Tackling tomorrow s computing challenges today at CERN. Maria Girone CERN openlab CTO

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2 Tackling tomorrow s computing challenges today at CERN CERN openlab CTO

3 CERN is the European Laboratory for Particle Physics. CERN openlab CTO

4 The laboratory straddles the Franco- Swiss border near Geneva.

Member states A world-wide endevour Associate

Cooperation agreements It has 22 member states

5 Member states A world-wide endevour Associate member states in the pre-stage to membership Associate member states Observers 22 members 8 associates 3 observers Budget (2017) 1100 MCHF Cooperation agreements It has 22 member states and supports a global community of 15,000 researchers.

6 Looking for Antimatter Understanding the very first moments of our Universe after the Big Bang Understanding Dark Matter These researchers are probing the fundamental structure of the Universe.

7 qadvance the frontiers of knowledge E.g. the secrets of the Big Bang what was the matter like within the first moments of the Universe s existence? qdevelop new technologies for accelerators and detectors Information technology - the Web and the GRID Medicine - diagnosis and therapy qtrain scientists and engineers of tomorrow qunite people from different countries and cultures CERN s mission: research, technology, education, and collaboration.

8 Tackling tomorrow s computing challenges today at CERN CERN openlab CTO

9 The LHC is the world s largest and most powerful particle accelerator.

10 CMS ALICE ATLAS LHCb The LHC is the world s largest and most powerful particle accelerator.

11 CMS ALICE ATLAS LHCb It is built around 100 m underground and has a circumference of 27 km.

12 CMS ALICE ATLAS LHCb The particles are accelerated to close to the speed of light.

13 The FASTEST RACETRACK on the Planet The Most Powerful MAGNETS The Most SOPHISTICATED DETECTORS ever built The Highest VACUUM HOTTEST spots in the galaxy COLDER TEMPERATURES than outer space The LHC is a machine of records!

14 What may come next.

15 Tackling tomorrow s computing challenges today at CERN CERN openlab CTO

16 The detectors are like gigantic digital cameras built in cathedral-sized caverns.

17 Experiments are run by collaborations of scientists from institutes all over the world.

ATLAS CMS 46m long, 25m diameter weights 7 000

km of cables 22m long, 15m diameter weights 14

18 ATLAS CMS 46m long, 25m diameter weights tonnes 100 million electronic channels, km of cables 22m long, 15m diameter weights tonnes Most powerful superconducting solenoid ever built Two general-purpose detectors cross-confirm discoveries, such as the Higgs boson.

Studies the behaviour difference between the b quark and the anti-b quark to

19 ALICE LHCb Studies the «Quark Gluon Plasma», state of matter which existed moments after the Big Bang. Studies the behaviour difference between the b quark and the anti-b quark to explain the matter-antimatter asymmetry in the Universe. ALICE and LHCb experiments have detectors specialised on studying specific phenomena.

20 Tackling tomorrow s computing challenges today at CERN CERN openlab CTO

21 Collisions generate particles that decay in complex ways into even more particles.

22 Up to about 1 billion particle collisions can take place every second.

23 Data generated 40 million times per second PB/s 100,000 selections per second TB/s 1,000 selections per second GB/s This can generate up to a petabyte of data per second. Filtering the data in real time, selecting potentially interesting events (trigger).

24 Tackling tomorrow s computing challenges today at CERN CERN openlab CTO

25 The CERN data centre processes hundreds of petabytes of data every year.

26 MEYRIN CENTRE (CH) 300,0000 processors cores 180 PB on disk 230 PB on tape WIGNER CENTRE (H) 100,0000 processors cores 100 PB on disk The two centres are connected by three 100 Gb/s fibre-optic links. CERN s data centre in Meyrin is the heart of the laboratory s computing infrastructure.

27 MEYRIN CENTRE (CH) 300,0000 processors cores 180 PB on disk 230 PB on tape WIGNER CENTRE (H) 100,0000 processors cores 100 PB on disk The two centres are connected by three 100 Gb/s fibre-optic links. The Wigner data centre in Budapest serves as an extension to the one in Meyrin.

