Dask-traj

 

For analysis of molecular dynamics (MD) simulations MDTraj is a fast and commonly used analysis. However MDTraj has some restrictions such as (1) the whole trajectory needs to fit into memory, or gathering results becomes inconvenient; (2) the result of the computation also need to fit into memory, and (3) all processes need access to all the memory, preventing out-of-machine parallelisation and HPC scaling.

Dask-traj solves these restrictions by rewriting the MDTraj functions to work with Dask in order to achieve out-of-memory computations. Combined with dask-distributed this allows for out-of-machine parallelisation, essential for HPCs, and results in a (surprising) speed-up even on a single machine.

Source code

The source code for this module, and modules that build on it, is hosted at https://github.com/sroet/dask-traj

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E-CAM article on the EU Research Magazine

 

An article about E-CAM has just been released with the Autumn edition of the EU Research Magazine. The EU research magazine is Europe’s leader in research dissemination.

The piece consists on an interview to Prof. Ignacio Pagonabarraga, E-CAM technical manager, Dr. Sara Bonella, leader of our work-package focused on quantum dynamics and also of the work-package that deals with the interactions with industry; Dr. Donal Mackernan, leader of our dissemination work-package and Dr. Jony Castagna, programmer in E-CAM.

The interview describes E-CAM’s work in

(1) developing software targeted at the needs of both academic and industrial end-users, with applications from drug development to the design of new materials ;

(2) tuning those codes to run on HPC machines, through application co-design and the provision of HPC oriented libraries and services;

(3) training scientists from industry and academia ; and

(4) supporting industrial end-users in their use of simulation and modelling, via workshops and direct discussions with experts in the CECAM community.

Autumns edition of the EU Research Magazine is available online at  http://www.euresearcher.com/14/eu-research-live. Our article can be seen here.

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Issue 14 – September 2020

E-CAM Newsletter of September 2020

 

Get the latest news from E-CAM, sign up for our  newsletter.

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A possible roadmap for the coarse graining and multiscale simulation community

 

A community-driven review with contributions from E-CAM “Unfolding the prospects of computational (bio)materials modeling has just been published in the Journal of Chemical Physics on the history, developments, and challenges facing coarse graining (CG) and multiscale simulation (MS)  and a set of recommendations on how the latter may be addressed. 

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E-CAM Industrial Case Study: Calculations for Applications in Photovoltaic Devices

Dr. David Lopez, Universidad de Córdoba, Spain

Abstract

The need to find easily renewable and environmentally friendly energy sources alternative to the traditional fossil fuels is nowadays a global quest. The solar energy is a promising candidate and organic solar cells (OSCs) have attracted attention. In this collaboration with Merck, E-CAM scientists have used electronic structure calculations to study how a key magnitude – the HOMO-LUMO band gap – changes with respect to the molecular disposition of the donor-acceptor molecule pair.

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CLstunfti: An extendable Python toolbox to compute scattering of electrons with a given kinetic energy in liquids and amorphous solids

 

Description

CLstunfti is an extendable Python toolbox to compute scattering of electrons with a given kinetic energy in liquids and amorphous solids. It uses a continuum trajectory model with differential ionization and scattering cross sections as input to simulate the motion of the electrons through the medium.

Originally, CLstunfti was developed to simulate two experiments: A measurement of the effective attenuation length (EAL) of photoelectrons in liquid water [1] and a measurement of the photoelectron angular distribution (PAD) of photoelectrons in liquid water [2]. These simulations were performed to determine the elastic mean free path (EMFP) and the inelastic mean free path (IMFP) of liquid water [3].

Practical application

The EMFP and IMFP are two central theoretical parameters of every simulation of electron scattering in liquids, but they are not directly accessible experimentally. As CLstunfti can be used to determine the EMFP and IMFP from experimental data, and as it can be easily extended to simulate other problems of particle scattering in liquids, it was decided to make the source code publicly available. For this purpose, within the E-CAM module, the necessary steps were taken to make CLstunfti a useful toolbox for other researchers by providing a documentation, examples, and also extensive inline documentation of the source code.

Source code

CLstunfti is available at https://gitlab.com/axelschild/CLstunfti .

