The AI-SPRINT project, launched in 2021 and funded by the European Commission, focuses on the development and implementation of AI applications across the computing continuum. This continuum ensures the coherent integration of computational resources and services from centralized data centers to edge devices, facilitating efficient and adaptive computation and application delivery. AI-SPRINT has achieved significant scientific advances, including streamlined processes, improved efficiency, and the ability to operate in real time, as evidenced by three practical use cases. This paper provides an in-depth examination of these applications – Personalized Healthcare, Maintenance and Inspection, and Farming 4.0 – highlighting their practical implementation and the objectives achieved with the integration of AI-SPRINT technologies. We analyze how the proposed toolchain effectively addresses a range of challenges and refines processes, discussing its relevance and impact in multiple domains. After a comprehensive overview of the main AI-SPRINT tools used in these scenarios, the paper summarizes of the findings and key lessons learned.

Developing complex biomolecular workflows is not always straightforward. It requires tedious developments to enable the interoperability between the different biomolecular simulation and analysis tools. Moreover, the need to execute the pipelines on distributed systems increases the complexity of these developments. To address these issues, we propose a methodology to simplify the implementation of these workflows on HPC infrastructures. It combines a library, the BioExcel Building Blocks (BioBBs), that allows scientists to implement biomolecular pipelines as Python scripts, and the PyCOMPSs programming framework which allows to easily convert Python scripts into task-based parallel workflows executed in distributed computing systems such as HPC clusters, clouds, containerized platforms, etc. Using this methodology, we have implemented a set of computational molecular workflows and we have performed several experiments to validate its portability, scalability, reliability and malleability.

The adoption of Artificial intelligence (AI) technologies is steadily increasing. However, to become fully pervasive, AI needs resources at the edge of the network. The cloud can provide the processing power needed for big data, but edge computing is close to where data are produced and therefore crucial to their timely, flexible, and secure management. In this paper, we introduce the AI-SPRINT project, which will provide solutions to seamlessly design, partition, and run AI applications in computing continuum environments. AI-SPRINT will offer novel tools for AI applications development, secure execution, easy deployment, as well as runtime management and optimization: AI-SPRINT design tools will allow trading-off application performance (in terms of end-to-end latency or throughput), energy efficiency, and AI models accuracy while providing security and privacy guarantees. The runtime environment will support live data protection, architecture enhancement, agile delivery, runtime optimization, and continuous adaptation.

Progress in science is deeply bound to the effective use of high-performance computing infrastructures and to the efficient extraction of knowledge from vast amounts of data. Such data comes from different sources that follow a cycle composed of pre-processing steps for data curation and preparation for subsequent computing steps, and later analysis and analytics steps applied to the results. However, scientific workflows are currently fragmented in multiple components, with different processes for computing and data management, and with gaps in the viewpoints of the user profiles involved. Our vision is that future workflow environments and tools for the development of scientific workflows should follow a holistic approach, where both data and computing are integrated in a single flow built on simple, high-level interfaces. The topics of research that we propose involve novel ways to express the workflows that integrate the different data and compute processes, dynamic runtimes to support the execution of the workflows in complex and heterogeneous computing infrastructures in an efficient way, both in terms of performance and energy. These infrastructures include highly distributed resources, from sensors and instruments, and devices in the edge, to High-Performance Computing and Cloud computing resources. This paper presents our vision to develop these workflow environments and also the steps we are currently following to achieve it.

Recently, efforts have been made to bring together the areas of high-performance computing (HPC) and massive data processing (Big Data). Traditional HPC frameworks, like COMPSs, are mostly task-based, while popular big-data environments, like Spark, are based on functional programming principles. The earlier are know for their good performance for regular, matrix-based computations; on the other hand, for fine-grained, data-parallel workloads, the later has often been considered more successful. In this paper we present our experience with the integration of some dataflow techniques into COMPSs, a task-based framework, in an effort to bring together the best aspects of both worlds. We present our API, called DDF, which provides a new data abstraction that addresses the challenges of integrating Big Data application scenarios into COMPSs. DDF has a functional-based interface, similar to many Data Science tools, that allows us to use dynamic evaluation to adapt the task execution in runtime. Besides the performance optimization it provides, the API facilitates the development of applications by experts in the application domain. In this paper we evaluate DDF's effectiveness by comparing the resulting programs to their original versions in COMPSs and Spark. The results show that DDF can improve COMPSs execution time and even outperform Spark in many use cases.

Summary EUBrazilOpenBio is a collaborative initiative addressing strategic barriers in biodiversity research by integrating open access data and user‐friendly tools widely available in Brazil and Europe. The project deploys the EU‐Brazil Hybrid Data Infrastructure that allows the sharing of hardware, software and data on‐demand. This infrastructure provides access to several integrated services and resources to seamlessly aggregate taxonomic, biodiversity and climate data, used by processing services implementing checklist cross‐mapping and ecological niche modelling. A Virtual Research Environment was created to provide users with a single entry point to processing and data resources. This article describes the architecture, demonstration use cases and some experimental results and validation. Copyright © 2014 John Wiley & Sons, Ltd.

Cloud computing is inherently service-oriented: cloud applications are delivered to consumers as services via the Internet. Therefore, these applications can potentially benefit from the Service-Oriented Architecture (SOA) principles: they can be programmed as added-value services composed by pre-existing ones, thus favouring code reuse. However, new programming models are required to simplify their development, along with systems that are capable of orchestrating the execution of the resulting SaaS in the Cloud. In that regard, this paper presents Service Super scalar (Servicess), an alternative to existing PaaS which provides a programming model and execution runtime to ease the development and execution of service-based applications in clouds. Servicess is a task-based model: the user is only required to select the tasks, which can be services or regular methods, to be spawned asynchronously. The application, a composite service, is programmed in a totally sequential way and no API call must be included in the code. The runtime is in charge of automatically orchestrating the execution of the tasks in the Cloud, as well as of elastically deploying new virtual resources depending on the load. After describing the main characteristics of the programming model and the runtime, we evaluate the productivity of Servicess and show how it offers a good trade-off between programmability and runtime performance.

Abstract Increasingly, complex scientific applications are structured in terms of workflows. These applications are usually computationally and/or data intensive and thus are well suited for execution in grid environments. Distributed, geographically spread computing and storage resources are made available to scientists belonging to virtual organizations sharing resources across multiple administrative domains through established service‐level agreements. Grids provide an unprecedented opportunity for distributed workflow execution; indeed, many applications are well beyond the capabilities of a single computer, and partitioning the overall computation on different components whose execution may benefit from runs on different architectures could provide better performances. In this paper we describe the design and implementation of the Grid Resource Broker (GRB) workflow engine. Copyright © 2008 John Wiley & Sons, Ltd.

Abstract This paper describes the Grid Resource Broker (GRB), a Grid portal built leveraging a set of high‐level, Globus‐Toolkit‐based Grid libraries called GRB libraries. The portal leverages the Liferay framework to provide users with an intuitive, highly customizable Web GUI. The underlying GRB middleware allows trusted users seamless access to their computational Grid environments. Copyright © 2007 John Wiley & Sons, Ltd.