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# Unified European Applications Benchmark Suite
The Unified European Application Benchmark Suite (UEABS) is a set of currently 13 application codes taken from the pre-existing PRACE and DEISA application benchmark suites, and extended with the PRACE Accelerator Benchmark Suite. The objective is providing a single benchmark suite of scalable, currently relevant and publicly available application codes and datasets, of a size which can realistically be run on large systems, and maintained into the future.
The UEABS activity was started during the PRACE-PP project and was publicly released by the PRACE-2IP project.
The PRACE "Accelerator Benchmark Suite" was a PRACE-4IP activity.
The UEABS has been and will be actively updated and maintained by the subsequent PRACE-IP projects.
Each application code has either one, or two input datasets. If there are two datasets, Test Case A is designed to run on Tier-1 sized systems (up to around 1,000 x86 cores, or equivalent) and Test Case B is designed to run on Tier-0 sized systems (up to around 10,000 x86 cores, or equivalent). If there is only one dataset (Test Case A), it is suitable for both sizes of system.
Contacts: Valeriu Codreanu <mailto:valeriu.codreanu@surfsara.nl> or Walter Lioen <mailto:walter.lioen@surfsara.nl>
Current Release
---------------
The current release is Version 2.1 (April 30, 2019).
Instructions to run each test cases of each codes can be found in the subdirectories of this repository.
For more details of the codes and datasets, and sample results, please see the PRACE-5IP benchmarking deliverable D7.5 "Evaluation of Accelerated and Non-accelerated Benchmarks" (April 18, 2019) at http://www.prace-ri.eu/public-deliverables/ .
The application codes that constitute the UEABS are:
---------------------------------------------------
- [ALYA](#alya)
- [Code_Saturne](#saturne)
- [CP2K](#cp2k)
- [GADGET](#gadget)
- [GPAW](#gpaw)
- [GROMACS](#gromacs)
- [NAMD](#namd)
- [NEMO](#nemo)
# ALYA <a name="alya"></a>
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
- 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
- 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
- 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 is open-source multi-purpose CFD software, primarily developed by EDF R&D and maintained by them. It relies on the Finite Volume method and a collocated arrangement of unknowns to solve the Navier-Stokes equations, for incompressible or compressible flows, laminar or turbulent flows and non-Newtonian and Newtonian fluids. A highly parallel coupling library (Parallel Locator Exchange - PLE) is also available in the distribution to account for other physics, such as conjugate heat transfer and structure mechanics. For the incompressible solver, the pressure is solved using an integrated Algebraic Multi-Grid algorithm and the scalars are computed by conjugate gradient methods or Gauss-Seidel/Jacobi.
The original version of the code is written in C for pre-postprocessing, IO handling, parallelisation handling, linear solvers and gradient computation, and Fortran 95 for most of the physics implementation. MPI is used on distributed memory machines and OpenMP pragmas have been added to the most costly parts of the code to handle potential shared memory. The version used in this work (also freely available) relies also on CUDA to take advantage of potential GPU acceleration.
The equations are solved iteratively using time-marching algorithms, and most of the time spent during a time step is usually due to the computation of the velocity-pressure coupling, for simple physics. For this reason, the two test cases chosen for the benchmark suite have been designed to assess the velocity-pressure coupling computation, and rely on the same configuration, with a mesh 8 times larger for Test Case B than for Test Case A, the time step being halved to ensure a correct Courant number.
- Web site: https://code-saturne.org
- Code download: https://repository.prace-ri.eu/ueabs/Code_Saturne/2.1/CS_5.3_PRACE_UEABS.tar.gz
- Disclaimer: please note that by downloading the code from this website, you agree to be bound by the terms of the GPL license.
