Andrei Tsaregorodtsev

The DIRAC interware has long served as a vital resource for user communities seeking access to distributed computing resources. Originating within the LHCb collaboration around 2000, DIRAC has undergone significant evolution. Despite its efficacy in meeting experiment-specific requirements, DIRAC has accrued technical debt over its 15-year history. Installation management remains intricate, with significant entry barriers and a reliance on bespoke infrastructure. The software development process lacks alignment with contemporary standards, impeding the onboarding process for new developers. Notably, integral components such as the network protocol and authentication mechanisms are proprietary and pose challenges for seamless integration with external applications. In response to these challenges, the DIRAC consortium has embarked on the development of DiracX. DiracX heralds a new era in distributed computing solutions. This contribution describes technical decisions, roadmap and timelines for the development of DiracX, while recognizing the criticality of maintaining a continuously operational DIRAC system for numerous user communities.

DIRAC is the interware for building and operating large scale distributed computing systems. It is adopted by multiple collaborations from various scientific domains for implementing their computing models. DIRAC provides a framework and a rich set of ready-to-use services for Workload, Data and Production Management tasks of small, medium and large scientific communities having different computing requirements. The base functionality can be easily extended by custom components supporting community specific workflows. DIRAC is at the same time an aging project, and a new DiracX project is taking shape for replacing DIRAC in the long term. This contribution will highlight DIRAC’s current, upcoming and planned capabilities and technologies, and how the transition to DiracX will take place. Examples include, but are not limited to, adoption of security tokens and interactions with Identity Provider services, integration of Clouds and High Performance Computers, interface with Rucio, improved monitoring and deployment procedures.

Joint Institute for Nuclear Research (JINR) has several large computing facilities: Tier1 and Tier2 grid clusters, Govorun supercomputer, cloud, and LHEP computing cluster. Each of them has different access protocols, authentication and authorization procedures, data access methods. With the help of the DIRAC Interware, we were able to integrate all these resources to provide a uniform access to all these facilities. Right now, it is possible to perform basic workflows on all resources. The main use-cases covered by the DIRAC service in JINR: centralized Monte-Carlo simulation for the MPD experiment, Monte-Carlo for the Baikal-GVD neutrino telescope, as well as running jobs for the Folding@HOME project. During the pre-production stage, it is important to estimate the characteristics of user jobs. That information is crucial for planning of the execution process on different resources. An approach was elaborated to collect data about RAM, CPU, and network consumption by each user job. This helped to tune the mass production algorithms before the production actually starts. Since the system processes tens of thousands of similar jobs during one particular production, it appeared to be possible to collect from DIRAC data about these jobs and perform analysis of their execution parameters. We collect data related to the CPU model, wall-time, CPU benchmark DB12, hostname, username, and resource name. An approach was elaborated to extract meta-data about job execution and visualize it. With this visualization, it became possible to compare different computing resources, study the CPU and worker node performance. This approach does not require submitting special jobs, so the resources are not wasted for this analysis.

Efficient access to distributed computing and storage resources is mandatory for the success of current and future High Energy and Nuclear Physics Experiments. DIRAC is an interware to build and operate distributed computing systems. It provides a development framework and a rich set of services for the Workload, Data and Production Management tasks of large scientific communities. A single DIRAC installation provides a complete solution for the distributed computing of one, or more than one collaboration. The DIRAC Workload Management System (WMS) provides a transparent, uniform interface for managing computing resources. The DIRAC Data Management System (DMS) offers all the necessary tools to ensure data handling operations: it supports transparent access to storage resources based on multiple technologies, and is easily expandable. Distributed Data management can be performed, also using third party services, and operations are resilient with respect to failures. DIRAC is highly customizable and can be easily extended. For these reasons, a vast and heterogeneous set of scientific collaborations have adopted DIRAC as the base for their computing models. Users from different experiments can interact with the system in different ways, depending on their specific tasks, expertise level and previous experience using command line tools, python APIs or Web Portals. The requirements of the diverse DIRAC user communities and hosting infrastructures triggered multiple developments to improve the system usability: examples include the adoption of industry standard authorization and authentication infrastructure solutions, the management of diverse computing resources (cloud, HPC, GPGPU, etc.), the handling of high-intensity work and data flows, but also advanced monitoring and accounting using no-SQL based solutions and message queues. This contribution will highlight DIRAC’s current, upcoming and planned capabilities and technologies.