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This is a feature release, i.e., new functionality is added to Espresso. An additional focus of this release is quality assurance and modernization. The testing of Espresso’s functionality has been extended considerably. Also, sample and tutorial scripts are now automatically tested. Moreover, a large effort was put into modernizing the C++ simulation core. Work has been done, e.g., on particle sorting, the MPI communication infrastructure, and the lattice-Boltzmann implementations. Electrostatic and magnetostatic methods now have a clear and common interface. These changes will facilitate future extensions of Espresso and make the code more understandable to new developers.
We recommend that this release be used for all production simulations. No further bug fix releases will be provided for the 4.0 line, and not all fixes are present in Espresso 4.0.2.
Please carefully read the detailed list of changes below before using this release. The numbers in brackets refer to ticket numbers on https://github.com/espressomd/espresso.
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Changed requirements
- Python 2 support has been dropped. Espresso now requires Python 3. For additional information, please see https://github.com/espressomd/espresso/wiki/python_2_3_transition.
- Espresso now needs a C++14-capable compiler, such as GCC 4.9 and later or Clang 4 and later.
- It is discouraged to use Espresso with Boost versions below 1.67.
Added functionality and documentation
- The distance between a shape (such as sphere) and a position can now be queried via
shape.calc_distance()
. - The lattice nodes of a lattice-Boltzmann fluid can now be iterated using
LBFluid.nodes()
. - A tutorial on magnetic fluids has been added.
- The stress created by the dissipative particle dynamics interaction (DPD) can now be obtained via the
DPDStress
observable. - The stress of a lattice-Boltzmann fluid can now be obtained via the
LBFluidStress
observable. - A torus shape has been added.
- Two new accumulators for observables have been added:
MeanVarianceCalculator
andTimeSeries
. - An
ElectricPlaneWave
constraint was added. - Experimental support for AMD GPUs via HIP. The future of this feature is unclear. Please do not base hardware buying decisions on its presence.
- Visualization of slit pores in the OpenGL visualizer.
- A Weeks-Chandler-Anderson short-range potential has been added (
WCA
). - The external force density applied to a lattice-Boltzmann fluid can now be changed during the simulation.
- Sanity checks for Mach limits and unequal MD and lattice-Boltzmann time steps have been added.
- The
system.cell_system.tune_skin()
method now has a keyword argumentadjust_max_skin
. If set toTrue
, the maximum skin to be tested will be reduced such that it is compatible with the local box size. - For the CPU lattice-Boltzmann implementation, the limit on the Verlet list skin (<0.5 agrid) has been lifted.
- A new observable
CosPersistenceAngles
has been added for the bond angles of a polymer (needed, e.g., for determining the persistence length).
Feature configuration at compile time
- The number of features which need to be defined at compile time in myconfig.hpp has been reduced. Features without performance impact are now always present. These are:
PARTIAL_PERIODIC
LB
LB_GPU
(for builds with CUDA)IMMERSED_BOUNDARY
BOND_ANGLE
- For most compilers, it is checked that only known features are declared in myconfig.hpp.
- The default feature configuration applied when no myconfig.hpp is present has been extended significantly. In particular, all tutorials can now be run with the default feature configuration. For production simulations, it is still recommended to use a custom myconfig.hpp containing only necessary features. This is true in particular, if particle rotation is not needed.
- Features that do not have automated tests now require
EXPERIMENTAL_FEATURES
to be defined in myconfig.hpp.
Interface changes
- Several parts of Espresso now use a method-specific seed for random number generation. For the following individual methods a random number seed has to be passed using the keyword argument
seed
: - Changes in the lattice-Boltzmann (LB) interface:
- By default the LB fluid is not thermalized. A temperature can be set using the
LBFluid
‘s keyword parameterkT
. IfkT > 0
, an additionalseed
keyword parameter has to be provided. - The LB thermostat gets its temperature from the LB fluid.
- The frictional coupling coefficient
gamma
is now a keyword parameter of the LB thermostat.
- By default the LB fluid is not thermalized. A temperature can be set using the
- For more detailed information on how to set up a LB fluid and thermostat, please see http://espressomd.org/html/doc4.1.0/lb.html.
- The method for polymer creation has been replaced. Now
espressomd.polymer.positions()
can be used to obtain particle positions for one or more polymer chains. Based on these positions, polymers can be created. For an example please see http://espressomd.org/html/doc4.1.0/particles.html#setting-up-polymer-chains.
Changed and removed functionality
- The
remove_total_momentum()
method for lattice-Boltzmann fluids has been removed. The overall velocity of a fluid can be changed using thelb_fluid.nodes()
iterator. - The
CATALYTIC_REACTIONS
feature has been removed. - The method for creating a polymer has been replaced.
espressomd.polymer.positions()
can now be used to obtain particle positions for one or more polymer chains. - Checkpointing has been added for the electrokinetics method.
- The global random number seed has been partly replaced by method-specific ones. These are specified when activating the relevant feature such as the Langevin, DPD and lattice-Boltzmann thermostats via a
seed
keyword argument. - The random number generator has been switched to Philox for most algorithms requiring random numbers.
- Limitations on the exclusion radius have been relaxed in the reaction ensemble method.
- A new observable
CosPersistenceAngles
has been added for the bond angles of a polymer (needed, e.g., for determining the persistence length). - ELC has been disabled for non-neutral systems with constant potential.
