QCD_accelerator_benchmark_2_all_systems.txt

###
###    README - QCD Accelerator Benchmarksuite Part 2
###
###   2017 -  Jacob Finkenrath - CaSToRC - The Cyprus Institute  (j.finkenrath@cyi.ac.cy)
###

The QCD Accelerator Benchmark suite Part 2 consists of two kernels,
the QUDA and the QPhix library. The QUDA library is based on CUDA and
optimized for running on NVIDIA GPUs. The QPhix library consists of
routines which are optimize to use INTEL intrinsic functions of
multiple vector length, including optimized routines for KNC and
KNL. In both QUDA and QPhix, this benchmark uses the Conjugate
Gradient solvers implemented within the libraries.

[1] R. Babbich, M. Clark and B. Joo, “Parallelizing the QUDA Library for Multi-GPU Calculations
in Lattice Quantum Chromodynamics” SC 10 (Supercomputing 2010)

[2] B. Joo, D. D. Kalamkar, K. Vaidyanathan, M. Smelyanskiy, K. Pamnany, V. W. Lee, P. Dubey,
W. Watson III, “Lattice QCD on Intel Xeon Phi”, International Supercomputing Conference (ISC’13), 2013

###
###  Table of Contents
###


GPU - BENCHMARK SUITE (QUDA)
1. Compile and Run the GPU-Benchmark Suite
1.1 Compile
1.2 Run
1.2.1 Main-script: "run_ana.sh"
1.2.2 Main-script: "prepare_submit_job.sh"
1.2.3 Main-script: "submit_job.sh.template"
1.3 Example Benchmark results

XEONPHI - BENCHMARK SUITE (QPHIX)
2. Compile and Run the XeonPhi-Benchmark Suite
2.1 Compile
2.1.1 Example compilation on PRACE machines
2.1.1.1 BSC - Marenostrum III Hybrid partitions
2.1.1.2 CINES - Frioul
2.2 Run
2.2.1 Main-script: "run_ana.sh"
2.2.2 Main-script: "prepare_submit_job.sh"
2.2.3 Main-script: "submit_job.sh.template"
2.3 Example Benchmark Results


###
###
###   GPU - BENCHMARK SUITE
###
###
##
## 1. Compile and Run the GPU-Benchmark Suite
##

##
## 1.1 Compile
##

Download Cmake and Quda

General information how to build QUDA with cmake can be found under:
"https://github.com/lattice/quda/wiki/Building-QUDA-with-cmake". Here
we just give a short overview:

Build Cmake: (./QCD_Accelerator_Benchmarksuite_Part2/GPUs/src/cmake-3.7.0.tar.gz)

Cmake can be downloaded from source at URL:
https://cmake.org/download/. This guide uses version 3.7.0. The build
instruction can be found in the main directory under "README.rst". Use
the configure file "./configure" .  Then run "gmake" to compile.

Build Quda: (./QCD_Accelerator_Benchmarksuite_Part2/GPUs/src/quda.tar.gz)

Download quda for example by using "git clone
https://github.com/lattice/quda.git".  Create a build-folder. Use
"cmake" in the build-folder, which should be under cmake/bin if you
compiled cmake from source. Execute:

./$PATH2CMAKE/cmake $PATH2QUDA -DQUDA_GPU_ARCH=sm_XX -DQUDA_DIRAC_WILSON=ON -DQUDA_DIRAC_TWISTED_MASS=OFF
-DQUDA_DIRACR_DOMAIN_WALL=OFF -DQUDA_HISQ_LINK=OFF -DQUDA_GAUGE_FORCE=OFF -DQUDA_HISQ_FORCE=OFF -DQUDA_MPI=ON

with

    PATH2CMAKE=<path to the cmake-executable>
    PAT2QUDA=<path to the home dir of QUDA>

Set -DQUDA_GPU_ARCH=sm_XX to the GPU Architecture (sm_60 for Pascal, sm_35 for Kepler)

If cmake or the compilation fails, library paths and options can be
set via the text user interface of cmake by using "ccmake".  Use
"./PATH2CMAKE/ccmake PATH2BUILD_DIR" to see and edit the available
options. After successfully configuring the buil, run "make".  Now in
the folder test/ one can find the needed Quda executables which begin
with "invert_".

