[DOC] refactor doc

doc/how_to/external_memory.md

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+Using XGBoost External Memory Version(beta)
+===========================================
+There is no big difference between using external memory version and in-memory version.
+The only difference is the filename format.
+
+The external memory version takes in the following filename format
+```
+filename#cacheprefix
+```
+
+The ```filename``` is the normal path to libsvm file you want to load in, ```cacheprefix``` is a
+path to a cache file that xgboost will use for external memory cache.
+
+The following code was extracted from [../demo/guide-python/external_memory.py](../demo/guide-python/external_memory.py)
+```python
+dtrain = xgb.DMatrix('../data/agaricus.txt.train#dtrain.cache')
+```
+You can find that there is additional ```#dtrain.cache``` following the libsvm file, this is the name of cache file.
+For CLI version, simply use ```"../data/agaricus.txt.train#dtrain.cache"``` in filename.
+
+Performance Note
+----------------
+* the parameter ```nthread``` should be set to number of ***real*** cores
+  - Most modern CPU offer hyperthreading, which means you can have a 4 core cpu with 8 threads
+  - Set nthread to be 4 for maximum performance in such case
+
+Distributed Version
+-------------------
+The external memory mode naturally works on distributed version, you can simply set path like
+```
+data = "hdfs:///path-to-data/#dtrain.cache"
+```
+xgboost will cache the data to the local position. When you run on YARN, the current folder is temporal
+so that you can directly use ```dtrain.cache``` to cache to current folder.
+
+
+Usage Note
+----------
+* This is a experimental version
+  - If you like to try and test it, report results to https://github.com/dmlc/xgboost/issues/244
+* Currently only importing from libsvm format is supported
+  - Contribution of ingestion from other common external memory data source is welcomed