PRACE/E-CAM successful collaboration produces task scheduling library for optimising time-scale molecular dynamics simulations

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Challenge

E-CAM is interested in the challenge of bridging up timescales. To study molecular dynamics with atomistic detail, timesteps must be used on the order of a femto-second. Many problems in biological chemistry, materials science, and other ends involve events that only spontaneously occur after a millisecond or longer (for example, biomolecular conformational changes, or nucleation processes). 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. And in order to obtain many examples, the same application must be run thousands of times with varying inputs. To manage this kind of computation, a task scheduling library is needed

Solution and benefits

The development of a python library, in collaboration with PRACE. This library builds on top of the scalable analytics framework Dask and enables it to resiliently manage multi-node and multiarchitecture environments. This offers exciting possibilities in the areas of interactive supercomputing and burst supercomputing. A white paper focused on the library was written in collaboration with PRACE and is available here.

The main elements of the mentioned scheduling library are: task de definition, a task scheduling (handled in Python) and task execution (facilitated by the MPI layer). While traditionally an HTC workload is looked down upon in the HPC space, the scientific use case for extreme-scale resources exists and algorithms that require a coordinated approach make efficient libraries that implement this approach increasingly important in the HPC space. The 5 Peta op booster technology of JURECA is an interesting concept with respect to this approach since the offloading approach of heavy computation marries perfectly to the concept outlined here.

Reference

Alan O’Cais, David Swenson, Mariusz Uchronski, & Adam Wlodarczyk. (2019, August 14). Task Scheduling Library for Optimising Time-Scale Molecular Dynamics Simulations. Zenodo. http://doi.org/10.5281/zenodo.3527643

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A Conversation on The Fourth Industrial Revolution: Opportunities & Trends for Particle Based Simulation

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Abstract

In the margins of a recent multiscale simulation workshop a discussion began between a prominent  pharmaceutical industry scientist, and E-CAM and EMMC regarding the unfolding Fourth Industrial Revolution and the role of particle based simulation and statistical methods there.  The impact of simulation  is predicted to become very significant.  This discussion is intended to create awareness of the general public, of how industry 4.0 is initiating in companies, and  how academic research will support that transformation.

Authors: Prof. Pietro Asinari (EMMC and Politecnico di Torino, denoted below as PA) and Dr. Donal MacKernan (E-CAM and University College Dublin, denoted below as  DM) , and a prominent  pharmaceutical industry scientist (name withheld at author’s request as  the view expressed is a personal one, denoted below as  IS)

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Upcoming event: Extended Software Development Workshop in Mesoscopic simulation models and HPC

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E-CAM partners at Aalto University (CECAM Finish Node) in collaboration with the HPC training experts from the CSC Supercomputing Centre, are organizing a joint Extended Software Development Workshop from 15-19 October 2019 , aimed at people interested in particle based methods, such as the Discrete Element and Lattice Boltzmann Methods, and on their massive parallelization using GPU architectures. The workshop will mix three different ingredients: (1) workshop on state-of-the-art challenges in computational science and software, (2) CSC -run school, and (3) coding sessions with the aid of CSC facilities and expertise.

How to Apply

Follow the instruction at the CECAM website for the event: https://www.cecam.org/workshop1752/

Organizers

  • Mikko Alava
    Aalto University, Finland
  • Brian Tighe
    TU Delft, The Netherlands
  • Jan Astrom
    CSC It center for science, Finland
  • Antti Puisto
    Aalto University, Finland

Location

CECAM-FI Node, Aalto University, Finland

Dates

October 15 – 19, 2019

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Extended Software Development Workshop: Mesoscopic simulation models and High-Performance Computing

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If you are interested in attending this event, please visit the CECAM website here.