28 Tackling tomorrow s computing challenges today at CERN CERN openlab CTO

29 Physicists must sift through the PBs produced annually by the LHC experiments.

30 ~40 MHz ~ PB/s Online Real time L1 Trigger (HW) ~100 khz HL Trigger (SW) ~1 khz WLCG Raw Data ~ 1-10 GB/s Offline - Asynchronous Physicists must sift through the PBs produced annually by the LHC experiments.

Tier-0 (CERN and Hungary): data recording, reconstruction and

Tier-2: Simulation, end-user analysis The Worldwide LHC Computing

storage resources into a single infrastructure accessible by all LHC

31 Tier-0 (CERN and Hungary): data recording, reconstruction and distribution Tier-1: permanent storage, re-processing, analysis Tier-2: Simulation, end-user analysis The Worldwide LHC Computing Grid integrates computer centres worldwide to combine computing and storage resources into a single infrastructure accessible by all LHC physicists The WLCG gives thousands of physicists across the globe near real-time access.

32 With 170 computing centres in 42 countries, the WLCG is the grid that never sleeps!

33 ~1M Cores CPU delivered ~170 sites, 42 countries ~1M CPU cores ~1EB of storage 3PB/day > 2 million jobs/day Gb/s links 340 Gb/s transatlantic 3PB moved per day The size of WLCG.

34 Making hundreds of petabytes of data accessible globally to scientists is one the biggest challenges of WLCG WLCG Compute Storage Compute cache Storage 1 to 10 Tb links Commercial Cloud Storage cache Compute HPC cache Data Organization, Management and Access in WLCG

35 Tackling tomorrow s computing challenges today at CERN CERN openlab CTO

36 The LHC has been designed to follow a carefully set out programme of upgrades.

37 RUN 3 ALICE & LHCb upgrades RUN 4 ATLAS & CMS upgrades The planned upgrades will greatly increase the scientific reach.

38 Rate of new physics is 1 event in Selecting a new physics event is like choosing 1 grain of sand in 20 volley ball courts More collisions help physicists to observe rare processes and study with greater precision.

39 CMS: event from 2017 with 78 reconstructed vertices CMS: event with 78 reconstructed vertices ATLAS: simulation for HL-LHC with 200 vertices The HL-LHC will come online around More collisions and more complex data.

LHCb and ALICE will move offline processing closer to the online data collection chain Performing processing and data analysis in near real-time Solutions under investigation New HLT farms

40 LHCb and ALICE will move offline processing closer to the online data collection chain Performing processing and data analysis in near real-time Solutions under investigation New HLT farms for Run3 Flexible and efficient system with ambitious PUE ratio Courtesy of Automation Data Center Facilities The ALICE and LHCb experiments will increase their data acceptance rates for Run 3.

41 By Run4, the detectors will become more granular and more radiation hard. PLACE Reconstructing more particles with more granular detectors will be computationally more expensive. The ATLAS and CMS experiments will be significantly upgraded for the HL-LHC.

42 CPU Resources [khs06*1000] ATLAS Preliminary Resource needs (2017 Computing model) Flat budget model (+20%/year) Run 2 Run 3 Run 4 Disk Storage [PBytes] ATLAS Preliminary Resource needs (2017 Computing model) Flat budget model (+15%/year) Run 2 Run 3 Run Year Year Using current techniques, required computing capacity increases times.

43 CPU Resources [khs06*1000] ATLAS Preliminary Resource needs (2017 Computing model) Flat budget model (+20%/year) Run 2 Run 3 Run 4 Disk Storage [PBytes] ATLAS Preliminary Resource needs (2017 Computing model) Flat budget model (+15%/year) Run 2 Run 3 Run Year Year Data storage needs are expected to be in the order of Exabytes by this time.

44 CPU Resources [khs06*1000] ATLAS Preliminary Resource needs (2017 Computing model) Flat budget model (+20%/year) Run 2 Run 3 Run 4 Disk Storage [PBytes] ATLAS Preliminary Factor 4 Factor 8 Resource needs (2017 Computing model) Flat budget model (+15%/year) Run 2 Run 3 Run Year Year It is vital to explore new technologies and methodologies.

45 Tackling tomorrow s computing challenges today at CERN CERN openlab CTO

Technology Evolution and Improvements Software innovation, New Architectures, Techniques and Methods Improvements in hardware performance

47 Technology Evolution and Improvements Software innovation, New Architectures, Techniques and Methods Improvements in hardware performance and capacity. Innovation and revolutionary thinking. Closing the resource gap in the next decade requires close collaboration with industry.

