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tqchen 2014-12-04 09:09:29 -08:00
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@ -8,7 +8,6 @@ Design Note
====
* Rabit is designed for algorithms that replicate same global model across nodes, while each node operating on local parition of data.
* The global statistics collection is done by using Allreduce
* Currently, Rabit is not good at problems where model is distributed across nodes, other abstractions might suits the purpose (for example [parameter server](https://github.com/mli/parameter_server))
Design Goal
====
@ -28,8 +27,3 @@ Features
* MPI compatible
- Codes using rabit interface naturally compiles with existing MPI compiler
- User can fall back to use MPI Allreduce if they like with no code modification
Persistence of Program
====
Many complicated Machine learning algorithm involves things like temporal memory allocation, result caching.
It is good to have a program that persist over iterations and keeps the resources instead of re-allocate and re-compute the caching every time. Rabit allows the process to persist over all iterations.