Commit e79ad69a authored by Kurt Lust's avatar Kurt Lust
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Updated the links for gpaw in the common benchmark page.

parent 71136842
...@@ -46,10 +46,10 @@ The application codes that constitute the UEABS are: ...@@ -46,10 +46,10 @@ The application codes that constitute the UEABS are:
The Alya System is a Computational Mechanics code capable of solving different physics, each one with its own modelization characteristics, in a coupled way. Among the problems it solves are: convection-diffusion reactions, incompressible flows, compressible flows, turbulence, bi-phasic flows and free surface, excitable media, acoustics, thermal flow, quantum mechanics (DFT) and solid mechanics (large strain). ALYA is written in Fortran 90/95 and parallelized using MPI and OpenMP. The Alya System is a Computational Mechanics code capable of solving different physics, each one with its own modelization characteristics, in a coupled way. Among the problems it solves are: convection-diffusion reactions, incompressible flows, compressible flows, turbulence, bi-phasic flows and free surface, excitable media, acoustics, thermal flow, quantum mechanics (DFT) and solid mechanics (large strain). ALYA is written in Fortran 90/95 and parallelized using MPI and OpenMP.
- Web site: https://www.bsc.es/computer-applications/alya-system - Web site: https://www.bsc.es/computer-applications/alya-system
- Code download: https://repository.prace-ri.eu/ueabs/ALYA/2.1/Alya.tar.gz - Code download: https://repository.prace-ri.eu/ueabs/ALYA/2.1/Alya.tar.gz
- Build instructions: https://repository.prace-ri.eu/git/UEABS/ueabs/blob/r2.1/alya/ALYA_Build_README.txt - Build instructions: https://repository.prace-ri.eu/git/UEABS/ueabs/blob/r2.1/alya/ALYA_Build_README.txt
- Test Case A: https://repository.prace-ri.eu/ueabs/ALYA/2.1/TestCaseA.tar.gz - Test Case A: https://repository.prace-ri.eu/ueabs/ALYA/2.1/TestCaseA.tar.gz
- Test Case B: https://repository.prace-ri.eu/ueabs/ALYA/2.1/TestCaseB.tar.gz - Test Case B: https://repository.prace-ri.eu/ueabs/ALYA/2.1/TestCaseB.tar.gz
- Run instructions: https://repository.prace-ri.eu/git/UEABS/ueabs/blob/r2.1/alya/ALYA_Run_README.txt - Run instructions: https://repository.prace-ri.eu/git/UEABS/ueabs/blob/r2.1/alya/ALYA_Run_README.txt
# Code_Saturne <a name="saturne"></a> # Code_Saturne <a name="saturne"></a>
...@@ -109,14 +109,15 @@ The equations of the (time-dependent) density functional theory within the PAW m ...@@ -109,14 +109,15 @@ The equations of the (time-dependent) density functional theory within the PAW m
The program offers several parallelization levels. The most basic parallelization strategy is domain decomposition over the real-space grid. In magnetic systems it is possible to parallelize over spin, and in systems that have k-points (surfaces or bulk systems) parallelization over k-points is also possible. Furthermore, parallelization over electronic states is possible in DFT and in real-time TD-DFT calculations. GPAW is written in Python and C and parallelized with MPI. The program offers several parallelization levels. The most basic parallelization strategy is domain decomposition over the real-space grid. In magnetic systems it is possible to parallelize over spin, and in systems that have k-points (surfaces or bulk systems) parallelization over k-points is also possible. Furthermore, parallelization over electronic states is possible in DFT and in real-time TD-DFT calculations. GPAW is written in Python and C and parallelized with MPI.