Workshop Description

In Discrete Element Methods the equation of motion of large number of particles is numerically integrated to obtain the trajectory of each particle [1]. The collective movement of the particles very often provides the system with unpredictable complex dynamics inaccessible via any mean field approach. Such phenomenology is present for instance in a seemingly simple systems such as the hopper/silo, where intermittent flow accompanied with random clogging occurs [2]. With the development of computing power alongside that of the numerical algorithms it has become possible to simulate such scenarios involving the trajectories of millions of spherical particles for a limited simulation time. Incorporating more complex particle shapes [3] or the influence of the interstitial medium [4] rapidly decrease the accessible range of the number of particles.

Another class of computer simulations having a huge popularity among the science and engineering community is the Computational Fluid Dynamics (CFD). A tractable method for performing such simulations is the family of Lattice Boltzmann Methods (LBMs) [5]. There, instead of directly solving the strongly non-linear Navier-Stokes equations, the discrete Boltzmann equation is solved to simulate the flow of Newtonian or non-Newtonian fluids with the appropriate collision models [6,7]. The method resembles a lot the DEMs as it simulates the the streaming and collision processes across a limited number of intrinsic particles, which evince viscous flow applicable across the greater mass.

As both of the methods have gained popularity in solving engineering problems, and scientists have become more aware of finite size effects, the size and time requirements to simulate practically relevant systems using these methods have escaped beyond the capabilities of even the most modern CPUs [8,9]. Massive parallelization is thus becoming a necessity. This is naturally offered by graphics processing units (GPUs) making them an attractive alternative for running these simulations, which consist of a large number of relatively simple mathematical operations readily implemented in a GPU [8,9].

 

References

[1] P.A. Cundall and O.D.L. Strack, Geotechnique 29, 47–65 (1979).
[2] H. G. Sheldon and D. J. Durian, Granular Matter 6, 579-585 (2010).
[3] A. Khazeni, Z. Mansourpour Powder Tech. 332, 265-278 (2018).
[4] J. Koivisto, M. Korhonen, M. J. Alava, C. P. Ortiz, D. J. Durian, A. Puisto, Soft Matter 13 7657-7664 (2017).
[5] S. Succi,The lattice Boltzmann equation: for fluid dynamics and beyond. Oxford university press, (2001).
[6] L. S. Luo, W. Liao, X. Chen, Y. Peng, W. Zhang, Phys. Rev. E, 83, 056710 (2011).
[7] S. Gabbanelli, G.Drazer, J. Koplik, Phys. Rev. E, 72, 046312 (2005).
[8] N Govender, R. K. Rajamani, S. Kok, D. N. Wilke, Minerals Engin. 79, 152-168 (2015).
[9] P.R. Rinaldi, E. A. Dari, M. J. Vénere, A. Clausse, Simulation Modelling Practice and Theory, 25, 163-171 (2012).

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Extended Software Development Workshop: Intelligent high throughput computing for scientific applications

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If you are interested in attending this event, please visit the CECAM website here.

Workshop Description

High throughput computing (HTC) is a computing paradigm focused on the execution of many loosely coupled tasks. It is a useful and general approach to parallelizing (nearly) embarrassingly parallel problems. Distributed computing middleware, such as Celery [1] or COMP Superscalar (COMPSs) [2], can include tools to facilitate HTC, although there may be challenges extending such approaches to the exascale.

Across scientific fields, HTC is becoming a necessary approach in order to fully utilize next-generation computer hardware. As an example, consider molecular dynamics: Excellent work over the years has developed software that can simulate a single trajectory very efficiently using massive parallelization [3]. Unfortunately, for a fixed number of atoms, the extent of possible parallelization is limited. However, many methods, including semiclassical approaches to quantum dynamics [4,5] and some approaches to rare events [6,7], require running thousands of independent molecular dynamics trajectories. Intelligent HTC, which can treat each trajectory as a task and manage data dependencies between tasks, provides a way to run these simulations on hardware up to the exascale, thus opening the possibility of studying previously intractable systems.

In practice, many scientific programmers are not aware of the range of middleware to facilitate parallel programming. When HTC-like approaches are implemented as part of a scientific software project, they are often done manually, or through custom scripts to manage SSH, or by running separate jobs and manually collating the results. Using the intelligent high-level approaches enabled by distributed computing middleware will simplify and speed up development.