- Build and Run instructions: [code_saturne/Code_Saturne_Build_Run_5.3_UEABS.pdf](code_saturne/Code_Saturne_Build_Run_5.3_UEABS.pdf)
- Test Case A: https://repository.prace-ri.eu/ueabs/Code_Saturne/2.1/CS_5.3_PRACE_UEABS_CAVITY_13M.tar.gz
- Test Case B: https://repository.prace-ri.eu/ueabs/Code_Saturne/2.1/CS_5.3_PRACE_UEABS_CAVITY_111M.tar.gz
CP2K is a freely available quantum chemistry and solid-state physics software package that can perform atomistic simulations of solid state, liquid, molecular, periodic, material, crystal, and biological systems. CP2K provides a general framework for different modelling methods such as DFT using the mixed Gaussian and plane waves approaches GPW and GAPW. Supported theory levels include DFTB, LDA, GGA, MP2, RPA, semi-empirical methods (AM1, PM3, PM6, RM1, MNDO, ...), and classical force fields (AMBER, CHARMM, ...). CP2K can do simulations of molecular dynamics, metadynamics, Monte Carlo, Ehrenfest dynamics, vibrational analysis, core level spectroscopy, energy minimisation, and transition state optimisation using NEB or dimer method.
CP2K is written in Fortran 2008 and can be run in parallel using a combination of multi-threading, MPI, and CUDA. All of CP2K is MPI parallelised, with some additional loops also being OpenMP parallelised. It is therefore most important to take advantage of MPI parallelisation, however running one MPI rank per CPU core often leads to memory shortage. At this point OpenMP threads can be used to utilise all CPU cores without suffering an overly large memory footprint. The optimal ratio between MPI ranks and OpenMP threads depends on the type of simulation and the system in question. CP2K supports CUDA, allowing it to offload some linear algebra operations including sparse matrix multiplications to the GPU through its DBCSR acceleration layer. FFTs can optionally also be offloaded to the GPU. Benefits of GPU offloading may yield improved performance depending on the type of simulation and the system in question.
- Code download: https://github.com/cp2k/cp2k/releases
- [Build & run instructions, details about benchmarks](./cp2k/README.md)
- Benchmarks:
- [Test Case A](./cp2k/benchmarks/TestCaseA_H2O-512)
- [Test Case B](./cp2k/benchmarks/TestCaseB_LiH-HFX)
- [Test Case C](./cp2k/benchmarks/TestCaseC_H2O-DFT-LS)
GADGET-4 (GAlaxies with Dark matter and Gas intEracT), an evolved and improved version of GADGET-3, is a freely available code for cosmological N-body/SPH simulations on massively parallel computers with distributed memory written mainly by Volker Springel, Max-Plank-Institute for Astrophysics, Garching, Germany, nd benefiting from numerous contributions, including Ruediger Pakmor, Oliver Zier, and Martin Reinecke. GADGET-4 supports collisionless simulations and smoothed particle hydrodynamics on massively parallel computers. All communication between concurrent execution processes is done either explicitly by means of the message passing interface (MPI), or implicitly through shared-memory accesses on processes on multi-core nodes. The code is mostly written in ISO C++ (assuming the C++11 standard), and should run on all parallel platforms that support at least MPI-3. So far, the compatibility of the code with current Linux/UNIX-based platforms has been confirmed on a large number of systems.
The code can be used for plain Newtonian dynamics, or for cosmological integrations in arbitrary cosmologies, both with or without periodic boundary conditions. Stretched periodic boxes, and special cases such as simulations with two periodic dimensions and one non-periodic dimension are supported as well. The modeling of hydrodynamics is optional. The code is adaptive both in space and in time, and its Lagrangian character makes it particularly suitable for simulations of cosmic structure formation. Several post-processing options such as group- and substructure finding, or power spectrum estimation are built in and can be carried out on the fly or applied to existing snapshots. Through a built-in cosmological initial conditions generator, it is also particularly easy to carry out cosmological simulations. In addition, merger trees can be determined directly by the code.
