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It sadden me that they make Jeff Dean work on these kind of issues. His map/reduce invention brought to the world a lot more than managing his coworker egos.


How do you know they are making him to this. It seems to me Jeff chose to accept leadership position at Google. So managing egos would be first class task to deal with for him.


Exactly.

If managing people were the same skill set as computer science perhaps we would not be seeing this play out as it has.

This is seriously a massive failure on the part of Google to handle their own shit. I often wonder if these people are too old or out of touch to appreciate how this kind of thing may or may not blow up on social media.

To not consider how this might cause public relations issues especially when dealing with someone who has an established social media presence seems like a misstep.

On the flip side maybe they thought it was worth it. Weather the storm for a few days and twitter will move one. It's hard to say.


No one is forcing him to be the head of Google's Brain division or to manage other people.


Jeff Dean did not invent map/reduce


Jeff Dean and Sanjay Ghemawat developed the system called MapReduce at Google and published a paper about it. They didn't invent the map or reduce functions, nor is the MapReduce system they published really about map or reduce functions.

The correct name is really MapShuffleReduce and the most important step is the shuffle, because it's a distributed sort. Of course, they didn't invent distributed sort, but combining these three concepts together in a distributed fashion and running it as a production system is really what was important.



The key ideas were part of the MPI interface for a decade before Jeff came along and applied the new branding

https://en.wikipedia.org/wiki/Message_Passing_Interface


MapReduce and MPI are very different. MapReduce doesn't use message passing- the map phase reads inputs from sharded files in a separate disk system, applies map to the inputs, and writes out the mapped outputs to temp sort files on a seperate disk system. Then the shuffler sorts those and writes the outputs to the appropriate destination output shards in a seperate disk system, at which point the reducer reads them, applies reduce, and writes the final outputs, sharded by key to a seperate disk system.

The mappers, shufflers, and reducers are all independent of each other, reading and writing from the filesystem, and managed by a coordinator. There's nothing like MPI, other than the use of the Stubby RPC system, which sort of resembles MPI but has completely different distributed communication semantics.


The distributed database systems of the mid 80s such as the Teradata DBC 1012[1] are better prior art for Map/Reduce.

1- https://en.wikipedia.org/wiki/DBC_1012


OK then tensor flow




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