Designing algorithms for Map Reduce

Since the emerging of Hadoop implementation, I have been trying to morph existing algorithms from various areas into the map/reduce model. The result is pretty encouraging and I've found Map/Reduce is applicable in a wide spectrum of application scenarios. So I want to write down my findings but then found the scope is too broad and also I haven't spent enough time to explore different problem dom

[restartr]

"Finally, I realize that there is no way for me to completely cover what Map/Reduce can do in all areas" "Notice that Map/Reduce is good for "data parallelism", which is different from "task parallelism". "

[kgbu]

Mapはモロに並列処理だが、Reduceは並列度が変化していくのでちょっと別の話なわけで、そこが面白いのかもなぁ。あとで読めるといいな。

[yujiorama]

MapReduce を使ったアルゴリズムの説明