- Web site: https://wiki.fysik.dtu.dk/gpaw/ - Web site: https://wiki.fysik.dtu.dk/gpaw/
- Code download: https://gitlab.com/gpaw/gpaw - Code download: [gpaw GitLab repository]()https://gitlab.com/gpaw/gpaw) or [gpaw on
- Build instructions: [gpaw/README.md#install](gpaw/README.md#install) PyPi](https://pypi.org/project/gpaw/)
- Build instructions: [gpaw README, section "Mechanics of building the benchmark"](gpaw/README.md#mechanics-of-building-the-benchmark)
- Benchmarks: - Benchmarks:
- [Case S: Carbon nanotube](gpaw/benchmark/carbon-nanotube) - [Case S: Carbon nanotube](gpaw/benchmark/1_S_carbon-nanotube/input.py)
- [Case M: Copper filament](gpaw/benchmark/copper-filament) - [Case M: Copper filament](gpaw/benchmark/2_M_copper-filament/input.py)
- [Case L: Silicon cluster](gpaw/benchmark/silicon-cluster) - [Case L: Silicon cluster](gpaw/benchmark/3_L_silicon-cluster/input.py)
- Run instructions: - Run instructions:
[gpaw/README.md#running-the-benchmarks](gpaw/README.md#running-the-benchmarks) [gpaw README, section "Mechanics of running the benchmark"](gpaw/README.md#mechanics-of-running-the-benchmark)
# GROMACS <a name="gromacs"></a> # GROMACS <a name="gromacs"></a>
...@@ -173,21 +174,21 @@ NAMD is written in C++ and parallelised using Charm++ parallel objects, which ar ...@@ -173,21 +174,21 @@ NAMD is written in C++ and parallelised using Charm++ parallel objects, which ar
# NEMO <a name="nemo"></a> # NEMO <a name="nemo"></a>
NEMO (Nucleus for European Modelling of the Ocean) [22] is mathematical modelling framework for research activities and prediction services in ocean and climate sciences developed by European consortium. It is intended to be tool for studying the ocean and its interaction with the other components of the earth climate system over a large number of space and time scales. It comprises of the core engines namely OPA (ocean dynamics and thermodynamics), SI3 (sea ice dynamics and thermodynamics), TOP (oceanic tracers) and PISCES (biogeochemical process). NEMO (Nucleus for European Modelling of the Ocean) [22] is mathematical modelling framework for research activities and prediction services in ocean and climate sciences developed by European consortium. It is intended to be tool for studying the ocean and its interaction with the other components of the earth climate system over a large number of space and time scales. It comprises of the core engines namely OPA (ocean dynamics and thermodynamics), SI3 (sea ice dynamics and thermodynamics), TOP (oceanic tracers) and PISCES (biogeochemical process).
Prognostic variables in NEMO are the three-dimensional velocity field, a linear or non-linear sea surface height, the temperature and the salinity. Prognostic variables in NEMO are the three-dimensional velocity field, a linear or non-linear sea surface height, the temperature and the salinity.
In the horizontal direction, the model uses a curvilinear orthogonal grid and in the vertical direction, a full or partial step z-coordinate, or s-coordinate, or a mixture of the two. The distribution of variables is a three-dimensional Arakawa C-type grid for most of the cases. In the horizontal direction, the model uses a curvilinear orthogonal grid and in the vertical direction, a full or partial step z-coordinate, or s-coordinate, or a mixture of the two. The distribution of variables is a three-dimensional Arakawa C-type grid for most of the cases.
The model is implemented in Fortran 90, with preprocessing (C-pre-processor). It is optimized for vector computers and parallelized by domain decomposition with MPI. It supports modern C/C++ and Fortran compilers. All input and output is done with third party software called XIOS with dependency on NetCDF (Network Common Data Format) and HDF5. It is highly scalable and perfect application for measuring supercomputing performances in terms of compute capacity, memory subsystem, I/O and interconnect performance. The model is implemented in Fortran 90, with preprocessing (C-pre-processor). It is optimized for vector computers and parallelized by domain decomposition with MPI. It supports modern C/C++ and Fortran compilers. All input and output is done with third party software called XIOS with dependency on NetCDF (Network Common Data Format) and HDF5. It is highly scalable and perfect application for measuring supercomputing performances in terms of compute capacity, memory subsystem, I/O and interconnect performance.
### Test Case Description ### Test Case Description
The GYRE configuration has been built to model seasonal cycle of double gyre box model. It consists of idealized domain over which seasonal forcing is applied. This allows for studying large number of interactions and their combined contribution to large scale circulation. The GYRE configuration has been built to model seasonal cycle of double gyre box model. It consists of idealized domain over which seasonal forcing is applied. This allows for studying large number of interactions and their combined contribution to large scale circulation.
The domain geometry is rectangular bounded by vertical walls and flat bottom. The configuration is meant to represent idealized north Atlantic or north pacific basin. The circulation is forced by analytical profiles of wind and buoyancy fluxes. The domain geometry is rectangular bounded by vertical walls and flat bottom. The configuration is meant to represent idealized north Atlantic or north pacific basin. The circulation is forced by analytical profiles of wind and buoyancy fluxes.
The wind stress is zonal and its curl changes sign at 22 and 36. It forces a subpolar gyre in the north, a subtropical gyre in the wider part of the domain and a small recirculation gyre in the southern corner. The net heat flux takes the form of a restoring toward a zonal apparent air temperature profile. The wind stress is zonal and its curl changes sign at 22 and 36. It forces a subpolar gyre in the north, a subtropical gyre in the wider part of the domain and a small recirculation gyre in the southern corner. The net heat flux takes the form of a restoring toward a zonal apparent air temperature profile.
