- Collections

Use collections to bring together multiple datasets and their associated files in an almost unlimited number of ways.

- Quantum molecular dynamics simulations are pivotal to understanding and predicting the microscopic details of molecules, and strongly rely on a combined theoretical and computational effort. When considering molecular systems, the complexity of the underlying equations is such that approximations have to be devised, and the resulting theories need to be translated into algorithms and computer programs for numerical simulations. In the last decades, the joint effort of theoretical physicists and quantum chemists around the challenges of quantum dynamics made it possible to investigate the quantum dynamics of complex molecular systems, with applications ranging from energy conversion, energy storage, organic electronics, light-emitting devices, biofluorescent molecules, or photocatalysis, to name a few.

Two different strategies have been successfully applied to perform quantum molecular dynamics: wavepacket propagation or trajectories. The first family of methods includes all quantum nuclear effects, but their computational cost hampers the simulation of systems with moderate number of more than 10-12 degrees of freedom. The method coined multi-configuration time-dependent Hartree (MCTDH) constitutes one of the most successful developments in this field and is often considered as a gold standard for quantum dynamics [1]. Other strategies for wavepacket propagation try to identify procedures to optimize the “space” where the wavefunction information is computed, such that Cartesian grids can be replaced with Smolyak grids [2]. The second family of methods introduces the idea of trajectories as a way to approximate the nuclear subsystem, either classically or semiclassically, and is exemplified by methods like the trajectory surface hopping and Ehrenfest schemes [3], or the more accurate methods coupled-trajectory mixed quantum-classical (CT-MQC) [4] and quantum-classical Liouville equation (QCLE) [5].

From a computational perspective, both families of methods require extensive electronic structure calculations, as the nuclei move under the effect of the electronic subsystem, either “statically” occupying its ground state or “dynamically” switching between excited states. Solving the quantum nuclear dynamics equations also becomes in itself very expensive in the case of wavepacket propagation methods. Contrary to other, more consolidated, areas of modeling, quantum dynamics simulations do not benefit from established community packages and most of the progress occurs based on in-house codes, difficult to maintain and with limits in optimization and portability. One of the core actions of E-CAM has been to seed a change in this situation, by promoting systematic developments of software, providing a repository to host and share code, and fostering collaborations on adding functionalities and improving the performance of common software scaffolds for wavepacket (Quantics) and trajectory-based (PaPIM) packages. Collaborations on developments on other codes have also been initiated. This workshop aims at continuing and extending these activities based on input from the community. - Jul 11, 2019
- 1 0

- Last year we had a workshop that looked at some possibilities for High Throughput Computing (you can find all the details here). We would like to invite you to a follow-up workshop this year from July 1-5, again hosted in Turin.

The workshop will be 3.5 days long consisting of 1.5 days with three different Python libraries related to Dask:

Dask: https://docs.dask.org/en/latest/

Dask_jobqueue: https://dask-jobqueue.readthedocs.io/en/latest/

jobqueue_features: https://github.com/E-CAM/jobqueue_features

The last library is something that has been developed since last year by E-CAM. It allows the user to create tasks that call out to MPI programs, and easily configure the tasks to run on different types of resources (CPU/GPU/KNL).

The final 2 days will be a hackathon where you can work on your own use case with technical assistance. - Jul 01, 2019
- 3 0

- 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 Dask.distributed or COMP Superscalar (COMPSs), 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. Unfortunately, for a fixed number of atoms, the extent of possible parallelization is limited. However, many methods, including semiclassical approaches to quantum dynamics and some approaches to rare events, 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, Dask.distributed and COMPSs include 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. - Jul 03, 2018
- 10 1

Powered by Clowder (1.7.1#local branch:N/A sha1:N/A).