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25/29 March 2024 in Taipei

ISGC 2024 - International Symposium on Grids and Clouds

EGI is thrilled to share the invitation to participate in and contribute to ISGC 2024

Promoting the open data/open science collaboration between Asia Pacific region and the world, the Symposium offers an excellent opportunity to learn from the latest achievements from Europe, America and Asia. The goal of ISGC is to create a face-to-face venue where individual communities and national representatives can present and share their contributions to the solutions to global challenges.


Topics of Interest

(1) Physics and Engineering Applications
Submissions should report on research for physics and engineering applications exploiting grid, cloud, or HPC services, applications that are planned or under development, or application tools and methodologies.

(2) Health & Life Sciences (including Pandemic Preparedness Applications)

During the last decade, research in Biomedicine and Life Sciences has dramatically changed thanks to the continuous developments in High Performance Computing and highly Distributed Computing. The recent pandemic caused by Sars-CoV2 has clearly demonstrated the critical role of e-Infrastructures such high performance, high throughput and clouds infrastructure, but also of big-data and machine learning solutions to support the worldwide efforts to fight this pandemic.

(3) Earth & Environmental Sciences & Biodiversity Applications

Earth system and environmental sciences go far beyond investigating physical sub-systems of our planet: increasingly, we develop an understanding of the Earth as a single, highly complex, coupled
physical system with living and dead organisms. For this purpose, information technology is widely used, for tasks from data taking and analysis to modelling of large parts of the earth system. Climate change has made research in this field mandatory, and supercomputing are getting involved in addressing the mitigation of natural hazards – in particular in Asia. This centre rack as a part of ISGC 2024 not only invites contributions from the classical simulation or data analysis sectors: it encourages presentations on works that involve artificial intelligence (AI). Thus, we reflect that earth system and
environmental sciences have been considerably enriched by AI techniques, in fields from weather-model downscaling over anomaly detection in measurements to surrogate models in digital twins of the
earth system. Projects that emphasize open science, open/FAIR data, and effective communication with stakeholders are particularly encouraged to submit their work.

(4) Social Sciences, Arts & Humanities (SSAH) Applications

Disciplines across the Social Sciences, Arts and Humanities (SSAH) have critically engaged with technological innovations such as grid- and cloud computing, and, most recently, various data analytic
technologies. The increasing availability of data, ranging from social media text data to consumer big data has led to an increasing interest in analysis methods such as natural language processing,
multilingualism and (semi-)automatic AI-powered translations, social network analysis, usage data analysis, machine learning and text mining, and data sharing. These developments pose challenges as well as opening up a world of opportunities. Members of the SSAH community have been at the forefront of discussions about the impact that novel forms of data, novel computational infrastructures and novel analytical methods have for the pursuit of science endeavours and our understanding of what science is and can be.

(5) Virtual Research Environment (including tools, services, workflows, portals, … etc.)

Virtual Research Environments (VRE) provide an intuitive, easy-to-use and secure access to (federated) computing resources for solving scientific problems, trying to hide the complexity of the underlying infrastructure, the heterogeneity of the resources, and the interconnecting middleware. Behind the scenes, VREs comprise tools, middleware and portal technologies, workflow automation as well a security solutions for layered and multifaceted applications. Topics of interest include but are not limited to: (1) Real-world experiences building and/or using VREs to gain new scientific knowledge; (2) Middleware technologies, tools, services beyond the state-of-the-art for VREs; (3) Science gateways as specific VRE environments, (4) Innovative technologies to enable VREs on arbitrary devices, including Internet-of-Things; and (5) One-step-ahead workflow integration and
automation in VREs.

(6) Data Management & Big Data

The rapid growth of the data available to scientists and scholars – in terms of Velocity and Variety as well as sheer Volume – is transforming research across disciplines. Increasingly these data sets are generated not just through experiments, but as a byproduct of our day-to-day digital lives. This track explores the consequences of this growth, and encourages submissions relating to two aspects in
particular – firstly, the conceptual models and analytical techniques required to process data at scale; secondly, approaches and tools for managing and creating these digital assets throughout their lifecycle.

(7) Network, Security, Infrastructure & Operations

Networking and the connected e-Infrastructures are becoming ubiquitous. Ensuring the smooth operation and integrity of the services for research communities in a rapidly changing environment
are key challenges. This track focuses on the current state of the art and recent advances in these areas: networking, infrastructure, operations, and security. The scope of this track includes advances in
high-performance networking (software defined networks, community private networks, the IPv4 to IPv6 transition, cross-domain provisioning), the connected data and compute infrastructures (storage
and compute systems architectures, improving service and site reliability, interoperability between infrastructures, data centre models), monitoring tools and metrics, service management (ITIL and SLAs), and infrastructure/systems operations and management.

(8) Infrastructure Clouds and Virtualizations

This track will focus on the development of cloud infrastructures and on the use of cloud computing and virtualization technologies in large-scale (distributed) computing environments in science and
technology. We solicit papers describing underlying virtualization and “cloud” technology including integration of accelerators and support for specific needs of AI/ML and DNN, scientific applications and case studies related to using such technology in large scale infrastructure as well as solutions overcoming challenges and leveraging opportunities in this setting. Of particular interest are results
exploring the usability of virtualization and infrastructure clouds from the perspective of machine learning and other scientific applications, the performance, reliability and fault-tolerance of
solutions used, and data management issues. Papers dealing with the cost, price, and cloud markets, with security and privacy, as well as portability and standards, are also most welcome.

(9) Converging High Performance Computing Infrastructures: Supercomputers, clouds, accelerators 

The classical simulation-oriented computing is nowadays complemented by the machined deep learning approaches. This requires novel approaches to build and integrate high performance computing infrastructures, combining supercomputers, clouds, and specialized accelerator and artificial intelligence hardware. The integration of these different systems, often provided by different owners and different location, requires new ideas for distribution and federation.

(10) Artificial Intelligence (AI)

During the last decade, Artificial Intelligence (AI) and statistical learning techniques have started to become pervasive in scientific applications, exploring the adoption of novel algorithms, modifying
the design principles of application workflows, and impacting the way in which grid and cloud computing services are used by a diverse set of scientific communities. This track aims at discussing problems, solutions and application examples related to this area of research, ranging from R&D activities to production-ready solutions. Topics of interests in this track include: AI-enabled scientific workflows; novel approaches in scientific applications adopting machine learning (ML) and deep learning (DL) techniques; cloud-integrated statistical learning as-a-service solutions; anomaly detection techniques; predictive and prescriptive maintenance; experience with MLOps practices; AI-enabled adaptive simulations; experience on ML/DL models training and inference on different hardware resources for scientific applications.