48 MANAGEMENT JOINT R&D INNOVATION & KNOWLEDGE TRANSFER COMMUNICATION EDUCATION CERN openlab is a unique science-industry partnership, fostering research and innovation.

Scale out capacity with public clouds, HPC, new architectures COMPUTING CHALLENGES Increase data centre performance with hardware accelerators (FPGAs,

49 Scale out capacity with public clouds, HPC, new architectures COMPUTING CHALLENGES Increase data centre performance with hardware accelerators (FPGAs, GPUs,..) optimized software New techniques with Machine Learning, Deep Learning, Advanced Data Analytics Three main areas of research and development.

50 Tackling tomorrow s computing challenges today at CERN CERN openlab CTO

51 Data centre technologies and infrastructures.

52 Faced with a resource gap of this magnitude: 1. fully exploit available hardware; 2. expand dynamically to new computing environments Data centre technologies and infrastructures.

CERN is one of the early adopters and largest

provided through a private cloud Allows for flexible and

even more flexibility Current investigations within CERN

Experiment Pilot Factories Public Cloud (LSF) Bare Metal

Services VMs OpenStack Resource Provisioning (>1

53 CERN is one of the early adopters and largest contributors to OpenStack 90% of the resources are provided through a private cloud Allows for flexible and dynamic deployment 320k cores Moving to containers for even more flexibility Current investigations within CERN openlab End Users Volunteer Computin g HTCondor Experiment Pilot Factories Public Cloud (LSF) Bare Metal and HPC CI/CD Containers APIs CLIs GUIs IT & Experiment Services VMs OpenStack Resource Provisioning (>1 physical data centre) Layered, virtualized services provide flexibility and efficiency.

54 300k Cores 80k Cores Experiments have demonstrated that it is possible to elastically and dynamically expand production resources to commercial clouds. Large-scale tests with commercial clouds.

55 Joint procurement of R&D cloud services for scientific research.

HPC are significant resources and are being tested by

applications Cores by processing type 500k MC simu

than 200k traditional x86 HPC cores for simulation

Smooth Tier-0 running on 23k cores All experiments are

CERN will partner with EU-PRACE optimizing the use of

56 HPC are significant resources and are being tested by the experiments Optimized for highly parallel applications Cores by processing type 500k MC simu Cores by resource type 500k HPC ATLAS reached more than 200k traditional x86 HPC cores for simulation workflows MC reco Grid Data Derivation HLT, Cloud Smooth Tier-0 running on 23k cores All experiments are exploring the use of heterogeneous HPC architectures CERN will partner with EU-PRACE optimizing the use of HPC resources for Demonstrations with large-scale, dedicated HPC resources, too.

57 DEEP-EST: Dynamical Exascale Entry Platform - Extreme Scale Technologies CERN is a partner of DEEP-EST, a blueprint project for heterogeneous HPC systems.

58 Tackling tomorrow s computing challenges today at CERN CERN openlab CTO

59 Computing Performance and Software Exploiting heterogeneous resources.

60 HEP has a vast investment in software Significant effort to make efficient multi-threaded and vectorized CPU code Accelerated computing devices (GPUs, FPGAs) offer a different model Complexity of heterogeneous architectures Simultaneously exploring lower performance but lower power alternatives like ARM C Leggett, LBNL Software optimization can gain factors in performance.

61 The landscape is shifting at all levels.

Higher data rates require more selective triggering and faster reconstruction LHCb is investigating FPGAs and GPUs to allow reconstruction of 5GB/s of events in real time.

62 Higher data rates require more selective triggering and faster reconstruction LHCb is investigating FPGAs and GPUs to allow reconstruction of 5GB/s of events in real time. CMS is porting heavy offline tasks to real-time processing for HL-LHC Integrate GPUs in the HLT farm to give highquality reconstruction in 100 msec latency (as opposed to tens of sec) 5 TB/s TB/s ~5 GB/s DETECTOR READOUT HLT1 PARTIAL RECO HLT2 FULL RECO 5% FULL 85% TURBO & real-time analysis 10% CALIB Exploiting co-processors for software-based filtering and real-time reconstruction.

