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Commodity Cluster Supercomputing

CERN's Super Computin Grid_1
(CERN's Super Computing Grid, CERN)


Commodity Cluster


Most computing is done on a single compute node. If the computation needs more than a node or parallel processing, like many scientific computing problems, we use parallel computers. Simply put, a parallel computer is a very large number of single computing nodes with specialized capabilities connected to other network. For example, the Gordon Supercomputer at the San Diego Supercomputer Center, has 1,024 compute nodes with 16 cores each equalling 16,384 compute cores in total. This type of specialized computer is pretty costly compared to its most recent cousin, the Commodity Cluster. The term, Commodity Cluster, is often heard in big data conversations. 


Data Parallelism and Fault-tolerance


Commodity clusters are affordable parallel computers with an average number of computing nodes. They are not as powerful as traditional parallel computers and are often built out of less specialized nodes. In fact, the nodes in the commodity cluster are more generic in their computing capabilities. The service-oriented computing community over the internet have pushed for computing to be done on commodity clusters as distributed computations. And in turn, reducing the cost of computing over the Internet. 

In commodity clusters, the computing nodes are clustered in racks connected to each other via a fast network. There might be many of such racks in extensible amounts. Computing in one or more of these clusters across a local area network or the Internet is called distributed computing. Such architectures enable what we call data-parallelism. In data-parallelism many jobs that share nothing can work on different data sets or parts of a data set. This type of parallelism sometimes gets called as job level parallelism. But in this specialization, we will refer to it as data-parallelism in the context of Big-data computing. Large volumes and varieties of big data can be analyzed using this mode of parallelism, achieving scalability, performance and cost reduction. 

As you can imagine, there are many points of failure inside systems. A node, or an entire rack can fail at any given time. The connectivity of a rack to the network can stop or the connections between individual nodes can break. It is not practical to restart everything every time, if failure happens. The ability to recover from such failures is called Fault-tolerance. For Fault-tolerance of such systems, two neat solutions emerged. Namely, Redundant data storage and restart of failed individual parallel jobs. The commodity clusters are a cost effective way of achieving data parallel scalability for big data applications. These type of systems have a higher potential for partial failures. It is this type of distributed computing that pushed for a change towards cost effective reliable and Fault-tolerant systems for management and analysis of big data.


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