Furthermore, middleware frameworks can meet the needs of many different computing infrastructures. For example, in addition to working within a single job on a cluster, COMPSs includes support for working through a cluster’s queueing system or working on a distributed grid. Moreover, architecting a software package such that it can take advantage of one HTC library will make it easy to use other HTC middleware. Having all of these possibilities immediately available will enable developers to quickly create software that can meet the needs of many users.

This E-CAM Extended Software Development Workshop (ESDW) will focus on intelligent HTC as a technique that crosses many domains within the molecular simulation community in general and the E-CAM community in particular. Teaching developers how to incorporate middleware for HTC matches E-CAM’s goal of training scientific developers on the use of more sophisticated software development tools and techniques.

This E-CAM extended software development workshop (ESDW) will focus on intelligent HTC, with the primary goals being:

1. To help scientific developers interface their software with HTC middleware.
2. To benchmark, and ideally improve, the performance of HTC middleware as applications approach extreme scale.

This workshop will aim to produce four or more software modules related to intelligent HTC, and to submit them, with their documentation, to the E-CAM software module repository. These will include modules adding HTC support to existing computational chemistry codes, where the participants will bring the codes they are developing. They may also include modules adding new middleware or adding features to existing middleware that facilitate the use of HTC by the computational chemistry community. This workshop will involve training both in the general topic of designing software to interface with HTC libraries, and in the details of interfacing with specific middleware packages.

The range of use for intelligent HTC in scientific programs is broad. For example, intelligent HTC can be used to select and run many single-point electronic structure calculations in order to develop approximate potential energy surfaces. Even more examples can be found in the wide range of methods that require many trajectories, where each trajectory can be treated as a task, such as:

* rare events methods, like transition interface sampling, weighted ensemble, committor analysis, and variants of the Bennett-Chandler reactive flux method
* semiclassical methods, including the phase integration method and the semiclassical initial value representation
* adaptive sampling methods for Markov state model generation
* approaches such as nested sampling, which use many short trajectories to estimate partition functions

The challenge is that most developers of scientific software are not familiar with the way such packages can simplify their development process, and the packages that exist may not scale to exascale. This workshop will introduce scientific software developers to useful middleware packages, improve scaling, and provide an opportunity for scientific developers to add support for HTC to their codes.

Major topics that will be covered include:

* Concepts of HTC; how to structure code for HTC
* Accessing computational resources to use HTC
* Interfacing existing C/C++/Fortran code with Python libraries
* Specifics of interfacing with Celery/COMPSs
* Challenges in using existing middleware at extreme scale

[1] Celery: Distributed Task Queue. http://celeryproject.org, date accessed 14 August 2017.

[2] R.M. Badia et al. SoftwareX 3-4, 32 (2015).

[3] S. Plimpton. J. Comput. Phys. 117, 1 (1995).

[4] W.H. Miller. J. Chem. Phys. 105, 2942 (2001).

[5] J. Beutier et al. J. Chem. Phys. 141, 084102 (2014).

[6] Du et al. J. Chem. Phys. 108, 334 (1998).

[7] G.A. Huber and S. Kim. Biophys. J. 70, 97 (1996).

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Extreme-Scale State-of-the-Art Workshop

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Goals of the Workshop:

The central goal of the 1st E-CAM Extreme-Scale State-of-the-art Workshop is to provide a forum for fellow E-CAM application end users and developers to:

  1. Identify emerging extreme-scale computing requirements across the centre, including from both academia and industry partners
  2. Increase the centre’s awareness of current and emerging HPC hardware and software technologies on the road to exascale computing
  3. Increase the centre’s awareness of PRACE services (Advanced Training, software enablement, and industry interactions)
  4. Interface with other members of the European HPC community
  5. Identify themes of future interest for the centre on the road to exascale computing

If you wish to apply for this workshop please do so through the CECAM website here.

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