diff --git a/README.md b/README.md
index 6a05ce0c4..cfb561bf4 100644
--- a/README.md
+++ b/README.md
@@ -74,6 +74,18 @@ Build
   export CXX = g++-4.9
   ```
   Then run ```bash build.sh``` normally.
+  
+  - For users who want to use [High Performance Computing for Mac OS X](http://hpc.sourceforge.net/), download the GCC 4.9 binary tar ball and follow the installation guidance to install them under `/usr/local`. Then edit [Makefile](Makefile/) by replacing:
+  ```
+  export CC  = gcc
+  export CXX = g++
+  ```
+  with
+  ```
+  export CC  = /usr/local/bin/gcc
+  export CXX = /usr/local/bin/g++
+  ```
+  Then run ```bash build.sh``` normally. This solution is given by [Phil Culliton](https://www.kaggle.com/c/otto-group-product-classification-challenge/forums/t/12947/achieve-0-50776-on-the-leaderboard-in-a-minute-with-xgboost/68308#post68308).
 
 Version
 =======
diff --git a/demo/kaggle-otto/benchmark.R b/demo/kaggle-otto/benchmark.R
deleted file mode 100644
index ffe7a8794..000000000
--- a/demo/kaggle-otto/benchmark.R
+++ /dev/null
@@ -1,43 +0,0 @@
-require(xgboost)
-require(methods)
-
-train = read.csv('data/train.csv',header=TRUE,stringsAsFactors = F)
-test = read.csv('data/test.csv',header=TRUE,stringsAsFactors = F)
-train = train[,-1]
-test = test[,-1]
-
-y = train[,ncol(train)]
-y = gsub('Class_','',y)
-y = as.integer(y)-1 #xgboost take features in [0,numOfClass)
-
-x = rbind(train[,-ncol(train)],test)
-x = as.matrix(x)
-x = matrix(as.numeric(x),nrow(x),ncol(x))
-trind = 1:length(y)
-teind = (nrow(train)+1):nrow(x)
-
-# Set necessary parameter
-param <- list("objective" = "multi:softprob",
-              "eval_metric" = "mlogloss",
-              "num_class" = 9,
-              "nthread" = 8)
-
-# Run Cross Valication
-cv.nround = 50
-bst.cv = xgb.cv(param=param, data = x[trind,], label = y, 
-                nfold = 3, nrounds=cv.nround)
-
-# Train the model
-nround = 50
-bst = xgboost(param=param, data = x[trind,], label = y, nrounds=nround)
-
-# Make prediction
-pred = predict(bst,x[teind,])
-pred = matrix(pred,9,length(pred)/9)
-pred = t(pred)
-
-# Output submission
-pred = format(pred, digits=2,scientific=F) # shrink the size of submission
-pred = data.frame(1:nrow(pred),pred)
-names(pred) = c('id', paste0('Class_',1:9))
-write.csv(pred,file='submission.csv', quote=FALSE,row.names=FALSE)
diff --git a/demo/kaggle-otto/readme.md b/demo/kaggle-otto/readme.md
deleted file mode 100644
index af95dd47a..000000000
--- a/demo/kaggle-otto/readme.md
+++ /dev/null
@@ -1,24 +0,0 @@
-Benckmark for Otto Group Competition
-=========
-
-This is a folder containing the benchmark for the [Otto Group Competition on Kaggle](http://www.kaggle.com/c/otto-group-product-classification-challenge).
-
-## Getting started
-
-1. Put `train.csv` and `test.csv` under the `data` folder
-2. Run the script
-
-The parameter `nthread` controls the number of cores to run on, please set it to suit your machine.
-
-## R-package
-
-To install the R-package of xgboost, please run
-
-```r
-devtools::install_github('tqchen/xgboost',subdir='R-package')
-```
-
-Windows users may need to install [RTools](http://cran.r-project.org/bin/windows/Rtools/) first.
-
-
-