messages in serialized form in a set of bundles (or a single bundle) to reduce the memory usage and RPC overhead. Unsafe serialization is used to serialize Vertex and its message objects more quickly. Another important improvement is the enhanced graph package. Instead of sending each message individually, we package the messages per vertex and send a packaged message to their assigned destination nodes. With this we achieved significant improvement in the performance of graph applications. The attached benchmarks were done to test scalability and performance of PageRank algorithm for random generated 1 billion edges graph using Apache Hama and Giraph on Amazon EMR 30 nodes cluster. Note that the aggregators was used for detecting the convergence condition in case of Apache Hama.




What’s Next?

After a month of testing and benchmarking this version will bring substantial performance improvements together with important bug fixes which significantly improve the platform stability. We look forward to add more and more and see our community grow. The primary objective of the technical plans are:

  • Add stream input format for listening messages coming from 3rd party applications, and incremental learning algorithms.
  • Improve reliability of system e.g., fault tolerance, HA, ..., etc.
  • More machine learning algorithms, such as ensemble classifier, SVM, DNN, ..., etc

Where I can download it?

The release artifacts are published and ready for you to download either from the Apache mirrors or from the Maven repository. We welcome your help, feedback, and suggestions. For more information on how to report problems, and to get involved, visit the Hama project website[1] and wiki[2].

[1]. Apache Hama Website: https://hama.apache.org/
[2]. Apache Hama Wiki: https://wiki.apache.org/hama/

Thursday March 05, 2015

Apache Hama now supports YARN, runs at Samsung Electronics

The Apache Hama team is pleased to announce that we’re now supporting not only the Mesos but the YARN (Thanks to Minho Kim who is a main contributor of YARN module).

Apache Hama is a High-Performance BSP computing engine, which can be used to perform compute-intensive general scientific BSP applications, Google’s Pregel-like graph applications, and machine learning algorithms.

YARN is the resource management technology that lets multiple computing frameworks run on the same Hadoop cluster using the same underlying storage. So, for example, a company could analyze the data using MapReduce, Spark, and Apache Hama.

“From the next release, you’ll be able to submit scientific BSP applications to the existing open source Hadoop, CDH, and HDP clusters without any installation” said Edward J. Yoon(@eddieyoon), a original creator of Apache Hama.

Meanwhile, we’re also working on support the HPC environment such as InfiniBand and GPUs — According to General Dynamics[1], they already proved the 10x performance improvement of Apache Hama on HPC cluster — and also plan to support deployment and automation configurations to the Hybrid Clouds for solving various problems of Manufacturing Engineering, Science, Finance, Research areas.

This contribution is mainly coming from Samsung Electronics. “Unlike most web services companies, our challenge is numerical or signal data, not text data. That’s why we’re investing in High-Performance computing for scientific advanced analytics.” said SeungHun Jeon, a Head of Cloud Tech Lab at Samsung Electronics.

“Since we build our own analytics platform in the Cloud by leveraging open source technologies such as Apache Hadoop, Storm, and Hama, we intend to keep making contributions to the Open Source communities. ” added Hyok S. Choi, a Principal Software Engineer at Samsung Electronics.

About Apache Hama

Apache Hama[2] was established in 2012 as a Top-Level Project of The Apache Software Foundation. It provides High-Performance BSP[3] computing engine on top of Hadoop.

1. http://www.gd-ais.com/News/General-Dynamics-at-SC14-Delivering-Real-time-Intelligence-with-High-Performance-Data-Analytics
2. http://hama.apache.org/
3. http://en.wikipedia.org/wiki/Bulk_synchronous_parallel

Wednesday March 05, 2014

[ANNOUNCE] Hama 0.6.4 has been released.

The Hama team is pleased to announce the Hama 0.6.4 release.

Apache Hama is a pure BSP (Bulk Synchronous Parallel) computing framework on top of HDFS (Hadoop Distributed File System) for massive scientific computations such as matrix, graph and network algorithms.

This release improves memory usage by 3 times better than before (without significant performance degradation) and adds runtime message compression.

The artifacts are published and ready for you to download[1] either from the Apache mirrors or from the Maven repository. We welcome your help, feedback, and suggestions. For more information on how to report problems, and to get involved, visit the project website[2] and wiki[3].

Thanks.

1. http://www.apache.org/dist/hama/
2. http://hama.apache.org
3. http://wiki.apache.org/hama/

Friday July 06, 2012

Apache Hama 0.5.0 Released

The Apache Hama PMC is pleased to announce the release of Apache Hama 0.5.0.

Apache Hama is a BSP (Bulk Synchronous Parallel) computing framework
on top of HDFS (Hadoop Distributed File System) for massive scientific
computations such as matrix, graph and network algorithms.

This release is the first release as a top level project, contains two
significant new features (Message Compressor, complete clone of the
Google's Pregel) and many improvements for computing system
performance and durability.

The artifacts are published and ready for you to download[1] either
from the Apache mirrors or from the Maven repository. For more
details, please take a look at our website[2] and wiki[3].

Many thanks to the Hama community for making this release possible.