 

References

[1] Suzuki, Nishizawa, Kurahashi, Suzuki, Effective attenuation length of an electron in liquid water between 10 and 600 eV, Phys. Rev. E 90, 010302 (2014)

[2] Thürmer, Seidel, Faubel, Eberhardt, Hemminger, Bradforth, Winter, Photoelectron Angular Distributions from Liquid Water: Effects of Electron Scattering, Phys. Rev. Lett. 111, 173005 (2013)

[3] Schild, Peper, Perry, Rattenbacher, Wörner, Alternative approach for the determination of mean free paths of electron scattering in liquid water based on experimental data, J. Phys. Chem. Lett., 11, 1128−1134 (2020)

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Addressing interactive HTC workloads with HPC characteristics: introduction to E-CAM’s HTC library

 

Abstract

Traditionally high-throughput computing (HTC) workloads are looked down upon in the HPC space, however the scientific use case for extreme-scale resources required by coordinated HTC workflows exists. For such cases where there may be thousands of tasks each requiring peta-scale computing, E-CAM has extended the data-analytics framework Dask with a capable and efficient library to handle such workloads.

 

Introduction

The initial motivation for E-CAM’s High Throughput Library, jobqueue_features library [1], is driven by the ensemble-type calculations that are required in many scientific fields, and in particular in the materials science domain. A concrete example is the study of molecular dynamics with atomistic detail, where timesteps must be used on the order of a femto-second. Many problems in biological chemistry and materials science involve events that only spontaneously occur after a millisecond or longer (for example, biomolecular conformational changes). That means that around 1012 time steps would be needed to see a single millisecond-scale event. This is the problem of “rare events” in theoretical and computational chemistry.

Modern supercomputers are beginning to make it possible to obtain trajectories long enough to observe some of these processes, but to fully characterize a transition with proper statistics, many examples are needed. In such cases the same peta-scale application must be run many thousands of times with varying inputs. For this use case, we were conceptually attracted to the Dask philosophy [2]: Dask is a specification that encodes task schedules with minimal incidental complexity using terms common to all Python projects, namely dicts, tuples, and callables.

However, Dask or it’s extensions do not currently support task-level parallelization (in particular multi-node tasks). We have been able to leverage the Dask extension dask_jobqueue [3] and build upon it’s functionality to include support for MPI-enabled task workloads on HPC systems. The resulting approach, described in the rest of this piece, allows for multi-level parallelization (at the task level via MPI, and at the framework level via Dask) while leveraging all of the pre-existing effort within the Dask framework such as scheduling, resilience, data management and resource scaling.

E-CAM’s HTC library was created in collaboration with a PRACE team in Wrocław, and is the subject of an associated white paper [4]. This effort is under continuous improvement and development. A series of dedicated webinars will happen in the fall of 2020, which will be an opportunity for people to learn how to use Dask and dask_jobqueue (to submit Dask workloads on a resource scheduler like SLURM), and to implement our library jobqueue_features in their codes. Announcement and more information will soon be available at https://www.e-cam2020.eu/calendar/.

 

Methodology

The jobqueue features library [1] is an extension of dask_jobqueue [3] which in turn utilizes the Dask [2] data analytics framework. dask_jobqueue is targeted at deploying Dask on several job queuing systems, such as SLURM or PBS with the use of a Python programming interface. The main enhancements of basic dask_jobqueue functionality is heavily extending the configuration implementation to handle MPI runtimes and different resource specifications. This allows the end-user to conveniently create parallelized tasks without extensive knowledge of the implementation details (e.g., the resource manager or MPI runtime). The library is primarily accessed through a set of Python decorators: on_cluster, task and mpi_task. The on_cluster decorator gets or creates clusters, which in turn submit worker resource allocation requests to the scheduler to execute tasks. The mpi_task decorator derives from task and enhances it with MPI specific settings (e.g. the MPI runtime and related settings).

Fig. 1: Example of decorator usage to parallelize computation

In Fig. 1 we show a minimal, but complete, example which uses the mpi_task and on_cluster decorators for a LAMMPS execution. The configuration, communication and serialization is isolated and hidden from user code.

Any call to my_lammps_job results in the lammps_task function being executed remotely by a lammps_cluster worker allocated by the resource manager with 2 nodes and 12 MPI tasks per node. The code can be executed interactively in a Jupyter notebook. To overlap calculations one would need to return the t1 future rather than the actual result.

 

Findings

The library can effectively handle simultaneous workloads on GPU, KNL and CPU partitions of the JURECA supercomputer [5]. The caveat with respect to the hardware environment is that you need to be able to have a network that supports TCP (usually via IPoIB) or UCX connections between the scheduler and the workers (which process and execute the tasks that are queued).

With respect to the software stack, this is an issue highlighted by the KNL booster of JURECA. On the booster, there is a different micro-architecture and it is required to completely change your software stack to support this. The design of the software stack implementation on JURECA simplifies this but ensuring your tasks are run in the correct software environment is one of the more difficult things to get right in the library. As a result, the configuration of the clusters (which define the template required to submit workers to the appropriate queue of the resource manager) can be quite non-trivial. However, they can be located within a single file which will need to be tuned for the available resources. With respect to the tasks themselves, no tuning is necessarily required.