##
##    1.2 Run
##


The Accelerator QCD-Benchmarksuite Part 2 provides bash-scripts
located in the folder
./QCD_Accelerator_Benchmarksuite_Part2/GPUs/scripts" to setup the
benchmark runs on the target machines. This bash-scripts are:

run_ana.sh              :   Main-script, sets up the benchmark mode and submits the jobs (analyse the results)
prepare_submit_job.sh   :   Generates the job-scripts
submit_job.sh.template  :   Template for submit script

##
## 1.2.1 Main-script: "run_ana.sh"
##

The path to the executable has to be set by $PATH2EXE .  Upon first
run, QUDA automatically tunes the GPU-kernels by sweeping the number
of threads per block. The optimal setup will be saved in the folder
which pointed to in the environment variable "QUDA_RESOURCE_PATH". You
must set this variable, otherwise the tune data will be lost and
performance will be sub-optimal. Set it to the folder where the tuning
data should be saved. Strong scaling or Weak scaling can be chosen by
using the variable sca_mode (="Strong" or ="Weak").  The lattice sizes
can be set by "gx" and "gt".  Choose mode="Run" for run mode or
mode="Analysis" for extracting the GFLOPS. Note that the script
assumes Slurm is used as the job scheduler. If not, change the line
which includes the "sbatch" command accordingly.

##
## 1.2.2 Main-script: "prepare_submit_job.sh"
##

Add additional options if necessary.

##
## 1.2.3 Main-script: "submit_job.sh.template"
##

The submit-template will be edited by "prepare_submit_job.sh" to
generate the final submit-script. The first lines (beginning with
"#SBATCH") depend on the queuing system of the target machine, which in
this case is assumed to be Slurm. These should be changed in case of a
different queuing system.

The Accelerator QCD-Benchmarksuite Part 2 provides bash-scripts to
setup the benchmark runs on the target machines. These bash-scripts
are:

##
## 1.3 Example Benchmark results
##

Here are shown the benchmark results on PizDaint located in Switzerland at CSCS
and the GPGPU-partition of Cartesius at Surfsara based in Netherland, Amsterdam. The runs are performed by using the provided bash-scripts. PizDaint has one Pascal-GPU per node and two different testcases are shown,
the "Strong-Scaling mode with a random lattice configuration of size 32^3x96 and
a "Weak-Scaling" mode with a configuration of local lattice size 48^3x24.
The GPGPU nodes of Cartesius has two Kepler-GPU per node and the "Strong-Scaling" test is shown for the case
that one card per node and two cards per node are used.
The benchmark are done by using the Conjugated Gradient solver which
solve a linear equation, D * x = b, for the unknown solution "x" based on the clover improved Wilson Dirac operator
"D" and a known right hand side "b".

---------------------
  PizDaint - Pascal  P100
---------------------
Strong - Scaling:
global lattice size (32x32x32x96)

sloppy-precision: single
       precision: single

GPUs     GFLOPS      sec
1    786.520000 4.569600
2   1522.410000 3.086040
4   2476.900000 2.447180
8   3426.020000 2.117580
16  5091.330000 1.895790
32  8234.310000 1.860760
64  8276.480000 1.869230

sloppy-precision: double
       precision: double

GPUs     GFLOPS      sec
1    385.965000 6.126730
2    751.227000 3.846940
4   1431.570000 2.774470
8   1368.000000 2.367040
16  2304.900000 2.071160
32  4965.480000 2.095180
64  2308.850000 2.005110


Weak - Scaling:
local lattice size (48x48x48x24)

sloppy-precision: single
       precision: single

GPUs     GFLOPS      sec
1     765.967000 3.940280
2    1472.980000 4.004630
4    2865.600000 4.044360
8    5421.270000 4.056410
16   9373.760000 7.396590
32  17995.100000 4.243390
64  27219.800000 4.535410

sloppy-precision: double
       precision: double

GPUs    GFLOPS      sec
 1   376.611000 5.108900
 2   728.973000 5.190880
 4  1453.500000 5.144160
 8  2884.390000 5.207090
16  5004.520000 5.362020
32  8744.090000 5.623290
64  14053.00000 5.910520


---------------------
  SurfSara - Kepler   K20m
---------------------
##
## 1 GPU per Node
##

Strong - Scaling:
global lattice size (32x32x32x96)

sloppy-precision: single
       precision: single
GPUs    GFLOPS      sec
1      243.084000 4.030000
2      478.179000 2.630000
4      939.953000 2.250000
8     1798.240000 1.570000
16    3072.440000 1.730000
32    4365.320000 1.310000

sloppy-precision: double
       precision: double

GPUs    GFLOPS      sec
1      119.786000 6.060000
2      234.179000 3.290000
4      463.594000 2.250000
8      898.090000 1.960000
16    1604.210000 1.480000
32    2420.130000 1.630000

##
## 2 GPU per Node
##

Strong - Scaling:
global lattice size (32x32x32x96)

sloppy-precision: single
       precision: single

GPUs    GFLOPS      sec
2      463.041000 2.720000
4      896.707000 1.940000
8     1672.080000 1.680000
16    2518.240000 1.420000
32    3800.970000 1.460000
64    4505.440000 1.430000

sloppy-precision: double
       precision: double

GPUs    GFLOPS      sec
2     229.579000 3.380000
4     450.425000 2.280000
8     863.117000 1.830000
16   1348.760000 1.510000
32   1842.560000 1.550000
64   2645.590000 1.480000