A portion of the net heat flux which comes from the solar radiation is allowed to penetrate within the water column. The fresh water flux is also prescribed and varies zonally. It is determined such as, at each time step, the basin-integrated flux is zero. A portion of the net heat flux which comes from the solar radiation is allowed to penetrate within the water column. The fresh water flux is also prescribed and varies zonally. It is determined such as, at each time step, the basin-integrated flux is zero.
The basin is initialized at rest with vertical profiles of temperature and salinity uniformity applied to the whole domain. The GYRE configuration is set through the namelist_cfg file. The basin is initialized at rest with vertical profiles of temperature and salinity uniformity applied to the whole domain. The GYRE configuration is set through the namelist_cfg file.
The horizontal resolution is determined by setting jp_cfg as follows: The horizontal resolution is determined by setting jp_cfg as follows:
...@@ -209,7 +210,7 @@ In this configuration, we use default value of 30 ocean levels depicted by jpk=3 ...@@ -209,7 +210,7 @@ In this configuration, we use default value of 30 ocean levels depicted by jpk=3
**Test Case B** **Test Case B**
* jp_cfg = 256 suitable up to 20,000 cores. * jp_cfg = 256 suitable up to 20,000 cores.
* Number of Days (real): 80 * Number of Days (real): 80
* Number of time step: 4320 * Number of time step: 4320
* Time step size(real): 20 mins * Time step size(real): 20 mins
* Number of seconds per time step: 1200 * Number of seconds per time step: 1200
...@@ -220,19 +221,19 @@ In this configuration, we use default value of 30 ocean levels depicted by jpk=3 ...@@ -220,19 +221,19 @@ In this configuration, we use default value of 30 ocean levels depicted by jpk=3
# PFARM <a name="pfarm"></a> # PFARM <a name="pfarm"></a>
PFARM is part of a suite of programs based on the ‘R-matrix’ ab-initio approach to the variational solution of the many-electron Schrödinger PFARM is part of a suite of programs based on the ‘R-matrix’ ab-initio approach to the variational solution of the many-electron Schrödinger
equation for electron-atom and electron-ion scattering. The package has been used to calculate electron collision data for astrophysical equation for electron-atom and electron-ion scattering. The package has been used to calculate electron collision data for astrophysical
applications (such as: the interstellar medium, planetary atmospheres) with, for example, various ions of Fe and Ni and neutral O, plus applications (such as: the interstellar medium, planetary atmospheres) with, for example, various ions of Fe and Ni and neutral O, plus
other applications such as data for plasma modelling and fusion reactor impurities. The code has recently been adapted to form a compatible other applications such as data for plasma modelling and fusion reactor impurities. The code has recently been adapted to form a compatible
interface with the UKRmol suite of codes for electron (positron) molecule collisions thus enabling large-scale parallel ‘outer-region’ interface with the UKRmol suite of codes for electron (positron) molecule collisions thus enabling large-scale parallel ‘outer-region’
calculations for molecular systems as well as atomic systems. calculations for molecular systems as well as atomic systems.
The PFARM outer-region application code EXDIG is domi-nated by the assembly of sector Hamiltonian matrices and their subsequent eigensolutions. The PFARM outer-region application code EXDIG is domi-nated by the assembly of sector Hamiltonian matrices and their subsequent eigensolutions.
The code is written in Fortran 2003 (or Fortran 2003-compliant Fortran 95), is parallelised using MPI and OpenMP and is designed to take The code is written in Fortran 2003 (or Fortran 2003-compliant Fortran 95), is parallelised using MPI and OpenMP and is designed to take
advantage of highly optimised, numerical library routines. Hybrid MPI / OpenMP parallelisation has also been introduced into the code via advantage of highly optimised, numerical library routines. Hybrid MPI / OpenMP parallelisation has also been introduced into the code via
shared memory enabled numerical library kernels. shared memory enabled numerical library kernels.
Accelerator-based implementations have been implemented for EXDIG, using off-loading (MKL or CuBLAS/CuSolver) for the standard (dense) eigensolver calculations that dominate overall run-time. Accelerator-based implementations have been implemented for EXDIG, using off-loading (MKL or CuBLAS/CuSolver) for the standard (dense) eigensolver calculations that dominate overall run-time.
