P8

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P8
P8 s822.jpg
Installed June 2016
Operating System Linux Ubuntu 16.04 le
Number of Nodes 2x Power8 with 2x NVIDIA K80, 2x Power 8 with 4x NVIDIA P100
Interconnect Infiniband EDR
Ram/Node 512 GB
Cores/Node 2 x 8core (16 physical, 128 SMT)
Login/Devel Node p8t0[1-2] / p8t0[3-4]
Vendor Compilers xlc/xlf, nvcc

Specifications

The P8 Test System consists of of 4 IBM Power 822LC Servers each with 2x8core 3.25GHz Power8 CPUs and 512GB Ram. Similar to Power 7, the Power 8 utilizes Simultaneous MultiThreading (SMT), but extends the design to 8 threads per core allowing the 16 physical cores to support up to 128 threads. 2 nodes have two NVIDIA Tesla K80 GPUs with CUDA Capability 3.7 (Kepler), consisting of 2xGK210 GPUs each with 12 GB of RAM connected using PCI-E, and 2 others have 4x NVIDIA Tesla P100 GPUs each wit h 16GB of RAM with CUDA Capability 6.0 (Pascal) connected using NVlink.

Compile/Devel/Test

Access through the Niagara login nodes niagara.scinet.utoronto.ca using your CC/SciNet account and from there you can ssh to p8t01 or p8t02 for the K80 GPUs and to p8t03 or p8t04 for the Pascal P100 GPUs.

Softwares

GNU Compilers

*The default system GCC is 5.4.0

To load the newer advance toolchain version use:

module load gcc/7.3.1

IBM Compilers

To load the native IBM xlc/xlc++ compilers

For p8t0[1-2]

module load xlc/13.1.5
module load xlf/15.1.5

For p8t0[3-4]

module load xlc/16.1.0
module load xlf/16.1.0

CUDA

The current installed CUDA Tookit is 9.2.

module load cuda/9.2

OpenMPI

Currently OpenMPI has been setup on the four nodes connected over QDR Infiniband.

$ module load openmpi/3.1.2-gcc-5.4.0
$ module load openmpi/3.1.2-gcc-7.3.1

cuDNN

The NVIDIA CUDA Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. cuDNN accelerates widely used deep learning frameworks, including Caffe2, MATLAB, Microsoft Cognitive Toolkit, TensorFlow, Theano, and PyTorch. If a specific version of cuDNN is needed, user can download from https://developer.nvidia.com/cudnn and choose "cuDNN [VERSION] Library for Linux (Power8/Power9)".

cuDNN is installed as modules:

module load cuda/9.2 cudnn/cuda9.2/7.2.1

NCCL

The NVIDIA Collective Communications Library (NCCL) implements multi-GPU and multi-node collective communication primitives that are performance optimized for NVIDIA GPUs. NCCL is provided as modules on the system:

module load cuda/9.2 nccl/2.2.13

Anaconda (Python)

Anaconda is a popular distribution of the Python programming language. It contains several common Python libraries such as SciPy and NumPy as pre-built packages, which eases installation. Anaconda is provided as modules: anaconda2 and anaconda3

To install Anaconda locally, user need to load the module and create a conda environment: (anaconda3 as example)

module load anaconda3
conda create -n myPythonEnv python=3.6

To activate the conda environment: (should be activated before running python)

source activate myPythonEnv

Once the environment is activated, user can update or install packages via conda or pip

conda install -n myPythonEnv <package_name>
pip install <package_name>

To deactivate:

source deactivate

To remove a conda enviroment:

conda remove --name myPythonEnv --all

To verify that the environment was removed, run:

conda info --envs

TensorFlow

The TensorFlow which is provided by Anaconda may not be the most recent version. Newer versions of TensorFlow are provided as prebuilt Python Wheels that users can use pip to install under user space. Python wheels are stored in /scinet/p8_ubuntu16.04/Applications/TensorFlow_wheels/conda. It is required to install custom TensorFlow wheels into a Conda virtual environment.

