Partitioning Techniques for Large-Grained Parallelism
IEEE Transactions on Computers
Scheduling divisible jobs on hypercubes
Parallel Computing
Journal of Parallel and Distributed Computing
Scheduling divisible loads in a three-dimensional mesh of processors
Parallel Computing
A Novel Data Distribution Technique for Host-Client Type Parallel Applications
IEEE Transactions on Parallel and Distributed Systems
Scheduling Divisible Loads in Parallel and Distributed Systems
Scheduling Divisible Loads in Parallel and Distributed Systems
Optimizing Computing Costs Using Divisible Load Analysis
IEEE Transactions on Parallel and Distributed Systems
Experiments with Scheduling Divisible Tasks in Clusters of Workstations
Euro-Par '00 Proceedings from the 6th International Euro-Par Conference on Parallel Processing
Scheduling Divisible Loads on Star and Tree Networks: Results and Open Problems
IEEE Transactions on Parallel and Distributed Systems
Practical Divisible Load Scheduling on Grid Platforms with APST-DV
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Papers - Volume 01
Interpreting the data: Parallel analysis with Sawzall
Scientific Programming - Dynamic Grids and Worldwide Computing
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Evaluating MapReduce for Multi-core and Multiprocessor Systems
HPCA '07 Proceedings of the 2007 IEEE 13th International Symposium on High Performance Computer Architecture
Experimental study of scheduling with memory constraints using hybrid methods
Journal of Computational and Applied Mathematics
Scheduling for Parallel Processing
Scheduling for Parallel Processing
Data-Intensive Text Processing with MapReduce
Data-Intensive Text Processing with MapReduce
Cluster-based optimized parallel video transcoding
Parallel Computing
A parallel method for computing rough set approximations
Information Sciences: an International Journal
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In this paper we analyze MapReduce distributed computations as a divisible load scheduling problem. The two operations of mapping and reducing can be understood as two divisible applications with precedence constraints. A divisible load model of the computation, and two load partitioning algorithms are proposed. Performance limits of MapReduce computations are investigated. To our best knowledge this is the first time that processing applications with precedence constraints have been considered on the grounds of divisible load theory.