1. http://www.apache.org/dist/hama/
2. http://hama.apache.org
3. http://wiki.apache.org/hama/

Tuesday March 06, 2012

Apache Hama 0.4-incubating Released!

Hi all,

The Hama team is pleased to announce the release of Apache Hama 0.4-incubating under the Apache Incubator.

Hama is a pure BSP(Bulk Synchronous Parallel) computing framework on top of HDFS (Hadoop Distributed File System) for massive scientific computations such as matrix, graph and network algorithms.

This release includes:

* Multiple tasks per node
* Input/Output Formatter
* Stabilized Barrier Synchronization
* Message Combiners
* Improved examples
* and its Benchmark test results

Thanks to the Hama and Apache Incubating community for helping grow the project!

Sunday July 31, 2011

Apache Hama 0.3-incubating Released!

Hi all,

The Hama team is pleased to announce the release of Apache Hama
0.3-incubating under the Apache Incubator.

Hama is a distributed computing framework based on BSP (Bulk
Synchronous Parallel)[1] computing techniques for massive scientific
computations.

This release includes:

  • Added LocalBSPRunner
  • Added web UI for BSP cluster and job monitoring
  • Added more practical examples e.g., Shortest Path Problem[2], PageRank[3]
  • Performance has improved with BSPMessageBundle
  • Switched from Ant to Maven

You can be downloaded from the download page of Hama website[4].

Thanks to the Hama and Apache Incubating community for helping grow the project.

1. http://en.wikipedia.org/wiki/Bulk_synchronous_parallel
2. http://wiki.apache.org/hama/SSSP
3. http://wiki.apache.org/hama/PageRank
4. http://incubator.apache.org/hama/downloads.html

Friday June 03, 2011

Apache Hama 0.2.0-incubating Released!

The Hama team is pleased to announce the release of Apache Hama 0.2.0-incubating under the Apache Incubator.

Hama is a distributed computing framework based on BSP (Bulk Synchronous Parallel) computing techniques for massive scientific computations.

This first release includes:

  • BSP computing framework and its examples
  • CLI-based managing and monitoring tool of BSP job

You can be downloaded from the download page of Hama website[2].

Thanks to the Hama and Apache Incubating community for helping grow the project.

1. http://en.wikipedia.org/wiki/Bulk_synchronous_parallel
2. http://incubator.apache.org/hama/downloads.html

Monday August 02, 2010

Apache Hama in academic paper.

Abstract—APPLICATION. Various scientific computations have become so complex, and thus computation tools play an important role. In this paper, we explore the state-of-the-art framework providing high-level matrix computation primitives with MapReduce through the case study approach, and demon-strate these primitives with different computation engines to show the performance and scalability. We believe the opportunity for using MapReduce in scientific computation is even more promising than the success to date in the parallel systems literature.

http://csl.skku.edu/papers/CS-TR-2010-330.pdf

Thursday July 15, 2010

How will Hama BSP different from Pregel?

Firstly, why did we use HBase?

Until last year, we tried to implement the distributed matrix/graph computing algorithms based on Map/Reduce.

As you know, the Hadoop is consists of HDFS, which is designed for commodity servers as a shared nothing model (also termed as data partitioning model), and a distributed programming model called Map/Reduce. The Map/Reduce is a high-performance parallel data processing engine, to be sure, but it's not good for complex numerical/relational processing requires huge iterations or inter-node communications. So, we used HBase as a shared storage (shared memory model).

Why BSP instead of Map/Reduce and HBase?

However, there were still problems as below:

  • OS overhead of running shared storage software (HBase)
  • The limitation of HBase faculty (especially, a size of column qualifier)
  • Growth of code complexity

Therefore, we started to consider about message-passing model, and decided to adopt the BSP (Bulk Synchronous Parallel) model, inspired by Pregel from Google Research Blog.

What's the Pregel?

According to my understanding, Pregel is graph-specific: a large-scale graph computing framework, based on BSP model.

How will Hama BSP different from Pregel?

Hama BSP is a computing engine, based on BSP model, like a Pregel, and it'll be compatible with existing HDFS cluster, or any FileSystem and Database in the future. However, we believe that the BSP computing model is not limited to a problems of graph; it can be used for widely distributed software such as Map/Reduce. In addition to a field of graph, there are many other algorithms, which have similar problems with graph processing using Map/Reduce. Actually, the BSP model has been researched for many years in the field of matrix computation, too.

Therefore, we're trying to implement more generalized BSP computing solution. And, the Hama will consists of the BSP computing engine, and a set of few examples (e.g., matrix inversion, pagerank, BFS, ..., etc).

Learn about Hama by reading the documentation.

Friday April 30, 2010

We're introduced in the BSP Worldwide.

We're introduced in the BSP Worldwide : http://www.bsp-worldwide.org/bspww3000.html via Prof. Rob Bisseling.

Dear Edward,

I have put a link from  http://www.bsp-worldwide.org/bspww3000.html 
to your page and to the paper. 

I read it and find it very interesting. 
I heard a talk by Greg Malewicz from Google (Pregel) who is very enthousiastic about BSP. 
Suddenly I note a high interest in BSP everywhere.

I wish you good luck with your project.

best wishes,
Rob

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