###
###
###   XEONPHI - BENCHMARK SUITE
###
###

##
## 2. Compile and Run the XeonPhi-Benchmark Suite
##

Unpack the provided source tar-file located in
"./QCD_Accelerator_Benchmarksuite_Part2/XeonPhi/src" or clone the
actual git-hub branches of the code packages QMP:

"git clone https://github.com/usqcd-software/qmp"

and for QPhix

"git clone https://github.com/JeffersonLab/qphix"

Note that the AVX512 instructions, which are needed for an optimal run
on KNLs, are not yet part of the main branch. The AVX512 instructions
are available in the avx512-branch ("git checkout avx512). The
provided source file is using the avx512-branch (Status as of 01/2017).

##
## 2.1 Compile
##

The QPhix library must be built upon QMP, a thin communication layer
on top of MPI. Compile QMP first:

./configure --prefix=$QMP_INSTALL_DIR CC=mpiicc CFLAGS=" -mmic/-xAVX512 -std=c99" --with-qmp-comms-type=MPI --host=x86_64-linux-gnu --build=none-none-none

Create the install folder and link with $QMP_INSTALL_DIR to it.  Use
the compiler flag "-mmic" for the compilation for KNC while use
"-xAVX512" for the compilation for KNL.  Then use "make" to compile
and "make install" to copy the necessary source files in
$QMP_INSTALL_DIR.

The QPhix executable can be compiled by using, for KNC:

./configure --enable-parallel-arch=parscalar --enable-proc=MIC --enable-soalen=8 --enable-clover --enable-openmp --enable-cean --enable-mm-malloc CXXFLAGS="-openmp -mmic -vec-report -restrict -mGLOB_default_function_attrs=\"use_gather_scatter_hint=off\" -g -O2 -finline-functions -fno-alias -std=c++0x" CFLAGS="-mmic -vec-report -restrict -mGLOB_default_function_attrs=\"use_gather_scatter_hint=off\" -openmp -g  -O2 -fno-alias -std=c9l9" CXX=mpiicpc CC=mpiicc --host=x86_64-linux-gnu --build=none-none-none --with-qmp=$QMP_INSTALL_DIR

or for KNL:

./configure --enable-parallel-arch=parscalar --enable-proc=AVX512 --enable-soalen=8 --enable-clover --enable-openmp --enable-cean --enable-mm-malloc CXXFLAGS="-qopenmp -xMIC-AVX512 -g -O3 -std=c++14" CFLAGS="-xMIC-AVX512 -qopenmp -O3 -std=c99" CXX=mpiicpc CC=mpiicc --host=x86_64-linux-gnu --build=none-none-none --with-qmp=$QMP_INSTALL_DIR

by using the previously set variable QMP_INSTALL_DIR which links to
the folder in which the QMP library was copied. The executable
"time_clov_noqdp" should appear in the "./qphix/test" folder. Note
that the avx512-branch will compile an additional executable which has
dependencies on the package QDP (which will generate an error at the
end of the compilation process).

##
## 2.1.1 Example compilation on PRACE machines
##

In the subsection we provide some example compilation on PRACE machines
which where used to develop the QCD Benchmarksuite 2.

##
## 2.1.1.1 BSC - Marenostrum III Hybrid partitions
##

The nodes of the hybrid partition of Marenostrum are equipped with KNC
cards. First load the following modules:

module unload openmpi
module load impi

and then setup the appropriate environment with:

source /opt/intel/impi/4.1.1.036/bin64/mpivars.sh
source /opt/intel/2013.5.192/composer_xe_2013.5.192/bin/compilervars.sh intel64
export I_MPI_MIC=enable
export I_MPI_HYDRA_BOOTSTRAP=ssh

Configure and compile the QMP-library with:

./configure --prefix=$QMP_INSTALL_DIR CC=mpiicc CFLAGS="-mmic -std=c99" --with-qmp-comms-type=MPI --host=x86_64-linux-gnu --build=none-none-none

make
make install

Configure and compile QPhix with:

./configure --enable-parallel-arch=parscalar --enable-proc=MIC --enable-soalen=8 --enable-clover --enable-openmp --enable-cean --enable-mm-malloc CXXFLAGS="-openmp -mmic -vec-report -restrict -mGLOB_default_function_attrs=\"use_gather_scatter_hint=off\" -g -O2 -finline-functions -fno-alias -std=c++0x" CFLAGS="-mmic -vec-report -restrict -mGLOB_default_function_attrs=\"use_gather_scatter_hint=off\" -openmp -g  -O2 -fno-alias -std=c9l9" CXX=mpiicpc CC=mpiicc --host=x86_64-linux-gnu --build=none-none-none --with-qmp=$QMP_INSTALL_DIR
make

