We are pleased to announce the fourteenth release (code name "Payne-Gaposchkin") of the Einstein Toolkit, an open, community developed software infrastructure for relativistic astrophysics. The highlights of this release are:
* The Llama multi-patch infrastructure, which has been publicly available for some time already, is now part of the Einstein Toolkit. An example in the Einstein Toolkit Gallery shows how to run Llama with a simple wave equation.
* There is a new example in the Einstein Toolkit Gallery demonstrating how to evolve a binary black hole system and reproduce a waveform consistent with GW150914, the first gravitational wave event detected by LIGO.
In addition, bug fixes accumulated since the previous release in May 2016 have been included.
The Einstein Toolkit is a collection of software components and tools for simulating and analyzing general relativistic astrophysical systems that builds on numerous software efforts in the numerical relativity community including CactusEinstein, the Carpet AMR infrastructure and the relativistic magneto-hydrodynamics code GRHydro. For parts of the toolkit, the Cactus Framework is used as the underlying computational infrastructure providing large-scale parallelization, general computational components, and a model for collaborative, portable code development. The toolkit includes modules to build complete codes for simulating black hole spacetimes as well as systems governed by relativistic magneto-hydrodynamics.
The Einstein Toolkit uses a distributed software model and its different modules are developed, distributed, and supported either by the core team of Einstein Toolkit Maintainers, or by individual groups. Where modules are provided by external groups, the Einstein Toolkit Maintainers provide quality control for modules for inclusion in the toolkit and help coordinate support. The Einstein Toolkit Maintainers currently involve postdocs and faculty from six different institutions, and host weekly meetings that are open for anyone to join in.
Guiding principles for the design and implementation of the toolkit include: open, community-driven software development; well thought out and stable interfaces; separation of physics software from computational science infrastructure; provision of complete working production code; training and education for a new generation of researchers.
For more information about using or contributing to the Einstein Toolkit, or to join the Einstein Toolkit Consortium, please visit our web pages at http://einsteintoolkit.org.
The Einstein Toolkit is primarily supported by NSF 1550551/1550461/1550436/1550514 (Einstein Toolkit Community Integration and Data Exploration).
The Einstein Toolkit contains about 200 regression test cases. On a large portion of the tested machines, almost all of these test suites pass, using both MPI and OpenMP parallelization.
=== Larger changes since last release ===
* Cactus Flesh - Update linker flags to allow linking using newer versions of 'ar' - Add '.' to perl @INC directory to allow build using newer versions of Perl - Provide Cactus version as CACTUS capability to thorns - Allow aligning interior (instead of just origin) of grid functions - Output build timing information in VERBOSE mode * Formaline - Attempt to use fast methods where possible (hardlinks are available) - Only update files in thorns that changed -> should make it noticeably faster on most systems * Simfactory - use of environment variables CACTUS_NUM_PROCS and CACTUS_NUM_THREADS in runscript of all machines. Update your private entries accordingly. - Updates to several machines (too many to list) * Multipole - Add possibility to have separate output directory * ExternalLibraries - Version updates if built from scratch: OpenMPI, OpenBLAS
* New thorns or tools - Llama: - multi-patch infrastructure for the Einstein Toolkit - has been publicly available for some time already, but is now part of the Einstein Toolkit
=== Upcoming changes for the next releases
The Tmunu parameter support_old_CalcTmunu_mechanism, will be removed after this release. If you rely on this, your code is probably unnecessarily slow. Let us know if the removal would create a problem for you.
Most of the Fortran code in GRHydro was already replaced by more modern, and much easier to maintain C++ code. Up to now, both versions are compiled, and can be chosen at start-time. For a few releases the C++ versions are the default. After this release, the Fortran versions will be removed. Let us know if the removal would create a problem for you.
=== How to upgrade from Brahe (ET_2016_05) ===
To upgrade from the previous release, use GetComponents with the new thornlist to check out the new version.
See the [http://einsteintoolkit.org/download Download] page on the Einstein Toolkit website for download instructions.
=== Machine notes ===
Supported (tested) machines include:
- Default Debian, Ubuntu, Fedora, CentOS, and MacOS installations - Bethe - Bluewaters - Comet (#) - Draco (#) - Edison (#) - Galileo - Gulob (#) - Gordon - Hydra (#) - Minerva - Queenbee 2 - Shelob - SuperMic (#) - Supermike II - Stampede (CPU) (*#) - Wheeler - Zwicky
* Stampede: defs.local.ini needs to have sourcebasedir = $WORK and basedir = $SCRATCH/simulations configured for this machine. You need to determine $WORK and $SCRATCH by logging in to the machine. A new configuration for KNL nodes is being worked on, but not yet included in the release (but compilation works and tests mostly pass). # ML_ADMConstraints: There seems to be a problem regarding calculating the constraints from the BSSN quantities. The exact problem is not known yet, but indications point towards a compiler problem (Intel in all cases). In all cases the metric data (evolved with ML_BSSN) is identical to the test data (to round-off error) and only the ML_ADMConstraints data differ significantly from the test data. If output statements of relevant quantities are inserted into the code, the differences disappear One consistent symptom is failing of the Dissipation and RotatingSymmetry testsuites (as they include output of the constraints using ML_ADMConstraints). Investigations on this matter will continue, new findings, together with workarounds/solutions will be announced on the users mailing list, and will be back-ported into the release branch. Updates can be found in ticket #1995: https://trac.einsteintoolkit.org/ticket/1995
All repositories participating in this release carry a branch ET_2016_11 marking this release. These release branches will be updated if severe errors are found.
The "Payne-Gaposchkin" Release Team on behalf of the Einstein Toolkit Consortium (2016-12-16)
Steven R. Brandt Peter Diener Roland Haas Ian Hinder Frank Löffler Erik Schnetter Barry Wardell
December 16th, 2016