{"id":{"repo_id":"uno","oai_identifier":"oai:scholarworks.uno.edu:td-1944"},"canonical_url":"https://search.dev.ndltd.org/etd/uno/oai:scholarworks.uno.edu:td-1944","repository":{"repo_id":"uno","name":"University of New Orleans","base_url":"https://scholarworks.uno.edu/do/oai/"},"display":{"title":"An Efficient Platform for Large-Scale MapReduce Processing","abstract":"In 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's 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.","abstract_html":"In 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's 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.","abstract_has_math":false,"creators":["Wang, Liqiang"],"institution":null,"degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Roussev, Vassil","Tu, Shengru","Richard III, Golden G."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2009,"date_issued":"2009-05-15T07:00:00Z","date_published":"2009-05-15T07:00:00Z","updated_at":"2026-07-24T05:28:56Z","subjects":["MapReduce","Pthreads","MPI","Hadoop and MMR(MPI Based MapReduce)"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarworks.uno.edu/td/963","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Roussev, Vassil","Tu, Shengru","Richard III, Golden G."]},{"key":"dc:creator","label":"Author","values":["Wang, Liqiang"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["MapReduce","Pthreads","MPI","Hadoop and MMR(MPI Based MapReduce)"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarworks.uno.edu/td/963"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In 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's 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."]},{"key":"dc:title","label":"Title","values":["An Efficient Platform for Large-Scale MapReduce Processing"]}]}],"canonical_facts":{"dc:contributor":["Roussev, Vassil","Tu, Shengru","Richard III, Golden G."],"dc:creator":["Wang, Liqiang"],"dc:description.abstract":["In 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's 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."],"dc:identifier":["https://scholarworks.uno.edu/td/963"],"dc:subject":["MapReduce","Pthreads","MPI","Hadoop and MMR(MPI Based MapReduce)"],"dc:title":["An Efficient Platform for Large-Scale MapReduce Processing"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."]},"updated_at":"2026-07-24T05:28:56Z"}