Scientific workflows are increasingly characterized by complex task dependencies and large-scale dataexchanges, which place significant pressure on the input/output (I/O) systems of traditional Workflow Engines(WFEs). These challenges are particularly evident in data-intensive and real-time processing contexts, whereconventional disk-based I/O mechanisms often become performance bottlenecks. This paper presents anapproach to enhancing the DAGonStar scientific workflow engine by integrating CAPIO, a middleware designedto support memory-based streaming I/O. The integration combines DAGonStar's orchestration capabilities withCAPIO's efficient data handling to better support workflows operating on continuous or large-scale datasets.We describe the architectural modifications introduced to enable this collaboration and provide an analysis ofthe resulting system. The proposed solution aims to improve the responsiveness and flexibility of scientificworkflows by streamlining data transfers and simplifying task coordination. This work contributes to theevolution of workflow systems toward more efficient and scalable models for scientific computing.

Advances in big data and the growth in complexity of modern applications highlight the necessity for optimizing workflow executions on different levels, such as hybrid workflow executions, automatic optimization of data movements, and efficient use of IO. Following this line, streaming features are the desired capabilities for file-based workflows as they can reduce overall execution times. Expanding workflows with streaming capabilities usually requires rewriting the application, which is time-consuming and requires deep knowledge of the application. With this work, we introduce the Cross-Application Programmable IO (CAPIO) methodology, of which the stack is composed of two parts: the CAPIO-CL coordination language and the CAPIO middleware (which implements the semantics expressed by the CAPIO-CL coordination language). The CAPIO-CL coordination language annotates synchronization semantics between files produced and consumed by workflow steps. At the same time, the CAPIO middleware improves the performance of file-based workflows, leveraging the information provided by the CAPIO-CL language while not having to change (recompile) the code of the original workflow steps. By design, the CAPIO middleware supports multiple backends and can be extended to support more. It is dynamic, and it supports dynamic job scheduling. Benchmarks, done on both microbenchmarks and real-life workflows, prove that with CAPIO, it is possible to reduce the workflow execution time by up to similar to 50%.

Data stream processing (DSP) applications consist of data-flow graphs of operators that process data streams. These operators run as dedicated threads, either in parallel or concurrently, on computing platforms with multi-core CPUs. The decision of where to run threads of parallel programs, specifically which CPU core of the underlying architecture to use, is known as thread pinning, and it can significantly affect the application’s throughput. For DSP applications, finding an efficient pinning can be challenging as it depends on information about the data-flow structure, operator types, and communication patterns. Although thread pinning is a low-level optimization available in several parallel programming frameworks , DSP frameworks typically do not allow users to configure thread pinning, leaving the decision to the Operating System. This paper extends the WindFlow DSP library, and its FastFlow-based parallel runtime system, to expose thread pinning mechanisms through a usable API. The effectiveness of this approach is demonstrated through extensive evaluations of four applications, using six custom pinning strategies on a high-end server.

MTCL (Multi-Transport Communication Library) is a C++ header-only library that unifies heterogeneous transports, such as MPI, UCX, TCP, and MQTT, under a single connection-oriented API for point-to-point (Pt2Pt) and collective communication. This paper advances MTCL along four directions. First, we introduce a lightweight Proxy layer that brokers multi-cluster connectivity across NATs and firewalls via configurable pools, enabling cross-site deployments. Second, we extend the library with an asynchronous Pt2Pt API that enables overlap and pipelining. Third, we present a comprehensive experimental study across multiple transports and networks, with emphasis on collectives. Finally, we demonstrate the library's usability with a simple cross-silo case study, showing how the Proxy and unified API streamline orchestration while maintaining predictable performance. Together, these contributions move MTCL beyond its initial release and position it as a practical building block for multi-protocol distributed applications spanning edge, cloud, and HPC.

The increasing complexity and scale of data-intensive scientific workflows necessitate advancements in workflow engines (WFEs) to handle real-time data streams and reduce input/output (I/O) bottlenecks. This paper introduces an innovative approach to enhancing the DAGonStar scientific workflow engine by integrating CAPIO, a middleware capable of injecting I/O streaming capabilities into traditional scientific workflows and optimized for high-speed data access and low latency. By combining DAGonStar's robust task orchestration and dependency management with CAPIO, we aim to significantly improve scientific workflows' performance and scalability. We present the design and implementation of this integration, detailing the architectural modifications required to enable seamless interaction between DAGonStar and CAPIO. The paper includes comprehensive benchmarks and performance evaluations demonstrating the impact of CAPIO on workflow execution times and data handling efficiency. Our findings indicate that the enhanced DAGonStar, equipped with CAPIO, offers a powerful solution for managing and processing large-scale, real-time data streams, thereby advancing the capabilities of scientific computing infrastructure.