Code download: https://repository.prace-ri.eu/git/UEABS/ueabs/-/tree/r2.2-dev/pfarm Code download: https://repository.prace-ri.eu/git/UEABS/ueabs/-/tree/r2.2-dev/pfarm
- Build & Run instructions: https://repository.prace-ri.eu/git/UEABS/ueabs/-/tree/r2.2-dev/pfarm/PFARM_Build_Run_README.txt - Build & Run instructions: https://repository.prace-ri.eu/git/UEABS/ueabs/-/tree/r2.2-dev/pfarm/PFARM_Build_Run_README.txt
...@@ -268,7 +269,7 @@ QUANTUM ESPRESSO is written mostly in Fortran90, and parallelised using MPI and ...@@ -268,7 +269,7 @@ QUANTUM ESPRESSO is written mostly in Fortran90, and parallelised using MPI and
The Scalable HeterOgeneous Computing (SHOC) benchmark suite is a collection of benchmark programs testing the performance and stability of systems using computing devices with non-traditional architectures The Scalable HeterOgeneous Computing (SHOC) benchmark suite is a collection of benchmark programs testing the performance and stability of systems using computing devices with non-traditional architectures
for general purpose computing. It serves as synthetic benchmark suite in the UEABS context. Its initial focus is on systems containing Graphics Processing Units (GPUs) and multi-core processors, featuring implementations using both CUDA and OpenCL. It can be used on clusters as well as individual hosts. for general purpose computing. It serves as synthetic benchmark suite in the UEABS context. Its initial focus is on systems containing Graphics Processing Units (GPUs) and multi-core processors, featuring implementations using both CUDA and OpenCL. It can be used on clusters as well as individual hosts.
Also, SHOC includes an Offload branch for the benchmarks that can be used to evaluate the Intel Xeon Phi x100 family. Also, SHOC includes an Offload branch for the benchmarks that can be used to evaluate the Intel Xeon Phi x100 family.
The SHOC benchmark suite currently contains benchmark programs, categoried based on complexity. Some measure low-level "feeds and speeds" behavior (Level 0), some measure the performance of a higher-level operation such as a Fast Fourier Transform (FFT) (Level 1), and the others measure real application kernels (Level 2). The SHOC benchmark suite currently contains benchmark programs, categoried based on complexity. Some measure low-level "feeds and speeds" behavior (Level 0), some measure the performance of a higher-level operation such as a Fast Fourier Transform (FFT) (Level 1), and the others measure real application kernels (Level 2).
...@@ -286,11 +287,11 @@ The SHOC benchmark suite currently contains benchmark programs, categoried based ...@@ -286,11 +287,11 @@ The SHOC benchmark suite currently contains benchmark programs, categoried based
# TensorFlow <a name="tensorflow"></a> # TensorFlow <a name="tensorflow"></a>
TensorFlow (https://www.tensorflow.org) is a popular open-source library for symbolic math and linear algebra, with particular optimization for neural-networks-based machine learning workflow. Maintained by Google, it is widely used for research and production in both the academia and the industry. TensorFlow (https://www.tensorflow.org) is a popular open-source library for symbolic math and linear algebra, with particular optimization for neural-networks-based machine learning workflow. Maintained by Google, it is widely used for research and production in both the academia and the industry.
TensorFlow supports a wide variety of hardware platforms (CPUs, GPUs, TPUs), and can be scaled up to utilize multiple compute devices on a single or multiple compute nodes. The main objective of this benchmark is to profile the scaling behavior of TensorFlow on different hardware, and thereby provide a reference baseline of its performance for different sizes of applications. TensorFlow supports a wide variety of hardware platforms (CPUs, GPUs, TPUs), and can be scaled up to utilize multiple compute devices on a single or multiple compute nodes. The main objective of this benchmark is to profile the scaling behavior of TensorFlow on different hardware, and thereby provide a reference baseline of its performance for different sizes of applications.
There are many open-source datasets available for benchmarking TensorFlow, such as `mnist`, `fashion_mnist`, `cifar`, `imagenet`, and so on. This benchmark suite, however, would like to focus on a scientific research use case. `DeepGalaxy` is a code built with TensorFlow, which uses deep neural network to classify galaxy mergers in the Universe, observed by the Hubble Space Telescope and the Sloan Digital Sky Survey. There are many open-source datasets available for benchmarking TensorFlow, such as `mnist`, `fashion_mnist`, `cifar`, `imagenet`, and so on. This benchmark suite, however, would like to focus on a scientific research use case. `DeepGalaxy` is a code built with TensorFlow, which uses deep neural network to classify galaxy mergers in the Universe, observed by the Hubble Space Telescope and the Sloan Digital Sky Survey.
- Website: https://github.com/maxwelltsai/DeepGalaxy - Website: https://github.com/maxwelltsai/DeepGalaxy
- Code download: https://github.com/maxwelltsai/DeepGalaxy - Code download: https://github.com/maxwelltsai/DeepGalaxy
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