SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
X10: an object-oriented approach to non-uniform cluster computing
OOPSLA '05 Proceedings of the 20th annual ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Pig latin: a not-so-foreign language for data processing
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Hive: a warehousing solution over a map-reduce framework
Proceedings of the VLDB Endowment
Hadoop: The Definitive Guide
Twister: a runtime for iterative MapReduce
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
NSDI'10 Proceedings of the 7th USENIX conference on Networked systems design and implementation
HaLoop: efficient iterative data processing on large clusters
Proceedings of the VLDB Endowment
Apache hadoop goes realtime at Facebook
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
SystemML: Declarative machine learning on MapReduce
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Java interoperability in managed X10
Proceedings of the third ACM SIGPLAN X10 Workshop
Cache conscious star-join in MapReduce environments
Proceedings of the 2nd International Workshop on Cloud Intelligence
Hone: "Scaling down" Hadoop on shared-memory systems
Proceedings of the VLDB Endowment
Resilient X10: efficient failure-aware programming
Proceedings of the 19th ACM SIGPLAN symposium on Principles and practice of parallel programming
Speeding-up codon analysis on the cloud with local MapReduce aggregation
Information Sciences: an International Journal
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Main Memory Map Reduce (M3R) is a new implementation of the Hadoop Map Reduce (HMR) API targeted at online analytics on high mean-time-to-failure clusters. It does not support resilience, and supports only those workloads which can fit into cluster memory. In return, it can run HMR jobs unchanged -- including jobs produced by compilers for higher-level languages such as Pig, Jaql, and SystemML and interactive front-ends like IBM BigSheets -- while providing significantly better performance than the Hadoop engine on several workloads (e.g. 45x on some input sizes for sparse matrix vector multiply). M3R also supports extensions to the HMR API which can enable Map Reduce jobs to run faster on the M3R engine, while not affecting their performance under the Hadoop engine.