- Web site: https://wwwmpa.mpa-garching.mpg.de/gadget4
- Code download: https://gitlab.mpcdf.mpg.de/vrs/gadget4
- Build and run instructions: https://wwwmpa.mpa-garching.mpg.de/gadget4/02_running.html
- [Case A: Colliding galaxies with star formation](./gadget/4.0/gadget4_caseA.tar.gz)
- [Case B: Cosmological DM-only simulation with IC creation](./gadget/4.0/gadget4-caseB.tar.gz)
- [Case C: Adiabatic collapse of a gas sphere](./gadget/4.0/gadget4-caseC.tar.gz)
- [Code used in the benchmarks](./gadget/4.0/gadget4.tar.gz)
- [Build & run instructions, details about the benchmarks](./gadget/4.0/README.md)
# GPAW <a name="gpaw"></a>
GPAW is an efficient program package for electronic structure calculations based on the density functional theory (DFT) and the time-dependent density functional theory (TD-DFT). The density-functional theory allows studies of ground state properties such as energetics and equilibrium geometries, while the time-dependent density functional theory can be used for calculating excited state properties such as optical spectra. The program package includes two complementary implementations of time-dependent density functional theory: a linear response formalism and a time-propagation in real time.
The program uses the projector augmented wave (PAW) method that allows one to get rid of the core electrons and work with soft pseudo valence wave functions. The PAW method can be applied on the same footing to all elements, for example, it provides a reliable description of the transition metal elements and the first row elements with open p-shells that are often problematic for standard pseudopotentials. A further advantage of the PAW method is that it is an all-electron method (frozen core approximation) and there is a one to one transformation between the pseudo and all-electron quantities.
The equations of the (time-dependent) density functional theory within the PAW method are discretized using finite-differences and uniform real-space grids. The real-space representation allows flexible boundary conditions, as the system can be finite or periodic in one, two or three dimensions (e.g. cluster, slab, bulk). The accuracy of the discretization is controlled basically by single parameter, the grid spacing. The real-space representation allows also efficient parallelization with domain decomposition.
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/
- Code download: https://gitlab.com/gpaw/gpaw
- Build instructions: [gpaw/README.md#install](gpaw/README.md#install)
- Benchmarks:
- [Case S: Carbon nanotube](gpaw/benchmark/carbon-nanotube)
- [Case M: Copper filament](gpaw/benchmark/copper-filament)
- [Case L: Silicon cluster](gpaw/benchmark/silicon-cluster)
- Run instructions:
[gpaw/README.md#running-the-benchmarks](gpaw/README.md#running-the-benchmarks)
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# GROMACS <a name="gromacs"></a>
GROMACS is a versatile package to perform molecular dynamics, i.e. simulate the Newtonian equations of motion for systems with hundreds to millions of particles.
It is primarily designed for biochemical molecules like proteins, lipids and nucleic acids that have a lot of complicated bonded interactions, but since GROMACS is extremely fast at calculating the nonbonded interactions (that usually dominate simulations) many groups are also using it for research on non-biological systems, e.g. polymers.
GROMACS supports all the usual algorithms you expect from a modern molecular dynamics implementation, (check the online reference or manual for details), but there are also quite a few features that make it stand out from the competition:
- GROMACS provides extremely high performance compared to all other programs. A lot of algorithmic optimizations have been introduced in the code; we have for instance extracted the calculation of the virial from the innermost loops over pairwise interactions, and we use our own software routines to calculate the inverse square root. In GROMACS 4.6 and up, on almost all common computing platforms, the innermost loops are written in C using intrinsic functions that the compiler transforms to SIMD machine instructions, to utilize the available instruction-level parallelism. These kernels are available in either single and double precision, and in support all the different kinds of SIMD support found in x86-family (and other) processors.
- Also since GROMACS 4.6, we have excellent CUDA-based GPU acceleration on GPUs that have Nvidia compute capability >= 2.0 (e.g. Fermi or later)
- GROMACS is user-friendly, with topologies and parameter files written in clear text format. There is a lot of consistency checking, and clear error messages are issued when something is wrong. Since a C preprocessor is used, you can have conditional parts in your topologies and include other files. You can even compress most files and GROMACS will automatically pipe them through gzip upon reading.
- There is no scripting language – all programs use a simple interface with command line options for input and output files. You can always get help on the options by using the -h option, or use the extensive manuals provided free of charge in electronic or paper format.
- As the simulation is proceeding, GROMACS will continuously tell you how far it has come, and what time and date it expects to be finished.
