Gridgain中Map/Reduce算法逻辑

compute_grids.gif(19.7 K)

切换到幻灯片模式

Map/Reduce
Like in traditional HPC systems (such as MPI) the trademark of computational grids is ability to split process into a set of sub-processes, execute them in parallel, and aggregate sub-results into the one final result.
Map/Rreduce (a.k.a as split and aggregate) design allows to parallelize the process of task execution gaining performance and/or scalability: with two nodes in the grid you can achieve up to 2 times performance increase, with 3 nodes - up to 3 times performance increase, and so on. For a example, with simple and inexpensive 10-nodes grid you can often achieve "an order of magnitude" performance increase for your applications.

In GridGain a grid task is a deployable unit of work that is responsible for initial splitting (mapping) and aggregation (reducing), and a grid job is an executable unit of work that executes on the remote node.

Following diagram illustrates the basics of split and aggregate logic:

Task execution request
Task splits (maps) into jobs
Result of job execution
Aggregation (reduction) of job results into task result

文章来自: GridGain
标签: GridGain