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CMS Software and Computing: Ready for Run 2

In Run 1 of the Large Hadron Collider, software and computing was a strategic strength of the Compact Muon Solenoid experiment. The timely processing of data and simulation samples and the excellent performance of the reconstruction algorithms played an important role in the preparation of the full suite of searches used for the observation of the Higgs boson in 2012. In Run 2, the LHC will run at higher intensities and CMS will record data at a higher trigger rate. These new running conditions will provide new challenges for the software and computing systems. Over the two years of Long Shutdown 1, CMS has built upon the successes of Run 1 to improve the software and computing to meet these challenges. In this presentation we will describe the new features in software and computing that will once again put CMS in a position of physics leadership.

Joe Incandela, the spokesperson of the Compact Muon Solenoid (CMS) experiment, had a huge smile on his face at the press conference after his CERN seminar on July 4, 2012. Why? It was because CMS software and computing had enabled the discovery of the Higgs boson. He had just shown evidence for Higgs decays to five different final states. The analyses had used every drop of data available from the Large Hadron Collider (LHC), and had all the necessary Monte Carlo samples in place. This could not be said of the competitor experiment, which had access to greater computing resources [1]. But the competition has hardly stood still in the time since then, and neither could CMS. This presentation describes the changes that CMS made in its software and computing tools in during Long Shutdown 1, and how they have made CMS ready for Run 2.
Run 2 has brought us to a new energy domain, with √ s = 13 TeV, and even during 2015 the LHC is delivering enough integrated luminosity to do real physics with, and perhaps to make a discovery if Nature is kind. Thus, CMS had to be ready from the start to do all the computing necessary for the physics. But the computing requirements are substantially larger than those of Run 1. CMS will record data at a rate of 1 kHz, a factor of 2.5 greater than in Run 1, with larger pileup rates. To process the data without any improvements to the software would require an increase in CPU resources by a factor of six. Thus, new approaches were needed to address the computational challenges. The result is a system of increased agility and flexibility that will enable physics discovery, all built off the extremely successful systems of Run 1.
Clearly an important first step was to improve the performance of the reconstruction algorithms. As shown in Figure 1, CMS has reduced the event reconstruction time while maintaining physics performance, even in the more difficult event environment of Run 2. The most important improvements were in the track reconstruction, which takes the bulk of the processing time. Some improvements were strictly techncial, while others were algorithmic, such as changes to the tracking algorithms that reduced the number of fake tracks and sped execution. In addition, the time to simulate events was improved by reducing the time spent tracking low-energy particles in GEANT4.
Meanwhile, the best thing that the sites in the CMS distributed computing infrastructure -seven Tier-1 sites and approximately 50 Tier-2 sites -could do was to keep running, to maintain operational readiness. Throughout 2014, there were an average of 18,500 and 54,300 jobs running at the Tier-1 and Tier-2 sites, respectively. These jobs allowed CMS physicists to finish Run 1 data analyses, study detector configurations for HL-LHC upgrades, and start generating simulation samples for Run 2.
One of the goals of the CMS computing organization for Run 2 was to use these excellent facilities in more flexible and heterogeneous ways. This is illustrated in Figure 2, which shows which workflows were performed at which types of facilities in Run 1, and how they will be able to be performed at a greater variety of facilities in Run 2. The high-level trigger (HLT) farm, which is approximately the size of the Tier-0 facility, can now be used for organized processing during technical stops. Tier-2 centers have been commissioned to do reconstruction tasks that were previously limited to Tier-1 sites. Analysis jobs will now be permitted to run at Tier-1 sites, with mechanisms in place to ensure that desired datasets are resident on disk and not only on tape. The more places that work can run, the faster the work will go.