- Both run input files and trajectories are independent of hardware endian-ness, and can thus be read by any version GROMACS, even if it was compiled using a different floating-point precision.
- GROMACS can write coordinates using lossy compression, which provides a very compact way of storing trajectory data. The accuracy can be selected by the user.
- GROMACS comes with a large selection of flexible tools for trajectory analysis – you won’t have to write any code to perform routine analyses. The output is further provided in the form of finished Xmgr/Grace graphs, with axis labels, legends, etc. already in place!
- A basic trajectory viewer that only requires standard X libraries is included, and several external visualization tools can read the GROMACS file formats.
- GROMACS can be run in parallel, using either the standard MPI communication protocol, or via our own “Thread MPI” library for single-node workstations.
- GROMACS contains several state-of-the-art algorithms that make it possible to extend the time steps is simulations significantly, and thereby further enhance performance without sacrificing accuracy or detail.
- The package includes a fully automated topology builder for proteins, even multimeric structures. Building blocks are available for the 20 standard aminoacid residues as well as some modified ones, the 4 nucleotide and 4 deoxinucleotide resides, several sugars and lipids, and some special groups like hemes and several small molecules.
- There is ongoing development to extend GROMACS with interfaces both to Quantum Chemistry and Bioinformatics/databases.
- GROMACS is Free Software, available under the GNU Lesser General Public License (LGPL), version 2.1. You can redistribute it and/or modify it under the terms of the LGPL as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version.
Instructions:
- Web site: http://www.gromacs.org/
- Code download: http://www.gromacs.org/Downloads The UEABS benchmark cases require the use of a 2020 or newer versio.
- Test Case A: https://repository.prace-ri.eu/ueabs/GROMACS/2.2/GROMACS_TestCaseA.tar.xz
- Test Case B: https://repository.prace-ri.eu/ueabs/GROMACS/2.2/GROMACS_TestCaseB.tar.xz
- Test Case C: https://repository.prace-ri.eu/ueabs/GROMACS/2.2/GROMACS_TestCaseC.tar.xz
- Build and Run Instructions : https://repository.prace-ri.eu/git/UEABS/ueabs/-/blob/r2.2-dev/gromacs/README.md
# NAMD <a name="namd"></a>
NAMD is a widely used molecular dynamics application designed to simulate bio-molecular systems on a wide variety of compute platforms. NAMD is developed by the “Theoretical and Computational Biophysics Group” at the University of Illinois at Urbana Champaign. In the design of NAMD particular emphasis has been placed on scalability when utilizing a large number of processors. The application can read a wide variety of different file formats, for example force fields, protein structure, which are commonly used in bio-molecular science.
A NAMD license can be applied for on the developer’s website free of charge. Once the license has been obtained, binaries for a number of platforms and the source can be downloaded from the website.
Deployment areas of NAMD include pharmaceutical research by academic and industrial users. NAMD is particularly suitable when the interaction between a number of proteins or between proteins and other chemical substances is of interest. Typical examples are vaccine research and transport processes through cell membrane proteins.
NAMD is written in C++ and parallelised using Charm++ parallel objects, which are implemented on top of MPI.
- Web site: http://www.ks.uiuc.edu/Research/namd/
- Code download: https://repository.prace-ri.eu/git/UEABS/ueabs/blob/r1.3/namd/NAMD_Download_README.txt
- Build instructions: https://repository.prace-ri.eu/git/UEABS/ueabs/blob/r1.3/namd/NAMD_Build_README.txt
- Test Case A: https://repository.prace-ri.eu/ueabs/NAMD/2.2/NAMD_TestCaseA.tar.gz
- Test Case B: https://repository.prace-ri.eu/ueabs/NAMD/2.2/NAMD_TestCaseB.tar.gz
- Test Case C: https://repository.prace-ri.eu/ueabs/NAMD/2.2/NAMD_TestCaseC.tar.gz
- Build and Run Instructions : https://repository.prace-ri.eu/git/UEABS/ueabs/-/blob/r2.2-dev/NAMD/README.md
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.