We see ∼90% throughput efficiency for trivial tasks, if the tasks executed for any reasonable length of time this throughout efficiency would be much higher.

 

Conclusions

The library is flexible, scalable, efficient and adaptive. It is capable of simultaneously utilising CPUs, KNL and GPUs (or any other hardware) and dynamically adjusting its use of these resources based on the resource requirements of the scheduled task workload. The ultimate scalability and hardware capabilities of the solution is dictated by the characteristics of the tasks themselves with respect to these. For example, for the use case described here these would mean the hardware and scalability capabilities of LAMMMPS with a further multiplicative factor coming from the library for the number of tasks running simultaneously. There is, unsurprisingly, room for further improvement and development, in particular related to error handling and limitations related to the Python GIL.

 

References

[1] jobqueue features repository, https://github.com/E-CAM/jobqueue_features

[2] Dask documentation, https://dask.org.

[3] Dask-Jobqueue documentation, https://jobqueue.dask.org/.

[4] A. O. Cais, D. Swenson, M. Uchronski and A. Wlodarczyk. (2019, Augoust 14). “Task Scheduling Library for Optimising Time-Scale Molecular Dynamics Simulations,” Zenodo. http://doi.org/10.5281/zenodo.3527643

[5] Krause, D. and Thörnig, P.: JURECA: Modular supercomputer at Jülich Supercomputing Centre, http://juser.fz-juelich.de/record/850758  (2016)

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Minimal distance segment to segment with Karush-Kuhn-Tucker conditions

 

Description

The module minDist2segments_KKT returns the minimal distance between two line segments. It uses the Karush-Kuhn-Tucker conditions (KKT) for the minimization under constraints.

Practical application

We use the present module to avoid topology violations in an entangled polymer system. To preserve the topology in a system of entangled polymers we need to determine the minimal distance between two bonds. Once done we can apply either a soft or hard core potential to avoid the crossing of two bonds. Here, we propose to determine the minimal distance between two segments with the help of the Karush-Kuhn-Tucker conditions.

This module is a part of an E-CAM pilot project at the ENS Lyon, focused on the implementation of contact joint to resolve excluded volume constraints

Background information

A detailed derivation of the minimal distance between two segments using the Karush-Kuhn-Tucker conditions is available at  https://gitlab.e-cam2020.eu:10443/carrivain/mindist2segments_kkt/-/blob/master/minDist2segments_KKT.pdf

This module is used by other ongoing work, such as module velocities_resolve_EV, that resolves the excluded volume constraint  with a velocity formulation.

Source code

The source code and more information can be found at minDist2segments_KKT GitLab repository.

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Accelerating the design and discovery of materials with tailored properties using first principles high-throughput calculations and automated generation of Wannier functions

 

A successful collaboration between the EU H2020 E-CAM and MaX Centres of Excellence, and the Swiss NCCR MARVEL

Abstract

In a recent paper[1], researchers from the Centres of Excellence E-CAM[2] and MaX[3], and the centre for Computational Design and Discovery of Novel Materials NCCR MARVEL[4], have proposed a new procedure for automatically generating Maximally-Localised Wannier functions (MLWFs) for high-throughput frameworks. The methodology and associated software  can be used for hitherto difficult cases of entangled bands, and allows the  electronic properties of a wide variety of materials to be obtained starting only from the specification of the initial crystal structure, including insulators, semiconductors and metals. Industrial applications that this work will facilitate include the development of novel superconductors, multiferroics, topological insulators, as well as more traditional electronic applications.

Graphical representation of all data and calculations run in the project and their interconnections (provenance), as tracked automatically by AiiDA in the form of a directed acyclic graph (image credits: G. Pizzi)

Challenge/context

Predicting the properties of complex materials generally entails the use of methods that facilitate coarse grained perspectives more suitable for large scale modelling, and  ultimately device design and manufacture. When a quantum level of description of a modular-like system  is required, this can often be facilitated by expressing the Hamiltonian in terms of a localised, real-space basis set, enabling it to be partitioned without ambiguity into sub-matrices that correspond to the individual subsystems. Maximally-localised Wannier functions  (MLWFs) are particularly suitable in this context. However, until now generating MLWFs has been difficult to exploit  in high-throughput design of materials, without  the specification by users of a set of initial guesses for the MLWFs,  typically trial functions localised in real space, based on their experience and chemical intuition. 

