Engineering topics

Belforte, S.

Publications and source records attributed to Belforte, S..

6 records

DOE OSTI2017

A comparison of different database technologies for the CMS AsyncStageOut transfer database

AsyncStageOut (ASO) is the component of the CMS distributed data analysis system (CRAB) that manages users transfers in a centrally controlled way using the File Transfer System (FTS3) at CERN. It addresses a major weakness of the previous, decentralized model, namely that the transfer of the user’s output data to a single remote site was part of the job execution, resulting in inefficient use of job slots and an unacceptable failure rate. Currently ASO manages up to 600k files of various sizes per day from more than 500 users per month, spread over more than 100 sites. ASO uses a NoSQL database (CouchDB) as internal bookkeeping and as way to communicate with other CRAB components. Since ASO/CRAB were put in production in 2014, the number of transfers constantly increased up to a point where the pressure to the central CouchDB instance became critical, creating new challenges for the system scalability, performance, and monitoring. This forced a re-engineering of the ASO application to increase its scalability and lowering its operational effort. In this contribution we present a comparison of the performance of the current NoSQL implementation and a new SQL implementation, and how their different strengths and features influenced the design choices and operational experience. We also discuss other architectural changes introduced in the system to handle the increasing load and latency in delivering output to the user.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2017

Use of DAGMan in CRAB3 to improve the splitting of CMS user jobs

CRAB3 is a workload management tool used by CMS physicists to analyze data acquired by the Compact Muon Solenoid (CMS) detector at the CERN Large Hadron Collider (LHC). Research in high energy physics often requires the analysis of large collections of files, referred to as datasets. The task is divided into jobs that are distributed among a large collection of worker nodes throughout the Worldwide LHC Computing Grid (WLCG). Splitting a large analysis task into optimally sized jobs is critical to efficient use of distributed computing resources. Jobs that are too big will have excessive runtimes and will not distribute the work across all of the available nodes. However, splitting the project into a large number of very small jobs is also inefficient, as each job creates additional overhead which increases load on infrastructure resources. Currently this splitting is done manually, using parameters provided by the user. However the resources needed for each job are difficult to predict because of frequent variations in the performance of the user code and the content of the input dataset. As a result, dividing a task into jobs by hand is difficult and often suboptimal. In this work we present a new feature called “automatic splitting” which removes the need for users to manually specify job splitting parameters. We discuss how HTCondor DAGMan can be used to build dynamic Directed Acyclic Graphs (DAGs) to optimize the performance of large CMS analysis jobs on the Grid. We use DAGMan to dynamically generate interconnected DAGs that estimate the processing time the user code will require to analyze each event. This is used to calculate an estimate of the total processing time per job, and a set of analysis jobs are run using this estimate as a specified time limit. Some jobs may not finish within the alloted time; they are terminated at the time limit, and the unfinished data is regrouped into smaller jobs and resubmitted.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2017

Efficient monitoring of CRAB jobs at CMS

CRAB is a tool used for distributed analysis of CMS data. Users can submit sets of jobs with similar requirements (tasks) with a single request. CRAB uses a client-server architecture, where a lightweight client, a server, and ancillary services work together and are maintained by CMS operators at CERN. As with most complex software, good monitoring tools are crucial for efficient use and longterm maintainability. This work gives an overview of the monitoring tools developed to ensure the CRAB server and infrastructure are functional, help operators debug user problems, and minimize overhead and operating cost. This work also illustrates the design choices and gives a report on our experience with the tools we developed and the external ones we used.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2015

Pushing HTCondor and glideinWMS to 200K+ Jobs in a Global Pool for CMS before Run 2

The CMS experiment at the LHC relies on HTCondor and glideinWMS as its primary batch and pilot-based Grid provisioning system. So far we have been running several independent resource pools, but we are working on unifying them all to reduce the operational load and more effectively share resources between various activities in CMS. The major challenge of this unification activity is scale. The combined pool size is expected to reach 200K job slots, which is significantly bigger than any other multi-user HTCondor based system currently in production. To get there we have studied scaling limitations in our existing pools, the biggest of which tops out at about 70K slots, providing valuable feedback to the development communities, who have responded by delivering improvements which have helped us reach higher and higher scales with more stability. We have also worked on improving the organization and support model for this critical service during Run 2 of the LHC. This contribution will present the results of the scale testing and experiences from the first months of running the Global Pool.

Balcas, J.↗

DOE OSTI2015

Using the GlideinWMS System as a Common Resource Provisioning Layer in CMS

CMS will require access to more than 125k processor cores for the beginning of Run 2 in 2015 to carry out its ambitious physics program with more and higher complexity events. During Run1 these resources were predominantly provided by a mix of grid sites and local batch resources. During the long shut down cloud infrastructures, diverse opportunistic resources and HPC supercomputing centers were made available to CMS, which further complicated the operations of the submission infrastructure. In this presentation we will discuss the CMS effort to adopt and deploy the glideinWMS system as a common resource provisioning layer to grid, cloud, local batch, and opportunistic resources and sites. We will address the challenges associated with integrating the various types of resources, the efficiency gains and simplifications associated with using a common resource provisioning layer, and discuss the solutions found. We will finish with an outlook of future plans for how CMS is moving forward on resource provisioning for more heterogenous architectures and services.

Balcas, J.↗

DOE OSTI2015

CMS distributed data analysis with CRAB3

The CMS Remote Analysis Builder (CRAB) is a distributed workflow management tool which facilitates analysis tasks by isolating users from the technical details of the Grid infrastructure. Throughout LHC Run 1, CRAB has been successfully employed by an average of 350 distinct users each week executing about 200,000 jobs per day.CRAB has been significantly upgraded in order to face the new challenges posed by LHC Run 2. Components of the new system include 1) a lightweight client, 2) a central primary server which communicates with the clients through a REST interface, 3) secondary servers which manage user analysis tasks and submit jobs to the CMS resource provisioning system, and 4) a central service to asynchronously move user data from temporary storage in the execution site to the desired storage location. Furthermore, the new system improves the robustness, scalability and sustainability of the service.Here we provide an overview of the new system, operation, and user support, report on its current status, and identify lessons learned from the commissioning phase and production roll-out.

97 MATHEMATICS AND COMPUTING↗