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 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 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.
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:
`Jpjglo = 20 x jp_cfg + 2`
In this configuration, we use default value of 30 ocean levels depicted by jpk=31. The GYRE configuration is an ideal case for benchmark test as it is very simple to increase the resolution and perform both weak and strong scalability experiment using the same input files. We use two configurations as follows:
* jp_cfg = 128 suitable up to 1000 cores
* Number of Days: 20
* Number of Time steps: 1440
* Time step size: 20 mins
* jp_cfg = 256 suitable up to 20,000 cores.
* Number of Days (real): 80
* Number of time step: 4320
* Time step size(real): 20 mins
* Number of seconds per time step: 1200
* Web site: <http://www.nemo-ocean.eu/>
* Download, Build and Run Instructions : <https://repository.prace-ri.eu/git/UEABS/ueabs/tree/master/nemo>
# 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
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
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’
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 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
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.
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
- Test Case 1: https://repository.prace-ri.eu/ueabs/PFARM/2.2/test_case_1_atom
- Test Case 2: https://repository.prace-ri.eu/ueabs/PFARM/2.2/test_case_2_mol
| **General information** | **Scientific field** | **Language** | **MPI** | **OpenMP** | **GPU** | **LoC** | **Code description** |
|------------------|----------------------|--------------|---------|------------|---------------------|---------|-------------------------------------------------------------------------------------------------------------------------------------------------------|
| <br>[- Bench](https://repository.prace-ri.eu/git/UEABS/ueabs/-/tree/r2.2-dev/qcd/part_1) <br>[- Summary](https://repository.prace-ri.eu/git/UEABS/ueabs/-/blob/r2.2-dev/qcd/part_1/README.md) | lattice Quantum Chromodynamics Part 1 | C | yes | yes | yes (CUDA) | -- | Accelerator enabled kernel E of UEABS QCD CPU part using targetDP model. Test case A - 8x64x64x64. Conjugate Gradient solver involving Wilson Dirac stencil. Domain Decomposition, Memory bandwidth, strong scaling, MPI latency. |
| <br>[- Source](https://lattice.github.io/quda/) <br>[- Bench](https://repository.prace-ri.eu/git/UEABS/ueabs/-/tree/r2.2-dev/qcd/part_2) <br>[- Summary](https://repository.prace-ri.eu/git/UEABS/ueabs/-/blob/r2.2-dev/qcd/part_2/README.md) | lattice Quantum Chromodynamics Part 2 - QUDA | C++ | yes | yes | yes (CUDA) | -- | Part 2: GPU is using a QUDA kernel for running on NVIDIA GPUs. [Test case A - 96x32x32x32] Small problem size. CG solver. Domain Decomposition, Memory bandwidth, strong scaling, MPI latency. [Test case B - 126x64x64x64] Moderate problem size. CG solver on Wilson Dirac stencil. Bandwidth bounded |
| <br>[- Source](http://jeffersonlab.github.io/qphix/) <br>[- Bench](https://repository.prace-ri.eu/git/UEABS/ueabs/-/tree/r2.2-dev/qcd/part_2) <br>[- Summary](https://repository.prace-ri.eu/git/UEABS/ueabs/-/blob/r2.2-dev/qcd/part_2/README.md) | lattice Quantum Chromodynamics Part 2 - QPHIX | C++ | yes | yes | no | -- | Part 2: Xeon(Phi) is using a QPhiX kernel which is optimize to run on x86, in particular Intel Xeon (Phi). [Test case A - 96x32x32x32] Small problem size. CG solver involving Wilson Dirac stencil. Domain Decomposition, Memory bandwidth, strong scaling, MPI latency. [Test case B - 126x64x64x64] Moderate problem size. CG solver on Wilson Dirac stencil. Bandwidth bounded |
