Google Research Paper Mapreduce

Google Research Paper Mapreduce-52
Given that the number of nodes may increase up to a certain threshold, the network incurs huge data transfer costs.

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Cloud Web services such as the Amazon Elastic Compute Cloud (EC2) and Amazon Elastic Map Reduce are commercially available, whereas the IBM/Google Cloud Computing University Initiative and the United States Department of Energy’s Magellan service are free.

Users generally upload their data by using a Web interface, after which they can perform operations on a remote client webpage.

In addition, we discuss the challenges faced by this field as well as the future works on parallel computing in bioinformatics.

Bioinformatics researchers have continuously and progressively helped biologists in solving computational biology problems, particularly in dealing with large amounts of genomic data.

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Bioinformatics is challenged by the fact that traditional analysis tools have difficulty in processing large-scale data from high-throughput sequencing.Dryad [2] is an extension of Map Reduce from Microsoft and Azure is one of Microsoft’s cloud technologies [3].With Dryad, Microsoft proposes the use of a directed acyclic graph (DAG) to combine computational ‘vertices’ with communication ‘channels’ to model data flow graphs [4].Given the absence of load balancing, fault tolerance and a distributed file system, MPI is unreliable and insufficiently robust.The compute unified device architecture (CUDA) programming model that is based on a GPU framework has recently facilitated the improvement of computing performance.The open source Apache Hadoop project, which adopts the Map Reduce framework and a distributed file system, has recently given bioinformatics researchers an opportunity to achieve scalable, efficient and reliable computing performance on Linux clusters and on cloud computing services.In this article, we present Map Reduce frame-based applications that can be employed in the next-generation sequencing and other biological domains.MPI is a widely used traditional parallel programming model.Although MPI provides a powerful API for the general programmers, researchers with a biology background still consider the program complicated.URL: A modified version of the Hadoop Map Reduce framework.URL: Apache Hadoop and Microsoft Dryad/Azure are widely implemented in local systems, not only for their parallel capability but also for their easy deployment in a commodity hardware cluster.

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