xgboost/plugin/updater_gpu/README.md

# CUDA Accelerated Tree Construction Algorithms
This plugin adds GPU accelerated tree construction algorithms to XGBoost.
## Usage
Specify the 'updater' parameter as one of the following algorithms. 

### Algorithms
| updater | Description |
| --- | --- |
grow_gpu | The standard XGBoost tree construction algorithm. Performs exact search for splits. Slower and uses considerably more memory than 'grow_gpu_hist' |
grow_gpu_hist | Equivalent to the XGBoost fast histogram algorithm. Faster and uses considerably less memory. Splits may be less accurate. |

### Supported parameters 
| parameter | grow_gpu | grow_gpu_hist |
| --- | --- | --- |
subsample | ✔ | ✔ |
colsample_bytree | ✔ | ✔|
colsample_bylevel | ✔ | ✔ |
max_bin | ✖ | ✔ |
gpu_id | ✔ | ✔ | 
n_gpus | ✖ | ✔ | 

The device ordinal can be selected using the 'gpu_id' parameter, which defaults to 0.

Multiple GPUs can be used with the grow_gpu_hist parameter using the n_gpus parameter, which defaults to -1 (indicating use all visible GPUs).  If gpu_id is specified as non-zero, the gpu device order is mod(gpu_id + i) % n_visible_devices for i=0 to n_gpus-1.  As with GPU vs. CPU, multi-GPU will not always be faster than a single GPU due to PCI bus bandwidth that can limit performance.  For example, when n_features * n_bins * 2^depth divided by time of each round/iteration becomes comparable to the real PCI 16x bus bandwidth of order 4GB/s to 10GB/s, then AllReduce will dominant code speed and multiple GPUs become ineffective at increasing performance.  Also, CPU overhead between GPU calls can limit usefulness of multiple GPUs.

This plugin currently works with the CLI version and python version.

Python example:
```python
param['gpu_id'] = 1
param['max_bin'] = 16
param['updater'] = 'grow_gpu_hist'
```
## Benchmarks
To run benchmarks on synthetic data for binary classification:
```bash
$ python benchmark/benchmark.py
```

Training time time on 1000000 rows x 50 columns with 500 boosting iterations on i7-6700K CPU @ 4.00GHz and Pascal Titan X.

| Updater | Time (s) |
| --- | --- |
| grow_gpu_hist | 11.09 |
| grow_fast_histmaker (histogram XGBoost - CPU) | 41.75 |
| grow_gpu | 193.90 |
| grow_colmaker (standard XGBoost - CPU) | 720.12 |


[See here](http://dmlc.ml/2016/12/14/GPU-accelerated-xgboost.html) for additional performance benchmarks of the 'grow_gpu' updater.

## Test
To run tests:
```bash
$ python -m nose test/python/
```
## Dependencies
A CUDA capable GPU with at least compute capability >= 3.5 (the algorithm depends on shuffle and vote instructions introduced in Kepler).

Building the plug-in requires CUDA Toolkit 7.5 or later (https://developer.nvidia.com/cuda-downloads)

submodule: The plugin also depends on CUB 1.6.4 - https://nvlabs.github.io/cub/ . CUB is a header only cuda library which provides sort/reduce/scan primitives.

submodule: NVIDIA NCCL from https://github.com/NVIDIA/nccl with windows port allowed by git@github.com:h2oai/nccl.git

## Build

From the command line on Linux starting from the xgboost directory:

On Linux, from the xgboost directory:
```bash
$ mkdir build
$ cd build
$ cmake .. -DPLUGIN_UPDATER_GPU=ON
$ make -j
```
On Windows using cmake, see what options for Generators you have for cmake, and choose one with [arch] replaced by Win64:
```bash
cmake -help
```
Then run cmake as:
```bash
$ mkdir build
$ cd build
$ cmake .. -G"Visual Studio 14 2015 Win64" -DPLUGIN_UPDATER_GPU=ON
```
Cmake will generate an xgboost.sln solution file in the build directory. Build this solution in release mode as a x64 build.

Visual studio community 2015, supported by cuda toolkit (http://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/#axzz4isREr2nS), can be downloaded from: https://my.visualstudio.com/Downloads?q=Visual%20Studio%20Community%202015 .  You may also be able to use a later version of visual studio depending on whether the CUDA toolkit supports it.  Note that Mingw cannot be used with cuda.

### For Developers!

In case you want to build only for a specific GPU(s), for eg. GP100 and GP102,
whose compute capability are 60 and 61 respectively:
```bash
$ cmake .. -DPLUGIN_UPDATER_GPU=ON -DGPU_COMPUTE_VER="60;61"
```
By default, the versions will include support for all GPUs in Maxwell and Pascal architectures.

### Using make
Now, it also supports the usual 'make' flow to build gpu-enabled tree construction plugins. It's currently only tested on Linux. From the xgboost directory
```bash
# make sure CUDA SDK bin directory is in the 'PATH' env variable
$ make PLUGIN_UPDATER_GPU=ON
```

Similar to cmake, if you want to build only for a specific GPU(s):
```bash
$ make PLUGIN_UPDATER_GPU=ON GPU_COMPUTE_VER="60 61"
```

### For Developers!

Now, some of the code-base inside gpu plugins have googletest unit-tests inside 'tests/'.
They can be enabled run along with other unit-tests inside '<xgboostRoot>/tests/cpp' using:
```bash
# make sure CUDA SDK bin directory is in the 'PATH' env variable
# below 2 commands need only be executed once
$ source ./dmlc-core/scripts/travis/travis_setup_env.sh
$ make -f dmlc-core/scripts/packages.mk gtest
$ make PLUGIN_UPDATER_GPU=ON GTEST_PATH=${CACHE_PREFIX} test
```

## Changelog
##### 2017/6/5

* Multi-GPU support for histogram method using NVIDIA NCCL.

##### 2017/5/31
* Faster version of the grow_gpu plugin
* Added support for building gpu plugin through 'make' flow too

##### 2017/5/19
* Further performance enhancements for histogram method.

##### 2017/5/5
* Histogram performance improvements
* Fix gcc build issues 

##### 2017/4/25
* Add fast histogram algorithm
* Fix Linux build
* Add 'gpu_id' parameter

## References
[Mitchell, Rory, and Eibe Frank. Accelerating the XGBoost algorithm using GPU computing. No. e2911v1. PeerJ Preprints, 2017.](https://peerj.com/preprints/2911/)

## Author
Rory Mitchell
Jonathan C. McKinney
Shankara Rao Thejaswi Nanditale
Vinay Deshpande
... and the rest of the H2O.ai and NVIDIA team.

Please report bugs to the xgboost/issues page.