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PRACE QCD Accelerator Benchmark 1
=================================
This benchmark is part of the QCD section of the Accelerator
Benchmarks Suite developed as part of a PRACE EU funded project
(http://www.prace-ri.eu).
The suite is derived from the Unified European Applications
Benchmark Suite (UEABS) http://www.prace-ri.eu/ueabs/
This specific component is a direct port of "QCD kernel E" from the
UEABS, which is based on the MILC code suite
(http://www.physics.utah.edu/~detar/milc/). The performance-portable
targetDP model has been used to allow the benchmark to utilise NVIDIA
GPUs, Intel Xeon Phi manycore CPUs and traditional multi-core
CPUs. The use of MPI (in conjunction with targetDP) allows multiple
nodes to be used in parallel.
For full details of this benchmark, and for results on NVIDIA GPU and
Intel Knights Corner Xeon Phi architectures (in addition to regular
CPUs), please see:
**********************************************************************
Gray, Alan, and Kevin Stratford. "A lightweight approach to
performance portability with targetDP." The International Journal of
High Performance Computing Applications (2016): 1094342016682071, Also
available at https://arxiv.org/abs/1609.01479
**********************************************************************
To Build
--------
Choose a configuration file from the "config" directory that best
matches your platform, and copy to "config.mk" in this (the
top-level) directory. Then edit this file, if necessary, to properly
set the compilers and paths on your system.
Note that if you are building for a GPU system, and the TARGETCC
variable in the configuration file is set to the NVIDIA compiler nvcc,
then the build process will automatically build the GPU
version. Otherwise, the threaded CPU version will be built which can
run on Xeon Phi manycore CPUs or regular multi-core CPUs.
Then, build the targetDP performance-portable library:
cd targetDP
make clean
make
cd ..
And finally build the benchmark code
cd src
make clean
make
cd ..
To Validate
-----------
After building, an executable "bench" will exist in the src directory.
To run the default validation (64x64x64x8, 1 iteration) case:
cd src
./bench
The code will automatically self-validate by comparing with the
appropriate output reference file for this case which exists in
output_ref, and will print to stdout, e.g.
Validating against output_ref/kernel_E.output.nx64ny64nz64nt8.i1.t1:
VALIDATION PASSED
The benchmark time is also printed to stdout, e.g.
******BENCHMARK TIME 1.6767786769196391e-01 seconds******
(Where this time is as reported on an NVIDIA K40 GPU).
To Run Different Cases
---------------------
You can edit the input file
src/kernel_E.input
if you want to deviate from the default system size, number of
iterations and/or run using more than 1 MPI task. E.g. replacing
totnodes 1 1 1 1
with
totnodes 2 1 1 1
will run with 2 MPI tasks rather than 1, where the domain is decomposed in
the "X" direction.
To Run using a Script
---------------------
The "run" directory contains an example script which
- sets up a temporary scratch directory
- copies in the input file, plus also some reference output files
- sets the number of OpenMP threads (for a multi/many core CPU run)
- runs the code (which will automatically validate if an
appropriate output reference file exists)
So, in the run directory, you should copy "run_example.sh" to
run.sh, which you can customise for your system.
Known Issues
------------
The quantity used for validation (see congrad.C) becomes very small
after a few iterations. Therefore, only a small number of iterations
should be used for validation. This is not an issue specific to this
port of the benchmark, but is also true of the original version (see
above), with which this version is designed to be consistent.
Performance Results for Reference
--------------------------------
Here are some performance timings obtained using this benchmark.
From the paper cited above:
64x64x64x32x8, 1000 iterations, single chip
Chip Time (s)
Intel Ivy-Bridge 12-core CPU 361.55
Intel Haswell 8-core CPU 376.08
AMD Opteron 16-core CPU 618.19
Intel KNC Xeon Phi 139.94
NVIDIA K20X GPU 96.84
NVIDIA K40 GPU 90.90
Multi-node scaling:
Titan GPU (one K20X per node)
Titan CPU (one 16-core Interlagos per node)
ARCHER CPU (two 12-core Ivy-bridge per node)
All times in seconds.
Small Case: 64x64x32x8, 1000 iterations
Nodes Titan GPU Titan CPU ARCHER CPU
1 9.64E+01 6.01E+02 1.86E+02
2 5.53E+01 3.14E+02 9.57E+01
4 3.30E+01 1.65E+02 5.22E+01
8 2.18E+01 8.33E+01 2.60E+01
16 1.35E+01 4.02E+01 1.27E+01
32 8.80E+00 2.06E+01 6.49E+00
64 6.54E+00 9.90E+00 2.36E+00
128 5.13E+00 4.31E+00 1.86E+00
256 4.25E+00 2.95E+00 1.96E+00
Large Case: 64x64x64x192, 1000 iterations
Nodes Titan GPU Titan CPU ARCHER CPU
64 1.36E+02 5.19E+02 1.61E+02
128 8.23E+01 2.75E+02 8.51E+01
256 6.70E+01 1.61E+02 4.38E+01
512 3.79E+01 8.80E+01 2.18E+01
1024 2.41E+01 5.72E+01 1.46E+01
2048 1.81E+01 3.88E+01 7.35E+00
4096 1.56E+01 2.28E+01 6.53E+00
Preliminary results on new Pascal GPU and Intel KNL architectures:
Single chip, 64x64x64x8, 1000 iterations
Chip Time (s)
12-core Intel Ivy-Bridge 7.24E+02
Intel KNL Xeon Phi 9.72E+01
NVIDIA P100 GPU 5.60E+01
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###
### 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