The Compute Continuum paradigm addresses the challenges of heterogeneous and dynamic computing resources, facilitating distributed application execution while enhancing data locality, performance, availability, adaptability, and energy efficiency. By integrating IoT, edge, and cloud resources into a cohesive continuum, applications can operate closer to data sources and end users. This approach supports refined adaptation strategies tailored to specific infrastructure components, enabling reduced latency, optimized bandwidth use, and improved privacy. To fully realize the Compute Continuum’s potential, autonomous and proactive management is essential, leveraging interdisciplinary methods from optimization theory, control theory, machine learning, and artificial intelligence. This special issue highlights advancements in three key areas: resource characterization and scheduling, middleware for application deployment and reconfiguration, and applications in the Compute Continuum. These contributions highlight innovative solutions for resource optimization, dynamic management, and real-world implementations, showcasing the potential of the Compute Continuum to revolutionize distributed computing across diverse domains.

A bstract A measurement of the $$ {K}^{+}\to {\pi}^{+}\nu \overline{\nu} $$ K + → π + ν ν ¯ decay by the NA62 experiment at the CERN SPS is presented, using data collected in 2021 and 2022. This dataset was recorded, after modifications to the beamline and detectors, at a higher instantaneous beam intensity with respect to the 2016–2018 data taking. Combining NA62 data collected in 2016–2022, a measurement of $$ \mathcal{B}\left({K}^{+}\to {\pi}^{+}\nu \overline{\nu}\right)=\left({13.0}_{-3.0}^{+3.3}\right)\times {10}^{-11} $$ B K + → π + ν ν ¯ = 13.0 − 3.0 + 3.3 × 10 − 11 is reported. With 51 signal candidates observed and an expected background of $$ {18}_{-2}^{+3} $$ 18 − 2 + 3 events, $$ \mathcal{B}\left({K}^{+}\to {\pi}^{+}\nu \overline{\nu}\right) $$ B K + → π + ν ν ¯ becomes the smallest branching ratio measured with a signal significance above 5 σ .

We present FastFlow-Python, a framework that brings parallelism to Python for stream-processing applications. FastFlow-Python enables developers to build high-throughput, low-latency data-flow networks by instantiating high-level, ready-to-use parallel building blocks. Built on the C++ FastFlow library, it leverages Python bindings via the C/Python API to efficiently manage parallel execution using both subinterpreters and multiprocessing, all abstracted by the framework. We demonstrate the performance benefits of FastFlow-Python through a comparative analysis with a pure Python stream-processing implementation, highlighting its effectiveness in overcoming the limitations imposed by the Global Interpreter Lock (GIL). Experimental results show almost linear scalability when increasing the number of workers.

The European Union’s efforts towards technological sovereignty in High-Performance Computing are driving research and development of RISC-V-based supercomputers. The DARE SGA1 project, in particular, aims to develop chips designed and owned by Europeans. This paper introduces the Italian contribution to DARE SGA1 regarding pathfinding activities toward future RISC-V-based accelerator designs, reliability improvements, system software, and AI and Quantum Chemistry applications.

The NA62 experiment at the CERN SPS reports the first detection of a tagged neutrino candidate based on the data collected in 2022. The candidate consists of a K+ -> mu(+)nu(mu) decay where the charged particles are reconstructed and the neutrino is detected through a charged-current interaction in a liquid krypton calorimeter.

The performance bottleneck in file-based workflows remains a pressing issue in the realm of I/O-based workflows. To address this challenge, a novel annotation language has been developed. CAPIO-CL is positioned as an innovative I/O coordination language, enabling users to annotate data dependencies within file-based workflows with synchronization semantics pertinent to the involved files and directories. Through the information provided by the language, optimization opportunities arise in streaming and preemptive data movement. This paper serves to illustrate the semantics and syntax enabling CAPIO-CL to enhance the performance of in situ workflows without necessitating the rewriting or modification of the original workflow application steps. Finally, an analysis of CAPIO-CL is provided, taking into consideration both language expressiveness and application performance enhancement.

A measurement of the K^+→π^+νν decay by the NA62 experiment at the CERN SPS is presented, using data collected in 2021 and 2022. This dataset was recorded, after modifications to the beamline and detectors, at a higher instantaneous beam intensity with respect to the 2016–2018 data taking. Combining NA62 data collected in 2016–2022, a measurement of ℬ(K^+→π^+νν)=(13.0_-3.0^+3.3)×10^-11 is reported. With 51 signal candidates observed and an expected background of 18_-2^+3 events, ℬ(K^+→π^+νν) becomes the smallest branching ratio measured with a signal significance above 5σ.

