Add to ORKGIn this thesis we proposed and implemented the MMR, a new and open-source MapRe- duce model with MPI for parallel and distributed programing. MMR combines Pthreads, MPI and the Google\u27s MapReduce processing model to support multi-threaded as well as dis- tributed parallelism. Experiments show that our model signi cantly outperforms the leading open-source solution, Hadoop. It demonstrates linear scaling for CPU-intensive processing and even super-linear scaling for indexing-related workloads. In addition, we designed a MMR live DVD which facilitates the automatic installation and con guration of a Linux cluster with integrated MMR library which enables the development and execution of MMR applications
MapReduce is a programming model and an associated implementation for processing and generating larg...
The emergence of big data has brought a great impact on traditional computing mode, the distributed ...
MapReduce encompasses a framework in the processing and management of large scale datasets within a ...
In this thesis we proposed and implemented the MMR, a new and open-source MapRe- duce model with MP...
MapReduce is a data processing approach, where a single machine acts as a master, assigning map/redu...
Web-scale digital assets comprise millions or billions of documents. Due to such increase, sequentia...
As the data growth rate outpace that of the processing capabilities of CPUs, reaching Petascale, tec...
In an attempt to increase the performance/cost ratio, large compute clusters are becoming heterogene...
dissertationIn-memory big data applications are growing in popularity, including in-memory versions ...
AbstractWith the development of computer technology, there is a tremendous increase in the growth of...
In the last two decades, the continuous increase of computational power has produced an overwhelming...
This is a post-peer-review, pre-copyedit version of an article published in International Conference...
International audienceA large part of today's most popular applications are data-intensive; the data...
Abstract The timely processing of large-scale digital forensic targets demands the empoyment of larg...
We present GPMR, our MapReduce library that leverages the power of GPU clusters for large-scale comp...
MapReduce is a programming model and an associated implementation for processing and generating larg...
The emergence of big data has brought a great impact on traditional computing mode, the distributed ...
MapReduce encompasses a framework in the processing and management of large scale datasets within a ...
In this thesis we proposed and implemented the MMR, a new and open-source MapRe- duce model with MP...
MapReduce is a data processing approach, where a single machine acts as a master, assigning map/redu...
Web-scale digital assets comprise millions or billions of documents. Due to such increase, sequentia...
As the data growth rate outpace that of the processing capabilities of CPUs, reaching Petascale, tec...
In an attempt to increase the performance/cost ratio, large compute clusters are becoming heterogene...
dissertationIn-memory big data applications are growing in popularity, including in-memory versions ...
AbstractWith the development of computer technology, there is a tremendous increase in the growth of...
In the last two decades, the continuous increase of computational power has produced an overwhelming...
This is a post-peer-review, pre-copyedit version of an article published in International Conference...
International audienceA large part of today's most popular applications are data-intensive; the data...
Abstract The timely processing of large-scale digital forensic targets demands the empoyment of larg...
We present GPMR, our MapReduce library that leverages the power of GPU clusters for large-scale comp...
MapReduce is a programming model and an associated implementation for processing and generating larg...
The emergence of big data has brought a great impact on traditional computing mode, the distributed ...
MapReduce encompasses a framework in the processing and management of large scale datasets within a ...