63 CERN openlab is engaging in QC with industry Can substantially speed-up training of deep learning and combinatorial searches Well suited for fitting, minimization, optimization Can directly describe basic interactions as well as lattice QCD calculations Quantum Computing is also on the horizon.

64 Tackling tomorrow s computing challenges today at CERN CERN openlab CTO

65 Machine learning and advanced data analytics.

accelerators infrastructure health needs

VACUUM Quality of produced data needs to

66 Experiment and accelerator operations have similar challenges to industrial applications A multitude of Industrial Control Systems Detectors and accelerators infrastructure health needs to be monitored Cooling & Ventilation VACUUM Quality of produced data needs to be validated Resource usage needs to be optimized Cryogenics GAS Working with industry partners to deploy similar techniques and automation Electric Grid LHC Circuit, QPS, WIC, PIC, Monitoring, automation and anomaly detection.

With current software and computer an event

Examine the detector hit information and use 3D

objects Recognize physics objects from learned

increase the speed for reconstruction Exploring

67 With current software and computer an event like HL-LHC takes 10s of seconds IDENTIFICATION Examine the detector hit information and use 3D image recognition techniques to identify objects Recognize physics objects from learned patterns INPUT IMAGES Might dramatically increase the speed for reconstruction Exploring image recognition for reconstruction and object identification.

accuracy of simulated events One network attempts to simulate events that match a data distribution While a

68 Main R&D areas Adapting the existing code to new computing architectures Replacing complex algorithms with deeplearning approaches (FAST SIMULATION) Looking at adversarial networks to improve speed without giving up accuracy of simulated events One network attempts to simulate events that match a data distribution While a second network tries to distinguish data and simulation Simulation is one of the most resourceintensive computing applications.

69 Tackling tomorrow s computing challenges today at CERN CERN openlab CTO

70 CERN is collaborating with other communities who share similar computing challenges.

First phase will be operational in the mid 2020s; observatory will function for

71 The Square Kilometre Array (SKA) observatory s two telescopes will enable astronomers to study the sky in unprecedented detail. First phase will be operational in the mid 2020s; observatory will function for 50 years. South Africa Western Australia Joint exascale data-storage and processing challenge between HL-LHC and SKA.

Dosimetry Computing & simulation for health applications CDERN MEDICIS

72 CERN-MEDICIS: production of innovative isotopes for medical research Accelerator design for future hadron therapy facilities Medical imaging Dosimetry Computing & simulation for health applications CDERN MEDICIS BioDynamo Accelerating innovation and knowledge transfer to medical applications.

73 Tackling tomorrow s computing challenges today at CERN CERN openlab CTO

74 Tackling tomorrow s computing challenges today at CERN CERN has been pushing the boundaries of knowledge and technology for more than 60 years. The next phase of the programme will include unprecedented computing challenges. We look forward to tackling these challenges through open collaboration and innovation with industry and other scientific communities. CERN openlab CTO

75 Tackling tomorrow s computing challenges today at CERN «Magic is not happening at CERN, magic is being explained at CERN.» Tom Hanks Thank you! Credit for the slide layout to Andrew Purcell, CERN CERN openlab CTO

76 Tackling tomorrow s computing challenges today at CERN Home page: home.cern openlab.cern/ whitepaper Home page: openlab.cern/ education CERN openlab CTO

77 Tackling tomorrow s computing challenges today at CERN Backup Slides CERN openlab CTO

Introduction of NVRAM provides much faster

78 Introduction of NVRAM provides much faster access to data and applications in memory High Performance and long duration SSD improves the access on large datasets Low cost and high capacity SSD could revolutionize long term storage Larger spinning disk brings us to exabytes of storage Storage has many more layers and improvements in access.

79 Raw data: Was a detector element hit? How much energy? What time? Reconstructed data: Momentum of tracks (4- vectors) Origin of collision Energy in clusters (jets) Type of Particle Calibration information n 150 Million sensors deliver data 40 Million times per second The data at the LHC.

Preparing for High-Luminosity LHC. Bob Jones CERN Bob.Jones <at> cern.ch

Preparing for High-Luminosity LHC Bob Jones CERN Bob.Jones cern.ch The Mission of CERN Push back the frontiers of knowledge E.g. the secrets of the Big Bang what was the matter like within the first