Installing with Anaconda2 (Python2.7):

  • Load modules:
module load cuda/9.2 cudnn/cuda9.2/7.2.1 nccl/2.2.13 anaconda2
  • Create a conda environment tensorflow-1.11.0-py2:
conda create -n tensorflow-1.11.0-py2 python=2.7
  • Activate conda environment:
source activate tensorflow-1.11.0-py2
  • Install TensorFlow into the conda environment with updated dependencies:
conda install -n tensorflow-1.11.0-py2 keras-applications keras-preprocessing scipy mock cython numpy=1.14.5 protobuf grpcio markdown html5lib werkzeug absl-py bleach six openblas h5py astor gast termcolor setuptools=39.1.0 backports.weakref
pip install /scinet/p8_ubuntu16.04/Applications/TensorFlow_wheels/conda/tensorflow-1.11.0-cp27-cp27mu-linux_ppc64le.whl

Installing with Anaconda3 (Python3.6):

  • Load modules:
module load cuda/9.2 cudnn/cuda9.2/7.2.1 nccl/2.2.13 anaconda3
  • Create a conda environment tensorflow-1.11.0-py3:
conda create -n tensorflow-1.11.0-py3 python=3.6
  • Activate conda environment:
source activate tensorflow-1.11.0-py3
  • Install TensorFlow into the conda environment with updated dependencies:
conda install -n tensorflow-1.11.0-py3 keras-applications keras-preprocessing scipy mock cython numpy=1.14.5 protobuf grpcio markdown html5lib werkzeug absl-py bleach six openblas h5py astor gast termcolor setuptools=39.1.0
pip install /scinet/p8_ubuntu16.04/Applications/TensorFlow_wheels/conda/tensorflow-1.11.0-cp36-cp36m-linux_ppc64le.whl

Performance Guide

CPU Performance

Simultaneous multithreading (SMT)

POWER8 is designed to be a massively multithreaded chip, with each of its cores capable of handling 8 hardware threads simultaneously, for a total of 128 threads executed simultaneously on P8 node with 16 physical cores. On the system, it will show 128 (logical) CPU cores: CPU 0-7 is physical core 0, CPU 8-15 is physical core 1, ... , CPU 120-127 is physical core 15. Many of the programs developed on Intel/AMD x86 system are not optimized for POWER8 CPU. Using up all 8 hardware threads may significantly slow down the performance. Many programs show best performance with only 1 or 2 threads per physical core.

A common problem is thread binding. Software like GROMACS and NAMD can automatically bind certain number of threads to physical cores. If setting 2 threads per physical core, Gromacs/NAMD will use CPU 0,4,8,12,16, ..., 120, 124 only. Many Deep Learning softwares including TensorFlow and Pytorch are not able to automatically bind threads to a certain core. In this case, user can manually force the program to use certain CPUs via numactl tool.

If using 1 thread each physical core:
numactl -C 0,8,16,24,32,40,48,56,64,72,80,88,96,104,112,120 python code.py

If using 2 threads each physical core:
numactl -C 0,4,8,12,16,20,24,28,32,36,40,44,48,52,56,60,64,68,72,76,80,84,88,92,96,100,104,108,112,116,120,124 python code.py
  • MPI program cannot easily use numactl for thread binding. A rank file is used to bind rank to specific hardware thread(s). An example of a rank file, which uses 2 hardware threads per physical core, is shown below:
rank 0=p8t03 slot=0,4,8,12,16,20,24,28
rank 1=p8t03 slot=32,36,40,44,48,52,56,60
rank 2=p8t03 slot=64,68,72,76,80,84,88,92
rank 3=p8t03 slot=96,100,104,108,112,116,120,124

MPI program needs to be launched with flags: "-bind-to hwthread -rf myrankfile".

mpirun -np 4 -bind-to hwthread -rf myrankfile -report-bindings  yourprogram ...