- The calculation of the linear particle momentum included the forces of the last time step. The function
system.analysis.linear_momentum()
now returns the sum of the product of mass and velocity of all particles, if no lattice-Boltzmann fluid is coupled.
Espresso 48 Inch Tv Console
Performance enhancements
Espresso Shots For Sale
- Speedup in the short-range force calculation in situations where the short-range cutoff varies strongly for different pairs of particles, e.g., in a bidisperse fluid.
- Speedup in particle resorting triggered when particles have moved by more than a skin.
- Significantly faster back-transfer of particle forces from GPU-based methods such as the GPU implementations of lattice-Boltzmann and P3M.
Bug fixes
- Lattice-Boltzmann boundaries, constraints and auto_update_accumulators are now included in checkpointing. (#2915)
- Collision detection is now checkpointed. (#2342)
- The rhomboid shape was fixed. (#2756)
- Deadlocks on certain GPUs have been resolved for the dipolar Barnes-Hut method. (#2719)
- The visualization of dihedrals has been fixed. (#2677)
- The
ENGINE
implementation for CPU LB has been fixed. (#3025) - The external force density in lattice-Boltzmann fluids is no longer ignored in the first integration step after setting the force density. (#3144)
- The positions of virtual sites and the charges of ICC particles are now updated before observable calculation. (#3128)
- The forces and torques for the Gay-Berne potential have been corrected. (#3091)
- Remove undocumented behaviour in the case of using a cylindrical sampling area in the reaction ensemble, constant pH ensemble, Wang-Landau ensemble, Widom-Insertion method. (#3174)
- The ELC tuning error calculation has been rearranged to produce correct results for higher accuracies. (#3123)
New tutorials
- Tutorials for simulating ferrofluids and for using the constant-pH method have been added.
Under the hood changes
- Automated testing has been enhanced. It now also includes samples and tutorials. The overall test coverage for the simulation core has increased by ~12% since Espresso 4.0.2.
- The CPU LB and LB-particle coupling have been refactored.
- Particle resorting has been simplified and sped-up.
- The MPI callback mechanism has been simplified. Furthermore, reduction operations such as summing values from all MPI ranks can now be performed.
- Nearly all manual memory management and C-style arrays have been removed.
- The rotation-related code has been simplified.
- Long-range electrostatic and magnetostatic methods now have a common interface.
- The kernels for short-range and bonded interactions have been simplified.
- The CMake build system has been refactored and dependencies between different parts of the code have been made clear.
- Python code formatting: the autopep8 version now matches the one in Ubuntu 18.04 (autopep8 v1.3.4 with pycodestyle v2.3.1).
Today’s topics: Understand the thinking patterns of your counterpart to get your innovation message across; Crossing the internal chasm in corporate innovation; Product managers for the digital world.
Please click on headline to start reading.
What kind of thinker are you?
Wattagio 1 0 – manage your macbook battery health program. Time to read: 5 minutes
Innovators, in particular when it comes to radical or even disruptive innovations, often face the problem that communication with their colleagues is tedious. “They don’t get it”, we often hear innovators say.
A recent HBR article provided a useful insight. It found that there is a small number of thinking styles. The authors segmented by a 2×4-matrix. On the one axis, they found that people think more in terms of the “Big Picture” or the “Details”. On the other axis, they found that people are thinking in “Ideas”, “Action”, “Process” or “Relationships”. All in all, there are 8 thinking styles, e.g. the “Explorer”, the “Energizer” or the “Coach”.
Our take: Communication theory says that the sender is responsible for ensuring that the recipient gets the message. Tuning in into the thinking style of the recipient and tuning the lines of argumentation and communication greatly increases the chances that your innovation messages are understood.
Link to the original article: HERE
Crossing the internal chasm in corporate innovation
Time to read: 8 minutes
In their hunt for radical or disruptive innovations, many large companies have set up Corporate Innovation Centers, Accelerators, Digital Labs, etc. At the same time, they are pursuing incremental / sustaining innovations in their core business and innovations that target adjacencies to their existing markets / business models and technologies.
McKinsey last year introduced the three-horizon model for structuring. However, as innovation.support’s Frank Mattes pointed out in a recent article, this model is not good enough. In reality, these are not three separate horizons but an interlinked continuum of three separate innovation playing fields, each of them with a distinct innovation logic. And actually, the latter of these is the hardest one since there are innovations – because this is the nexus, where three different innovation logics have to be integrated into one “dual innovation management system”.
Link to the original article: HERE
Product managers for the digital world
Time to read: 8 minutes
Historically, Product Managers have been the drivers of incremental innovation. Now, with Digital knocking on the company’s wall (think e.g. Smart Products), the scope and the role of the Product Manager is fundamentally changing.
McKinsey has recently published an article which zeroes in on the issue. The article sees four main drivers reshaping Product Management: (a) Increasing role of data; (b) Products are built differently; (c) Products and their ecosystems are becoming more complex; (d) Changes in the ‘execution pod’. It also illustrates the lines along which Product Management could be developed.
Our take: Developing a pivotal role for Horizon-1-innovation (with consequences for Horizon-2-innovation) will require a development of the innovation management system as well. From the six factors that make up a superior early-phase innovation management system (see our ENGAGE model HERE), only the software platform may remain constant. Provided it presents insights from Smart Products etc. automatically (Note: If you are interested in this aspect: Have a look at this MIT article)
Espresso 4 Cube Organizer
Link to the original article: HERE