Many new services were deployed to support more agile operations. The "Any Data, Anytime, Anywhere" (AAA) global data federation allows CMS applications to read data efficiently over wide-area networks [2]. This relaxes constraints on the locations of datasets and workflows, and provides a great convenience to physicists who want to analyze data that isn't resident on their local computer. Disk-tape separation at Tier-1 sites gives operators greater control over what datasets are available on disk and, through AAA, allows Tier-1 data to be used in workflows anywhere. A new dynamic data management system performs automatic transfers of datasets upon their creation, and then deletes them when they are not needed, using "popularity" DPF%2015%-%CMS%Software%and%Computing:%Ready%for%Run%2 K.%Bloom,%6%August%2015 ▪ Use excellent facilities in more flexible, heterogeneous ways ▪ Use HLT (~size of T0) for organized processing during technical stops ▪ Commission T2's to do reconstruction tasks previously limited to T1 ▪ Allow analysis jobs to run at more sites ▪ The more places work can run, the faster the work goes!  data on file access rates. There is now a global job pool for resource provisioning through the glideinWMS system [3]. This single task queue for all jobs within the distributed computing infrastructure allows for central control of job priorities, and is simpler to manage than the multiple provisioning systems used in Run 1. It has been demonstrated to scale sufficiently well to operate all Tier-1, Tier-2 and opportunistic resources in a single pool. The glideinWMS system also gives the possibility of provisioning cloud infrastructures. This allows the use of the HLT and possible opportunistic and commercial cloud resources, and the ability to burst into extra resources when necessary. Finally, data access across the ocean is facilitated by the establishment of a 100 Gbps transatlantic network link that is provided by ESnet.
A number of advances within the CMS software have also provided new opportunities and efficiencies. The event-processing framework and the reconstruction code are now multi-threaded. The code can use several CPU cores concurrently to reconstruct multiple events simultaneously. This has several beneficial effects. There is less demand on the computing infrastructure in general, as processing can be done with fewer open files and fewer jobs. The time to process a luminosity block of data (the basic quantum of detector data) is reduced, which is needed for higher trigger rates. There is also a huge reduction in the memory required per CPU core with little efficiency loss in the throughput of event processing. The multi-threading enables the use of multi-core pilot jobs that have internal dynamic partitioning of resources for greater efficiency.
CMS has also provided better tools for physics users, making data analysis easier, more flexible and less resource-intensive. A new job submission tool, CRAB3, has many improved features over its predecessor. There are automatic job retries, better job tracking, and more reliable delivery of user output. CRAB3 has a thinner client layer, and more logic on the server side that allows for easier upgrades. The tool fully exploits the HTCondor [4] and glideinWMS systems, including overflowing jobs from busy to less-busy sites. The new miniAOD analysis data format is a mere 30 KB/event, one tenth the size of the current AOD format. It is designed to serve about 80% of analyses. A format this small makes it easier to keep more of the data disk-resident at desired locations. Once again, the AAA federation plays a role as user job location is no longer tied to data location. This is a major enabler for university-based data analyses, as smaller university groups are not always able to maintain large storage systems.
All of the improvements discussed are targeted for LHC Run 2, but CMS must always be preparing for the future. All software and computing systems have natural life cycles that require regular re-engineering, to continue coping with increasing LHC requirements, to adapt to changing technologies, and to improve usability and maintainability. Unlike detector upgrades, the deployment of new systems is not necessarily coupled to the LHC operation schedule, which means that they can have a different development and deployment cycles than instrumentation. A current hot project is the use of large-scale commercial cloud resources. The goal is to be able to rapidly expand resources for burst needs. CMS is working with Amazon Web Services on a demonstration project to increment the experiment's computing resources by about 50% for an expected fall reprocessing campaign. If such demonstrations are successful (a big "if"), it might become possible to only own the processing resources required for average demand rather than peak demand. Other ongoing projects include developing tools, environments and algorithms needed for new architectures, such as GPU's and low-energy clusters, and exploring the evolution of computing models and the scaling of tools for LHC Run 3.
How is the new software and computing system performing in the beginning of Run 2? There is not much data yet, so the system has not really been tested at scale, but everything has worked fine so far. The Tier-0 facility does prompt reconstruction of events, and physicists soon have the data in hand. The first 13 TeV physics paper, on dN/dη, has been submitted for publication [5]. Other preliminary results available at this time include a first measurement of the top-antitop production cross section [6] and a measurement of the dijet mass spectrum [7]. Obviously, much more excitement is expected, and CMS software and computing tools are needed to make all of these measurements possible.
While CMS software and computing were very successful in Run 1, the experiment could not -and did not -sit still during the long shutdown. Significant evolutionary changes to the Run 1 systems have taken advantage of technical developments that have led to more flexible and efficient resource usage and better tools for physics users. CMS is already showing significant success in the processing and analysis of Run 2 data. If Nature cooperates, CMS software and computing will have everyone smiling again.