| <br>[- Source](https://repository.prace-ri.eu/ueabs/QCD/1.3/QCD_Source_TestCaseA.tar.gz) <br>[- Bench](https://repository.prace-ri.eu/git/UEABS/ueabs/-/tree/r2.2-dev/qcd/part_cpu) <br>[- Summary](https://repository.prace-ri.eu/git/UEABS/ueabs/-/blob/r2.2-dev/qcd/part_cpu/README.md) | lattice Quantum Chromodynamics - CPU Part - legacy UEABS | C/Fortran | yes | yes/no | No | -- | CPU part based on UEABS QCD CPU part (legacy) benchmark kernels (last update 2017). Based on 5 different Benchmark applications representative for the European Lattice QCD community (see doc for more details). |
# Quantum Espresso <a name="espresso"></a>
QUANTUM ESPRESSO is an integrated suite of computer codes for electronic-structure calculations and materials modeling, based on density-functional theory, plane waves, and pseudopotentials (norm-conserving, ultrasoft, and projector-augmented wave). QUANTUM ESPRESSO stands for opEn Source Package for Research in Electronic Structure, Simulation, and Optimization. It is freely available to researchers around the world under the terms of the GNU General Public License. QUANTUM ESPRESSO builds upon newly restructured electronic-structure codes that have been developed and tested by some of the original authors of novel electronic-structure algorithms and applied in the last twenty years by some of the leading materials modeling groups worldwide. Innovation and efficiency are still its main focus, with special attention paid to massively parallel architectures, and a great effort being devoted to user friendliness. QUANTUM ESPRESSO is evolving towards a distribution of independent and inter-operable codes in the spirit of an open-source project, where researchers active in the field of electronic-structure calculations are encouraged to participate in the project by contributing their own codes or by implementing their own ideas into existing codes.
QUANTUM ESPRESSO is written mostly in Fortran90, and parallelised using MPI and OpenMP.
- Web site: http://www.quantum-espresso.org/
- Code download: http://www.quantum-espresso.org/download/
- Build instructions: http://www.quantum-espresso.org/wp-content/uploads/Doc/user_guide/
- Test Case A: https://repository.prace-ri.eu/ueabs/Quantum_Espresso/QuantumEspresso_TestCaseA.tar.gz
- Test Case B: https://repository.prace-ri.eu/ueabs/Quantum_Espresso/QuantumEspresso_TestCaseB.tar.gz
- Run instructions: https://repository.prace-ri.eu/git/UEABS/ueabs/blob/r1.3/quantum_espresso/QE-guide.txt
# SHOC <a name="shoc"></a>
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.
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).
- Web site: https://github.com/vetter/shoc
- Code download: https://github.com/vetter/shoc/archive/master.zip
- Build instructions: https://repository.prace-ri.eu/git/ueabs/ueabs/blob/r2.1-dev/shoc/README_ACC.md
- Run instructions: https://repository.prace-ri.eu/git/ueabs/ueabs/blob/r2.1-dev/shoc/README_ACC.md
| **General information** | **Scientific field** | **Language** | **MPI** | **OpenMP** | **GPU** | **LoC** | **Code description** |
|------------------|----------------------|--------------|---------|------------|---------------------|---------|-------------------------------------------------------------------------------------------------------------------------------------------------------|
| [- Website](https://geodynamics.org/cig/software/specfem3d_globe/) <br>[- Source](https://github.com/geodynamics/specfem3d_globe.git) <br>[- Bench](https://repository.prace-ri.eu/git/UEABS/ueabs/tree/r2.1-dev/specfem3d) <br>[- Summary](https://repository.prace-ri.eu/git/UEABS/ueabs/blob/r2.1-dev/specfem3d/PRACE_UEABS_Specfem3D_summary.pdf) | Geodynamics | Fortran | yes | yes | Yes (CUDA) | 140000 | The software package SPECFEM3D simulates three-dimensional global and regional seismic wave propagation based upon the spectral-element method (SEM). |
# 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 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.
- Website: https://github.com/maxwelltsai/DeepGalaxy
- Code download: https://github.com/maxwelltsai/DeepGalaxy
- [Prerequisites installation](tensorflow/prerequisites-installation.md)
- [Test Case A](tensorflow/Testcase_A/)
- [Test Case B](tensorflow/Testcase_B/)