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MapReduce is a programming model and an associated implementation for processing and generating big data sets with a parallel and distributed algorithm on a cluster. [1] [2] [3]A MapReduce program is composed of a map procedure, which performs filtering and sorting (such as sorting students by first name into queues, one queue for each name), and a reduce method, which performs a summary ...
In linear programming, reduced cost, or opportunity cost, is the amount by which an objective function coefficient would have to improve (so increase for maximization problem, decrease for minimization problem) before it would be possible for a corresponding variable to assume a positive value in the optimal solution.
Pig Latin abstracts the programming from the Java MapReduce idiom into a notation which makes MapReduce programming high level, similar to that of SQL for relational database management systems. Pig Latin can be extended using user-defined functions (UDFs) which the user can write in Java , Python , JavaScript , Ruby or Groovy [ 3 ] and then ...
Incorporation of "low-cost thinking" into an organisation's culture [5]: 8 Half cost strategies: ambitious strategies which aim to reduce the costs of specific production processes or value adding stages to 1/N of the previous cost. [7] Examples specifically focussed on the use of suppliers and the costs of goods and services supplied include:
For example, in the MapReduce architecture, the map actor type is the source for reduce, and vice versa. The system infers this from the data flow archetypes and duly links map instances with reduce instances. However, there may be several MapReduce jobs in the data flow and linking all map instances with all reduce instances can create false ...
MapReduce is introduced as a clustered method and largely summarizes Googles technology for map-reduce in a clustered environment from circa 2004. However map-reduce has been an integral part of functional programming and enabler of parallelism decades before.
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All-reduce can be interpreted as a reduce operation with a subsequent broadcast (§ Broadcast). For long messages a corresponding implementation is suitable, whereas for short messages, the latency can be reduced by using a hypercube ( Hypercube (communication pattern) § All-Gather/ All-Reduce ) topology, if p {\displaystyle p} is a power of two.