This paper introduces DAGonCAPIO, a workflow framework that integrates the DAGonStar engine with the CAPIO middleware to enable I/O streaming in scientific workflows. DAGonStar utilizes a Directed Acyclic Graph (DAG) to orchestrate tasks. At the same time, CAPIO enables downstream tasks to process data as soon as partial outputs become available, without requiring modifications to application code. This integration reduces delays from traditional file-based communication. The system uses the workflow:// schema to define data dependencies and generate CAPIO coordination scripts. DAGonStar was modified to support early scratch directory naming and decoupled task execution. Experiments with a WRF-based weather forecasting workflow on a 256-core cluster demonstrate that DAGonCAPIO reduces time-to-first-result by up to 4171 seconds, achieving a nearly 10x speedup.

The recent evolution of IoT devices, coupled with the increasing interest in fog and edge computing, highlights that technologies traditionally developed to support real-time applications can also benefit other contexts, such as processing large volumes of data generated by IoT devices. In this paper, we show how to use a real-time control group scheduler to support data-processing applications based on the FastFlow framework. We first detail the design and implementation of the scheduler, then describe the application scenario considered. Our experimental evaluation confirms the effectiveness of the proposed approach.

RED-SEA (https://redsea-project.eu/) is a European project funded in the framework of the H2020-JTI-EuroHPC-2019-1 call that started in April 2021. The goal of the project is to evaluate the architectural design of the main elements of the interconnection networks for the next generation of HPC systems supporting hundreds of thousands of computing nodes enabling the Exascale for HPC, HPDA and AI applications while providing preliminary prototypes. The main technological feature is the BXI network, originally designed and produced by ATOS (France). The plan is to integrate in the next release of the network – BXI3 – the architectural solutions and novel IPs developed within the framework of the RED-SEA project. The consortium is composed of 11 well-established research teams across Europe, with extensive experience in interconnects, including network design, deployment and evaluation. Within RED-SEA, INFN is adopting a hardware/software co-design approach to design APEnetX, a scalable interconnect prototyped on latest generation Xilinx FPGAs, adding innovative components for the improvement of the performance and resiliency of the interconnect. APEnetX is an FPGA-based, PCIe Gen3/4 network interface card equipped with RDMA capabilities being the endpoint of a direct multidimensional toroidal network and suitable for integration in the BXI environment. APEnetX design will be benchmarked on project testbeds using real scientific applications like NEST, a spiking neural network simulator.

This paper addresses challenges arising from inherent heterogeneity in the rapidly evolving landscape of computing infrastructures, spanning diverse environments across the compute continuum. As a result of the current transition from centralized architectures to contemporary distributed and edge computing models, the demand for innovative programming paradigms becomes increasingly apparent. These paradigms must efficiently harness available resources while seamlessly accommodating their heterogeneity, thus relieving programmers of management burdens. Our work encompasses various resource types within the computational environment to maximize efficiency, responsiveness, and availability. At its core, our contribution introduces a structured programming approach that facilitates the integration and exploitation of dynamically enlisted resources. A dynamic allocation policy and a communication abstraction layer orchestrate resources and workloads adaptively. To address the heterogeneity of the execution environment, we adopt a fat binary that encapsulates diverse executable formats in a single deployment package. Empirical evaluation validates the efficacy of our programming pattern in meeting real-world application needs within the compute continuum. Our approach demonstrates significant promise in navigating the complexities posed by the diverse and dynamic nature of modern computing environments.

The NA62 experiment at CERN utilises a differential Cherenkov counter with achromatic ring focus (CEDAR) for tagging kaons within an unseparated monochromatic beam of charged hadrons. The CEDAR-H detector was developed to minimise the amount of material in the path of the beam by using hydrogen gas as the radiator medium. The detector was shown to satisfy the kaon tagging requirements in a test-beam before installation and commissioning at the experiment. The CEDAR-H performance was measured using NA62 data collected in 2023.

A sample of 3984 candidates of the K+→π+γγ decay, with an estimated background of 291±14 events, was collected by the NA62 experiment at CERN during 2017–2018. In order to describe the observed di-photon mass spectrum, the next-to-leading order contribution in chiral perturbation theory was found to be necessary. The decay branching ratio in the full kinematic range is measured to be (9.61±0.17)×10−7. The first search for production and prompt decay of an axion-like particle with gluon coupling in the process K+→π+a, a→γγ is also reported.

ADAPIO symposium was a forum to discuss how to create adaptive I/O systems through the creation of an active I/O stack that dynamically adjusts computation and storage requirements through intelligent coordination, malleability of computation and I/O, and the scheduling of storage resources along all levels of the storage hierarchy. Moreover, ADAPIO shows examples of co-designing applications and I/O.