Hadoop isn’t just for Web 2.0 Big Data Anymore. Hadoop for HPC.
In 2004 Google released a white paper on their use of the MapReduce framework to perform fast and reliable executions of similar processes / data transformations & queries at terabyte scale. Yahoo then began the Hadoop project to support their search product. As a result of this, Apache elevated Hadoop, their MapReduce and DFS (distributed file management system) initiative out of Nutch, their open source search project.
Although technically Hadoop is still in pre-release 1.0, it has proven to be stable and useful for Big Data web 2.0 applications. When you are using Google, LinkedIn, Facebook, Twitter and Yahoo! you are running on Hadoop.
What about Hadoop for High Performance Computing with scientific applications? It certainly has its place and a basic understanding of Hadoop helps you to understand where you can take advantage of Hadoop in HPC.
Firstly, what is MapReduce? MapReduce is a methodology of performing parallel computations on very large volumes of data , by dividing the workload across a large number of similar machines, called ‘nodes’. Map Reduce methodology enables linear scalability through good data and file management. Additionally, Map Reduce differs from other methodologies in that it relies on nodes which are servers with attendant disk storage. Work is allocated to these storage server nodes based upon where the data is, as opposed to moving data to where processing occurs. This dramatically accelerates applications which process Big Data sets.
With Map – Reduce, you ‘map’ your input data to the type of output you desire using some function that is replicable. For instance in manipulating strings by substituting a space for a comma in all input data. Or counting the number of occurrences of each word in a book. ‘Reducing’ aggregates the mapped data together into useful results, perhaps through functions such as addition and subtraction.
Much like RedHat with Linux, there are now commercial releases of Hadoop such as Cloudera that provide tools to simplify Hadoop implementation as well as reliable technical support. Hadoop itself provides built-in fault tolerance through triplicate copies of data distributed across processing nodes, enabling a robust implementation ‘out of the box’. Whereas GPFS and Lustre have scaled across hundreds of servers, known Hadoop implementations have successfully scaled across tens of thousands of nodes.
So what does all this mean for HPC, scientific and engineering applications? Microway sees Hadoop as an excellent addition to the stack for data intensive scientific applications. This can include bioinformatics, physics and weather modeling applications. Hadoop can also accelerate science when the workloads include a series of queries of very large data sets. Additionally, when scaling science from the desktop up to larger workloads, Hadoop can provide an effective transition model.
A few examples of Microway Hadoop solutions include the NumberSmasher 1U, 2U and 4U servers.. With one to four multi-core Xeon CPUs, 512GB memory and up to 120TB storage, the NumberSmasher servers are flexible and cost-effective. Microway will build your cluster for you – whether it’s four nodes or a hundred nodes.
Practical Task Scheduling Deployment
July 20, 2016 12:00 pm CDT
One of the best things about the UNIX environment (aside from being stable and efficient) is the vast array of software tools available to help you do your job. Traditionally, a UNIX tool does only one thing, but does that one thing very well. For example, grep is very easy to use and can search vast amounts of data quickly. The find tool can find a particular file or files based on all kinds of criteria. It's pretty easy to string these tools together to build even more powerful tools, such as a tool that finds all of the .log files in the /home directory and searches each one for a particular entry. This erector-set mentality allows UNIX system administrators to seem to always have the right tool for the job.
Cron traditionally has been considered another such a tool for job scheduling, but is it enough? This webinar considers that very question. The first part builds on a previous Geek Guide, Beyond Cron, and briefly describes how to know when it might be time to consider upgrading your job scheduling infrastructure. The second part presents an actual planning and implementation framework.
Join Linux Journal's Mike Diehl and Pat Cameron of Help Systems.
Free to Linux Journal readers.Register Now!
- SUSE LLC's SUSE Manager
- Murat Yener and Onur Dundar's Expert Android Studio (Wrox)
- My +1 Sword of Productivity
- Managing Linux Using Puppet
- Non-Linux FOSS: Caffeine!
- Doing for User Space What We Did for Kernel Space
- SuperTuxKart 0.9.2 Released
- Parsing an RSS News Feed with a Bash Script
- Google's SwiftShader Released
- Rogue Wave Software's Zend Server
With all the industry talk about the benefits of Linux on Power and all the performance advantages offered by its open architecture, you may be considering a move in that direction. If you are thinking about analytics, big data and cloud computing, you would be right to evaluate Power. The idea of using commodity x86 hardware and replacing it every three years is an outdated cost model. It doesn’t consider the total cost of ownership, and it doesn’t consider the advantage of real processing power, high-availability and multithreading like a demon.
This ebook takes a look at some of the practical applications of the Linux on Power platform and ways you might bring all the performance power of this open architecture to bear for your organization. There are no smoke and mirrors here—just hard, cold, empirical evidence provided by independent sources. I also consider some innovative ways Linux on Power will be used in the future.Get the Guide