Intel agreed to sponsor a 3-day Optane hackathon at Durham. It’s aim was to learn how Intel® Optane™ memory and intelligent system acceleration work to deliver higher performance. This event provided the DiRAC community an opportunity to explore the potential for Intel’s new Optane memory in supporting their science.

The Hackathon was open to all DiRAC HPC users. There were 5 teams of between 2 and 3 people each. Over the three days, several major DiRAC science codes were ported to make efficient use of this and standard memory and the teams who attend gained the skills to assist other DiRAC researchers to port additional codes in the future.

Teams

GRChombo

lead by Dr Kacper Kornet from the University of Cambridge

GRChombo is a new open-source code for numerical general relativity
simulations. It is developed and maintained by a collaboration of numerical
relativists with a wide range of research interests, from early universe
cosmology to astrophysics and mathematical general relativity, and has been used in many papers since its first release in 2015.
GRChombo is written entirely in C++14, using hybrid MPI/OpenMP parallelism and vector intrinsics to achieve good performance on the latest architectures.
Furthermore, it makes use of the Chombo library for adaptive mesh refinement to allow automatic increasing and decreasing of the grid resolution in regions of arbitrary shape and topology.

TROVE

lead by Sergey Yurchenko from University College London

TROVE is a variational method with an associated Fortran 2003 program to
construct and solve the ro-vibrational Schrödinger equation for a general
polyatomic molecule of arbitrary structure. The energies and eigenfunctions obtained via a variational approach can be used to model
absorption/emission intensities (molecular line lists) for a given temperature as well as to compute temperature independent line strengths and Einstein coefficients. A typical TROVE pipe line requires a construction and diagonalisation of about 200-300 double-real, dense symmetric matrices of sizes varying from 10,000×10,000 to 500,000×500,000. For the line list production it is important to compute almost all eigenvalues (or at least 50-80%) together with eigenvectors. The diagonalisations are efficiently done using external libraries, for example the DSYEV-family for smaller matrices (N< 200,000) or the PDSYEV-family for large matrices (N > 200,000). The TROVE program is highly optimized for the mass production of line lists for medium-size molecules applicable for high temperatures. TROVE has been extensively used to produce line lists for a number of key polyatomic molecules, including NH3 , CH4 , H2CO, H2CS, PH3 , SbH3 , HOOH, HSOH, CH3Cl, BiH3 , SO3 , SiH4 , CH3 , C2H4 , CH3F (about 80 papers in peer-reviewed journals). TROVE is a well-recognized method of the modern theoretical spectroscopy with the TROVE paper being highly cited.

Swift

lead by Prof Richard Bower from Durham University

Is a fine-grained task-parallel approach to cosmological simulations.
The code solves gravity and hydro equations, with additional sub-grid physics sources terms that encapsulate star formation, black holes, chemical enrichment, cooling etc.

The novelty of the code lies in its approach to parallelising this challenging
problem. It delivers a factor 10 speed improvement over the current state of
the art code, gadget.

The code is primarily used by the EAGLE simualtions team and at JPL for
planetary science. It is a new code and we expect widespread adoption as
the results are published.

MODEL_LSS

lead by Arjen Tamerus from the University of Cambridge

MODAL_LSS is an astrophysics code used for estimation of the bispectrum of simulation data or observational data, using the MODAL methodology. It is currently in active development and not (yet) open source.

Ramses

lead by Adrianne Slye from Oxford University

This team was using the open source code RAMSES
https://bitbucket.org/rteyssie/ramses
written by Romain Teyssier.
While mainly designed to study structure formation in an expanding Universe, the code has been applied to a range of problems dealing with self-gravitating MHD fluids (turbulence, planet formation) and is also equipped with a module to solve the radiative transfer equations. There also exists versions of the code with a modified Poisson solver to study alternatives to Einstein’s gravity.
The RAMSES community consists of about ~150 users around the world, with ~15 in the UK.

SLH (Seven-League Hydro) Code

lead by Phillipp Edelmann from Newcastle University

SLH is a finite-volume hydrodynamics code, mainly intended for use in stellar astrophysics. Its most distinguishing feature is the fully implicit time
discretization, which allows it to run efficiently for problems at low Mach
numbers, such as the deep interiors of stars. The resulting large non-linear
system is very challenging to solve and needs Newton-Krylov methods to be
efficient.

It is designed with flexibility in mind, supporting arbitrary structured curvilinear grids, different equations of state and a selection of other physics modules (nuclear reactions, neutrino losses, …).

More information about the code at: https://slh-code.org/features.html
The code is currently not open source, but our group is open to pass the code on in collaborations.

JBCA

lead by Rohini Joshi of the University of Manchester

They were using WSCLEAN (https://sourceforge.net/p/wsclean/wiki/Home/), a C++ code designed to perform Fast Fourier Transforms (FFTs) of large
datasets produced by connected arrays of radio telescopes (interferometers), and then perform iterative beam-deconvolution (following the CLEAN algorithm; Hogbom, 1974) to produce sensitive, high-resolution, wide-field images for astrophysical interpretation. eMERLIN, the UK National Facility for high-resolution radio astronomy, has proved a productive instrument for smaller (few nights, yielding ~100GB datasets) PI-led studies, imaging narrow fields of view (4 mega-pixels), however the flagship “legacy” programs, which address key STFC science goals, entail several thousand hours of on-sky data aquisition and have produced datasets of the order >>1-10TB over fields of view orders of magnitude larger (~2 giga-pixels). Producing images from these legacy programmes has thus presented a unique “big data” challenge.

In recent years an experienced corps of radio astronomers have pushed the
boundaries of radio imaging techniques to deliver eMERLIN legacy science
using machines with ~512GB–1.5TB RAM, however to circumvent bottlenecks in our existing infrastructure and fully deliver on the potential of these datasets in the coming years we envisage even higher memory
requirements.

These same challenges have been encountered by other modern radio
telescopes (e.g. LOFAR, ASKAP, MeerKAT, and will be critical to science
delivery from the Square Kilometer Array in the next decate). WSCLEAN is a modern imaging package designed to meet the needs of current and
forthcoming facilities. It is rapidly becoming the standard wide-field imaging package, replacing older packages like AIPS and CASA. The source code is publicly available.

We have build a Docker file containing WSCLEAN and all required
dependencies: https://hub.docker.com/r/lofaruser/imaging-pipeline/ contains WSClean (version 2.6)