Solution

E-CAM[2] scientist Valerio Vitale and co-authors from the partner H2020 Centre of Excellence  MAX[3] and the Swiss based NCCR MARVEL [4] in a recent article[1] look afresh at this problem in the context of an algorithm by Damle et al[5], known as the selected columns of the density matrix (SCDM) method, as a method to provide automatically initial guesses for the MLWF search, to compute a set of localized orbitals associated with the Kohn–Sham subspace for insulating systems. This has shown great promise in avoiding the need for user intervention in obtaining MLWFs and is robust, being based on standard linear-algebra routines rather than on iterative minimisation. In particular, Vitale et al. developed a fully-automated protocol based on the SCDM algorithm in which the three remaining free parameters (two from the SCDM method, plus the choice of the target dimensionality for the disentangled subspace) are determined automatically, making it thus parameter-free even in the case of entangled bands. The work systematically compares the accuracy and ease of use of standard methods to generate localised basis sets  as (a) MLWFs; (b)  MLWFs combined with SCDM’s and (c) using solely SCDM’s;  and applies this multifaceted perspective to hundreds of materials including insulators, semiconductors and metals.

Comparison between Wannier-interpolated valence bands (red lines) and the full direct-DFT band structure (black lines), for 150 different materials. The direct and interpolated band structures are essentially indistinguishable (image credits: G. Pizzi)

Benefit

This is significant because it greatly expands the scope of materials for which MLWFs can be generated in high throughput studies and has the potential to accelerate the design and discovery of materials with tailored properties using first-principles high-throughput (HT) calculations, and facilitate advanced industrial applications. Industrial applications that this work will facilitate include the development of novel superconductors, multiferroics, topological insulators, as well as more traditional electronic applications.

Background information

This module is a collaboration between the E-CAM and MaX HPC centres of excellence, and the NCCR MARVEL

In SCDM Wannier Functions, E-CAM has implemented the SCDM algorithm in the pw2wannier90 interface code between the Quantum ESPRESSO software and the Wannier90 code. This was done in the context of an E-CAM pilot project at the University of Cambridge. Researchers have then used this implementation as the basis for a complete computational workflow for obtaining MLWFs and electronic properties based on Wannier interpolation of the Brillouin zone, starting only from the specification of the initial crystal structure. The workflow was implemented within the AiiDA materials informatics platform (from the NCCR MARVEL and the MaX CoE) , and used to perform a HT study on a dataset of 200 materials.

Source Code

See the Materials Cloud Archive entry. A downloadable virtual machine is provided that allows to reproduce the results of the associated paper and also to run new calculations for different materials, including all first-principles and atomistic simulations and the computational workflows.

Bibliography

[1] Automated high-throughput Wannierisation, Valerio Vitale, Giovanni Pizzi, Antimo Marrazzo, Jonathan R. Yates, Nicola Marzari and Arash A. Mostofi, Nature Computational Materials (2020)6:66 ; https://doi.org/10.1038/s41524-020-0312-y

[2] https://www.e-cam2020.eu/

[3] http://www.max-centre.eu/

[4] https://nccr-marvel.ch/

[5] Compressed Representation of Kohn−Sham Orbitals via Selected Columns of the Density Matrix , Anil Damle, Lin Lin,  and Lexing Ying, J. Chem. Theory Comput. 2015, 11, 1463−1469 https://pubs.acs.org/doi/10.1021/ct500985f

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E-CAM is helping to organise a session on HPC Carpentry at CarpentryCon @ Home

HPC Carpentry – a way forward

E-CAM is helping to organise a session on HPC Carpentry during CarpentryCon @ Home that aims to foster a scalable HPC training model. Join our Software Manager Alan O’Cais on Monday 20 July, at 9 am and at 5pm CEST. More details about the session at https://2020.carpentrycon.org/schedule/#session-33. View the session’s Etherpad and sign up at https://pad.carpentries.org/cchome-hpc-carpentry

#CarpentryConHome

What:
Session “HPC Carpentry – a way forward” at the CarpentryCon @ Home

When:
Session 1: July 20, 2020 at 07h00 UTC (9h00 CEST)
Session 2: July 20, 2020 at 15h00 UTC (17h00 CEST)

Presenters: 

  • Alan O’Cais, E-CAM Software Manager, Jülich Supercomputing Centre (JSC), Germany
  • Peter Steinbach, Helmholtz AI Consultants Team Lead for Matter Research, Helmholtz-Zentrum Dresden-Rossendorf, Germany

More information about the session and sign up:
https://pad.carpentries.org/cchome-hpc-carpentry

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