Get more throughput, resize me! A case of study: LAMMPS malleable

Project reference: 1804

This project is based on LAMMPS, a classical molecular dynamics code, used to model atoms or, more generically, as a parallel particle simulator at the atomic, meso, or continuum scale.

LAMMPS runs on single processors or in parallel using message-passing techniques and a spatial-decomposition of the simulation domain. Many of its models have versions that provide accelerated performance on CPUs, GPUs, and Intel Xeon Phis. The code is designed to be easy to modify or extend with new functionality.

In this project, we aim to convert the MPI version of LAMMPS in malleable, in other words, a version of LAMMPS able to be resized, in terms of number of processes, during its execution time.

For the last years, malleability has proven to be an interesting solution for high-throughput computing (HTC) and energy saving in large high-performance facilities. From previous works, we have learned how remarkable is the effect of the dynamic reconfiguration in the execution of a workload composed, partly or wholly, of malleable jobs. Since LAMMPS is a well-known HPC application, used in a wide range of scientific fields, we are interested in obtain a reconfigurable version of it and analyze its behavior when it is included in a workload.

Project Mentor: Sergio Iserte

Project Co-mentor: Rafael Mayo

Site Co-ordinator: Maria-Ribera Sancho

Learning Outcomes:
The student will learn about the structure of modern scientific software and distributed computation.

Student Prerequisites (compulsory):
A good knowledge of C++ and MPI is required.

Student Prerequisites (desirable):
It is also desirable to be familiar with resource manager systems (RMS).

Training Materials:


  • Week 1: Training week
  • Week 2: Literature Review Preliminary Report (Plan writing)
  • Week 3 – 6: Project Development
  • Week 7: Throughput analysis
  • Week 8: Final Report write-up

Final Product Description:
The result will be a malleable version of LAMMPS and the subsequent performance/throughput analysis in a workload of more jobs.

Adapting the Project: Increasing the Difficulty:
The project can be adapted to the difficulty by including more LAMMPS features to the reconfiguration.

Adapting the Project: Decreasing the Difficulty:
The difficulty can be decreased by using other molecular dynamics code such as miniMD.

The student will have a workstation and access to the research cluster of our group.

Universitat Jaume I, Castelló de la Plana, Spain

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