Rescale Case Studies Accelerating Nanofluidics Research with IoT: A Case Study at the University of Kansas

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	Accelerating Nanofluidics Research with IoT: A Case Study at the University of Kansas Rescale

Accelerating Nanofluidics Research with IoT: A Case Study at the University of Kansas

Rescale

Technology Category	Analytics & Modeling - Digital Twin / Simulation Robots - Parallel Robots
Applicable Industries	Equipment & Machinery Life Sciences
Applicable Functions	Procurement Product Research & Development
Use Cases	Digital Twin Virtual Reality
Challenge	Eric Lee, a graduate student at the University of Kansas, was conducting research on nanofluidics, specifically nanowetting problems. His work involved the use of molecular dynamics codes, such as LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator), for computational analyses. However, to fully leverage the scalability of LAMMPS and expedite his analyses, he required access to a large computing cluster that could work seamlessly with LAMMPS. He also needed a platform that could support his specific version of LAMMPS and any custom scripts he wanted to upload. The challenge was to find a solution that could provide the necessary computational power and software compatibility, while also being user-friendly and responsive to his specific needs. Read More
About Customer	The customer in this case study is Eric Lee, a graduate student at the University of Kansas. His research focuses on nanofluidics, specifically nanowetting problems. Nanofluidics is the study of nanoflows in and around nanosized objects, and is the basis for miniaturization of microfluidic devices down to the nanoscale. Eric uses molecular dynamics codes, such as LAMMPS, for his computational analyses. His work requires him to run multiple analyses quickly and efficiently, necessitating access to a large computing cluster that can work seamlessly with LAMMPS. Read More
Solution	The solution came in the form of Rescale, a platform that supports LAMMPS and many other software codes across domains. Rescale's hardware was state-of-the-art, promising faster run times for LAMMPS jobs. Initially, Rescale did not include some custom LAMMPS packages necessary for Eric's work, but their engineers quickly resolved this issue. The platform also provided quick and helpful customer support, responding to Eric's queries within a few hours and providing useful screenshots to guide him in setting up his LAMMPS job. The process to run a job on Rescale was simple and straightforward, involving setting up a simulation name, choosing the number of cores needed, uploading the input file and associated data files, choosing the analysis type, and submitting the job. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Digital Twin / Simulation

Robots - Parallel Robots

Applicable Industries

Equipment & Machinery

Life Sciences

Applicable Functions

Procurement

Product Research & Development

Use Cases

Digital Twin

Virtual Reality

Challenge

Eric Lee, a graduate student at the University of Kansas, was conducting research on nanofluidics, specifically nanowetting problems. His work involved the use of molecular dynamics codes, such as LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator), for computational analyses. However, to fully leverage the scalability of LAMMPS and expedite his analyses, he required access to a large computing cluster that could work seamlessly with LAMMPS. He also needed a platform that could support his specific version of LAMMPS and any custom scripts he wanted to upload. The challenge was to find a solution that could provide the necessary computational power and software compatibility, while also being user-friendly and responsive to his specific needs.

About Customer

The customer in this case study is Eric Lee, a graduate student at the University of Kansas. His research focuses on nanofluidics, specifically nanowetting problems. Nanofluidics is the study of nanoflows in and around nanosized objects, and is the basis for miniaturization of microfluidic devices down to the nanoscale. Eric uses molecular dynamics codes, such as LAMMPS, for his computational analyses. His work requires him to run multiple analyses quickly and efficiently, necessitating access to a large computing cluster that can work seamlessly with LAMMPS.

Solution

The solution came in the form of Rescale, a platform that supports LAMMPS and many other software codes across domains. Rescale's hardware was state-of-the-art, promising faster run times for LAMMPS jobs. Initially, Rescale did not include some custom LAMMPS packages necessary for Eric's work, but their engineers quickly resolved this issue. The platform also provided quick and helpful customer support, responding to Eric's queries within a few hours and providing useful screenshots to guide him in setting up his LAMMPS job. The process to run a job on Rescale was simple and straightforward, involving setting up a simulation name, choosing the number of cores needed, uploading the input file and associated data files, choosing the analysis type, and submitting the job.

Impact #1	The use of Rescale not only accelerated Eric's research by reducing the time taken for LAMMPS simulations, but also provided a user-friendly platform for running his jobs. The platform was able to support his specific version of LAMMPS and any custom scripts he wanted to upload. Furthermore, Rescale's customer support was quick and helpful, responding to Eric's queries within a few hours and providing useful guidance on setting up his LAMMPS job. This level of support and ease of use made the process of running jobs on Rescale straightforward and efficient, contributing to the overall success of Eric's research.

Impact #1

The use of Rescale not only accelerated Eric's research by reducing the time taken for LAMMPS simulations, but also provided a user-friendly platform for running his jobs. The platform was able to support his specific version of LAMMPS and any custom scripts he wanted to upload. Furthermore, Rescale's customer support was quick and helpful, responding to Eric's queries within a few hours and providing useful guidance on setting up his LAMMPS job. This level of support and ease of use made the process of running jobs on Rescale straightforward and efficient, contributing to the overall success of Eric's research.

Benefit #1	LAMMPS simulations that usually took about 4 days to finish on local machines were completed in less than 12 hours on Rescale.
Benefit #2	The use of Rescale resulted in a significant productivity gain for Eric's research.

Benefit #1

LAMMPS simulations that usually took about 4 days to finish on local machines were completed in less than 12 hours on Rescale.

Benefit #2

The use of Rescale resulted in a significant productivity gain for Eric's research.

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Overview

Accelerating Nanofluidics Research with IoT: A Case Study at the University of Kansas

Operational Impact

Quantitative Benefit