Research support

High Performance Computing

The DCSR team is able to help you regarding high performance computing topics including:
Here are the people involved in HPC topics:

Technical skills

Here are some topics on which we can help.

Using the clusters efficiently

The cluster are shared resources. In order to allow all users to get a fair access to the computing resources, the job scheduler has been configured so that excesses are avoided. However this does not prevent you from inefficiently using the resources. Depending on your workload we can help you to minimise the execution time and use of the resources required and therefore to minimise the billing and allowing you to get your results faster. This is usually achieved by tuning the job scripts and the threading parameters of your applications. We can also provide you with some insight regarding the use of the different storage locations on the clusters which can have a large impact on how long your jobs run.

Scientific computing & choice of optimised libraries

If you have to develop your own codes, several rules and good practices should be adopted. Furthermore, instead of reinventing the wheel and reprogramming everything from scratch, it's very likely than optimised and maintained libraries exist for many problems you will face. We can help you to choose the most commonly used optimised libraries and possibly to benchmark various libraries operating on the same topics according to your needs.

Profiling & code optimisation

Once you have developed a code, we can help you to profile it in order to identify bottlenecks and to improve some parts of the code where most time is being spent. Optimisation can be achieved in several ways including using a different algorithm, improving memory access, improving storage access, using vectorisation.

Parallelisation

Depending on your code, it could be possible to slightly modify the core computations so that it could be spread over several CPU cores or nodes. In some cases, it could also be possible and very interesting to port some parts of the code to GPU. Even if some languages like C/C++ or Fortran are more friendly to parallelisation, significant gains can also be obtained with Python, R, or even Julia codes.

Energy consumption

Energy consumption is a major concern in our current world. We are currently working on providing you with mechanisms that allow them to correlate your computations with the associated energy consumption. That can help you to choose between several computing strategies, to define trade-off between precision and consumption, or even to put the global benefit of your research respecting to your environmental impact.

Terms of support

We distinguish two kinds of support:

Contact

Please send an email to helpdesk@unil.ch and put "DCSR HPC support request" in the subject.

Machine Learning

Machine learning methods are nowadays used in a wide variety of scientific domains. For example,

- Unsupervised machine learning methods may be used as tools for data exploration and visualisation (dimension reduction and clustering),

- Supervised machine learning methods may be used as tools to make predictions: given an input data point, predict the output (classification or regression).

At the DCSR, we can help you with the following aspects of machine learning.

Formation

We can help you to understand how a particular machine learning method works and how it may be used in your research. Note that the DCSR gives a few short courses on machine learning; see https://www.unil.ch/ci/dcsr-en

Methodology

We can help you to choose and apply the appropriate machine learning methods in your research. This may involve a pilote phase during which we develop together codes and run them on your laptop or on the UNIL clusters. Later on, we can also help you with the production phase.

More specifically, we can help you to find existing machine learning programs that would fit the aim of your analysis, install and run them on your laptop or on the UNIL clusters, and explain the meaning of their underlying option parameters. If no satisfactory machine learning program exists, we can help you to develop new algorithms and write codes that would fit the aim of your analysis.

Infrastructure

We can help you to implement efficiently your machine learning pipeline on the UNIL clusters. More specifically, we can help you to install your codes on the UNIL clusters and to profile them in order to find the optimal setting (in terms of RAM, number of nodes and CPU/GPU).

Foster UNIL collaborations

We can help you to find appropriate experts at the UNIL to discuss with you about specific machine learning related problems. Note that the DCSR organises a ML Café every 4 months during which two researchers give short presentations on how they use machine learning in their work, followed by discussions. The presentations should be accessible to a broad audience that knows machine learning methods but does not necessarily know the specific scientific aspects of the works presented. In this way you will discover how machine learning tools are being used in various field of research. If you are interested in participating to this informal meeting, please send an email to helpdesk@unil.ch with subject: DCSR ML Café.

A few examples:

1. An experimental scientist would like to learn how to analyse his/her data on his/her laptop or on the UNIL clusters by using machine learning methods. We can help this researcher to find appropriate machine learning tools, to understand how they work and to use them on his/her laptop or on the UNIL clusters.

2. A data scientist would like to implement a machine learning pipeline (on his/her laptop or on the UNIL clusters) but he/she is not sure how to do it properly. We can help this researcher to find and apply appropriate machine learning methods.

3. A data scientist has implemented a machine learning pipeline (on his/her laptop or on the UNIL clusters) and would like to discuss about the methodology he/she used. We can help this researcher to check whether his/her approach is correct and we can also suggest alternative approaches.

4. A data scientist has implemented a machine learning pipeline on his/her laptop and would like to run it on the UNIL clusters (going from laptop to cluster). We can help this researcher to implement efficiently his/her pipeline on the UNIL clusters. This may involve the installation of softwares and the profiling of codes.

 

BioImage Analysis

The DCSR team can help you with your image analysis pipeline. The person involved in this is Arianna Ravera, Image Analysis and Machine Learning specialist.

Support Overview

From a quick question or a quick opinion, to a long project conceived and structured together. Everything related to extracting information and data from your images can be discussed together.

Here are some of the main topics that we are usually asked about:

Most of the analyzes carried out from us are programmed in Python as it is the most widespread programming language for the subject and is very fast and adaptive. (Please, if you're already familiar with Python, visit this very useful page: https://bioimagebook.github.io/index.html)
But...

Softwares

If you are not interested or do not feel ready to experiment with code, we can use together some easy and intuitive software.
Here something you can checkout:

Contact  &  Terms of support

Main contact: research-computing-fbm@unil.ch
Important: we distinguish two kinds of support:
Stay tuned: general info, scheduled events and meetings, or also to directly contact us, join our Teams channel:

https://teams.microsoft.com/l/team/19%3aFeDnpOEAd5F_q4vM_JnZhLMpm-gGBt03gAYMflkvqIg1%40thread.tacv2/conversations?groupId=92e190bf-29ce-42cd-9a11-616e8b7f01fa&tenantId=25933cd5-fa42-4290-9edd-84c5831bcdd8

Other contact: helpdesk@unil.ch with subject DCSR Image Analysis

Best Practices for Software Development

In progress

Database support for humanities

The DCSR provides support to researchers in the humanities for projects based on structured corpora (databases, digital libraries or collections, etc.).

The DCSR carries out a technological watch to provide researchers and research groups in the humanities with tools likely to cover some of the digital infrastructure needs encountered in the humanities. In the form of shared services, these tools enable researchers to organise, exploit and expose research databases online thanks to configurable presentation interfaces.

The DCSR relies on tools used and supported by a strong community (individuals and institutions). These tools are made available to researchers as shared services in order for the DCSR to provide a lasting and sustainable maintenance for the database and their presentation interfaces.

Database support for humanities

The DCSR offers support for each stage of research within the framework of the tools made available :

Terms of service

TBD

Contact

Researchers are encouraged to contact the DCSR well in advance (2 months) if the intended support is to help submit a project to a funding agency. Researchers should fill this form in order to provide the necessary details regarding the project.

Alternatively, for a one-time request, researchers can also contact directly the following people:

Arches

Arches est une plateforme open source pour la gestion des données issues du patrimoine culturel. La DCSR met à disposition des chercheurs de l'UNIL des instances locales d'Arches.

Arches

Présentation

Arches est une plateforme open source développée par le Getty Conservation Institute et le World Monuments Fund pour la gestion de données issues du domaine du patrimoine culturel.