##
## 2.1.1.2 CINES - Frioul
##

On a test cluster at CINES the Benchmarksuite was tested on KNL cards.
The steps are similar to Marenostrum above. First setup the appropriate environment with:

source /opt/software/intel/composer_xe_2015/bin/compilervars.sh intel64
source /opt/software/intel/impi_5.0.3/bin64/mpivars.sh

Configure and compile QMP with:

./configure --prefix=$QMP_INSTALL_DIR CC=mpiicc CFLAGS="-xMIC-AVX512 -mGLOB_default_function_attrs="use_gather_scatter_hint=off" -openmp -g  -O2 -fno-alias -std=c99"  --with-qmp-comms-type=MPI --host=x86_64-linux-gnu --build=none-none-none
make
make install

Configure and compile QPhix with:

./configure --enable-parallel-arch=parscalar --enable-proc=AVX512 --enable-soalen=8 --enable-clover --enable-openmp --enable-cean --enable-mm-malloc CXXFLAGS="-qopenmp -xMIC-AVX512 -g -O3 -std=c++14" CFLAGS="-xMIC-AVX512 -qopenmp -O3 -std=c99" CXX=mpiicpc CC=mpiicc --host=x86_64-linux-gnu --build=none-none-none --with-qmp=/home/finkenrath/benchmark/qmp/install

and

make

##
##    2.2 Run
##


The Accelerator QCD-Benchmarksuite Part 2 provides bash-scripts to
setup the benchmark runs on the target machines. These are:

run_ana.sh              :   Main-script, set up the bechmark mode and submit the jobs (analyse the results)
prepare_submit_job.sh   :   Generate the job-scripts
submit_job.sh.template  :   Template for submit script

##
## 2.2.1 Main-script: "run_ana.sh"
##

The path to the executable has to be set by $PATH2EXE .  Choose a
scaling mode between Strong scaling or Weak scaling by setting the
variable sca_mode (="Strong" or ="Weak"). The lattice sizes can be set
by "gx" and "gt".  Choose between mode="Run" for run mode or
mode="Analysis" for extracting the GFLOPS. Note that the script
assumes Slurm is used as the job scheduler. If not, change the line
which includes the "sbatch" command accordingly.

##
## 2.2.2 Main-script: "prepare_submit_job.sh"
##

Add additional options if necessary.

##
## 2.2.3 Main-script: "submit_job.sh.template"
##

The submit-template will be edited by "prepare_submit_job.sh" to
generate the final submit-script. The first lines (beginning with
"#SBATCH") depend on the queuing system of the target machine, which
in this case is assumed to be Slurm. These should be changed in case
of a different queuing system.

##
## 2.3 Example Benchmark Results
##


The benchmark results for the XeonPhi benchmark suite are performed on
Frioul, a test cluster at CINES, and the hybrid partion on MareNostrum III at BSC.
Frioul has one KNL-card per node while the hybrid partion of MareNostrum III is
equiped with two KNCs per node. The data on Frioul are generated by using
the bash-scripts provided by the QCD-Accelerator Benchmarksute Part 2
and are done for the two test cases "Strong-Scaling" with a lattice size
of 32^3x96 and "Weak-scaling" with a local lattice size of 48^3x24 per
card. In case of the data generated at MareNostrum, data for the "Strong-Scaling"
mode on a 32^3x96 lattice are shown. The Benchmark is using a random gauge configuration and uses the
Conjugated Gradient solver to solve a linear equation involving the clover Wilson Dirac operator.

---------------------
  Frioul - KNLs
---------------------
Strong - Scaling:
global lattice size (32x32x32x96)

precision: single

KNLs     GFLOPS
1       340.75
2       627.612
4      1111.13
8      1779.34
16     2410.8

precision: double

KNLs     GFLOPS
1      328.149
2      616.467
4      1047.79
8      1616.37


Weak - Scaling:
local lattice size (48x48x48x24)

precision: single

KNLs   GFLOPS
1       348.304
2       616.697
4      1214.82
8      2425.45
16     4404.63

precision: double

KNLs   GFLOPS
 1      172.303
 2      320.761
 4      629.79
 8     1228.77
16     2310.63

---------------------
  MareNostrum III - KNC's
---------------------

Strong - Scaling:
global lattice size (32x32x32x96)

precision: single - 1 Cards per Node

KNCs  GFLOPS
2    103.561
4    200.159
8    338.276
16   534.369
32   815.896

precision: single - 2 Cards per Node

KNCs  GFLOPS
4    118.995
8    212.558
16   368.196
32   605.882
64   847.566