CINECA releases innovative services for interactive computing via two-factor authentication, leveraging the ICE4HPC application by E4 Computer Engineering.
CINECA Consorzio Interuniversitario, an organisation that has been supporting the research activities of the academic scientific community and offering computing services to universities in Italy for more than 50 years, recently announced the release of additional functionality for the more than 5000 users of supercomputing systems and applications
The new interactive computing service, which went into pre-production at the end of June on CINECA’s Galileo100 supercomputer, will provide users with a completely new way of accessing supercomputing resources with a capacity of over 2 Pflops and over 20 PB of data storage. Access to the computing systems will be completely via a web browser, an innovative and flexible approach in the field of supercomputing. It will thus be possible for each user to create a working environment that best suits their research needs, combining data analysis tools, visualisation, workflow creation, and other functions that will gradually be implemented in the new interactive computing service. Once logged in with the two-factor authentication system, in order to increase the level of security, resources will be accessible almost immediately, eliminating the waiting time usually required to start an interactive processing session.
“When it comes to applied research, the constant availability of applications is an important value, as is the ability to switch seamlessly from one session to another,” explains Sanzio Bassini, Director of CINECA’s SuperComputing Applications and Innovation Department (SCAI). “With the support of E4 Analytics, we have been able to provide our data scientists with a specific module, created specifically for the HPC world, which allows them to optimise their activities in terms of simplicity, functionality and effectiveness, exploiting a mode of cloud access to CINECA’s HPC architecture.“
The interface, based on the new ICE4HPC application developed by E4 Computer Engineering and E4 Analytics, provides web-based access to computing resources, while introducing Jupyter technology into the HPC environment, which enables the creation of interactive notebooks and allows users to explore data, run simulations and display results in real time. In addition, the new interactive environment integrates directly with the resource management system currently in production at CINECA (the SLURM scheduler), and enables efficient management of all requests sent to supercomputing environments. The services are designed to improve the user experience for both interactive and command-line users, prioritising usability and efficiency.
“The collaboration with CINECA is a historic one for E4, and stems from the deep harmony of views that exists between the two organisations, both focused on technological excellence,” adds Cosimo Gianfreda, CEO of E4 Computer Engineering. “CINECA researchers contribute to shaping the technological developments that will define the future, and it is an honour for us to be able to support this research activity with our application solutions that can help them operate with maximum effectiveness.“
The working environments available to scientists and researchers will include from the outset:
- Python for specific tasks (e.g. Dask, Pytorch, Tensorflow…) with the possibility of creating customised ones;
- C/C++ kernel (Xeus);
- SLURM Queue Manager (a web interface to submit and monitor jobs on CINECA-enabled systems);
- Integration with NVIDIA monitoring tools;
- VSCode interface;
- Kernel R.
“By its nature, HPC research requires a mix of high-level application resources, which researchers and data scientists are called upon to use every day, seamlessly,” adds Mario Rosati, Board Member of E4 Computer Engineering and CEO of E4 Analytics. “With ICE4HPC, we provide them with a multi-user interactive computing service for HPC clusters based on Jupyter Notebook technologies, which implements an interactive workbench capable of handling GPU computing for data analysis and visualisation, big data analytics, machine learning and deep learning.“