1. FUTURE ICT CHALLENGES IN SCIENTIFIC COMPUTING
White Paper 2017
FUTURE ICT CHALLENGES IN SCIENTIFIC COMPUTING
Maria Girone, CERN
on behalf of all the contributors to the white paper
CERN openlab Open Day
September 2017

2. Maria Girone, CERN openlab CTO
A public-private partnership between the research community and industry
CERN openlab V Collaboration
Phase V
Maria Girone, CERN openlab CTO

3. THE LHC UPGRADE PROGRAMME
Identify challenge areas and R&D topics for the next phase of CERN openlab
RUN3: LHCb and ALICE upgrades
RUN4: ATLAS and CMS upgrades
RUN3 and RUN4 will present important ICT challenges
Multi-exabyte per year data flows, CPU needs of 20M today’s cores
About a factor 10 above what technology evolution will bring
Constrained budgets
CPU:
x60 from 2016
Data:
Raw 2016: 50 PB  2027: 600 PB
Derived (1 copy): 2016: 80 PB  2027: 900 PB
ECFA2016
Maria Girone, CERN openlab CTO

4. Maria Girone, CERN openlab CTO
EVENTS AT THE HL-LHC
Increased complexity due to much higher pile-up and higher trigger rates will bring several challenges to reconstruction algorithms
Average pile-up 20
Average pile-up 200
Maria Girone, CERN openlab CTO

5. FACING THE HL-LHC COMPUTING CHALLENGES
HL-LHC will pose many challenges to software and computing. Collaborating with industry is key
High trigger rates and complex events present challenges for experimental algorithms
New industry tools like image recognition or machine learning for classification may have a big impact
The infrastructure needs to evolve to handle the much higher data rates
New architectures, co-processors, FPGAs, GPUs are all candidates
Software performance will be key
Modern coding, parallelization and vectorization
Courtesy of I. Bird
Maria Girone, CERN openlab CTO

6. WHITE PAPER: PROCESS AND TOPICS
A series of workshops and discussions was held to capture the challenges faced by the LHC to set out collaborative R&D projects
A solid starting point for a very challenging and constructive Phase VI
R&D topics
DATA-CENTRE TECHNOLOGIES AND INFRASTRUCTURES
COMPUTING PERFORMANCE AND SOFTWARE
MACHINE LEARNING AND DATA ANALYTICS
APPLICATIONS IN OTHER DISCIPLINES
Set out to be a community process from brainstorming to white paper and plans
We invite industry to pick up some of these challenges and work with us
Maria Girone, CERN openlab CTO

7. DATA-CENTRE TECHNOLOGIES AND INFRASTRUCTURES R&D
Maria Girone, CERN openlab CTO

8. DATA-CENTRE TECHNOLOGIES AND INFRASTRUCTURES R&D
CERN is evaluating different models for increasing computing and data storage capacity to rapidly adapt to diverse workflow types
Alternative architectures and specialized capabilities
Heterogeneity and elasticity are key concepts
Designing and operating distributed data infrastructures and computing centres poses several challenges, becoming greater as scale increases
Networking
DC Architectures
Storage
Databases
Clouds
Should cern decide on a DC, ambitious goals such PUE <1.1, a facility eventually reaching 15 MW
Maria Girone, CERN openlab CTO

9. Maria Girone, CERN openlab CTO
CHALLENGE AREAS (1)
Data Centre Technologies and Infrastructures R&D
Networking
High-bandwidth links from detectors to the Data Centre
would open opportunities for a consolidated CERN Data Centre with the experiments online systems
Affordable high performance & low-latency interconnects
blurring boundaries between HTC and HPC
Software-defined networking and separation out of the data plane and the management plane
Improvements in data management and data access
Automation of network configuration
Data-Centre Architectures
Investigating rack disaggregation and software defined infrastructures
enabling rapid allocation of the optimal storage and computing resources, including memory and bandwidth
Investigating data access with hierarchical storage buffers, including volatile/non-volatile memory and high-bandwidth solid-state storage drives
Maria Girone, CERN openlab CTO

10. Maria Girone, CERN openlab CTO
CHALLENGE AREAS (2)
Data Centre Technologies and Infrastructures R&D
Data Storage
Investigation of models for expansion of storage capacity
Different media with multiple speeds and quality of service
‘Cold storage’ evolution for long term archival
Evaluating low-cost flash memory, coupled with increased storage bandwidth and connectivity
Database Technologies
Traditional database applications will continue to play a key role in the HL-LHC
High ingest rates of data, time-series workloads, high write- and read-throughputs
Scale-out databases and cloud resources
Evaluating new approaches, such as PaaS cloud-based architectures for services like databases and data-analytics
Evaluating alternative solutions out of the traditional hierarchical mass-storage systems used so far by the LHC experiments (spinning disks and tape-systems as active archival)
Maria Girone, CERN openlab CTO

11. Maria Girone, CERN openlab CTO
CHALLENGE AREAS (3)
Data Centre Technologies and Infrastructures R&D
Cloud infrastructures
Orchestration and automation of compute provisioning
Investigations on how rack disaggregation and software-defined infrastructure could be used to provide optimized configurations
Scalable clouds and global scientific clouds
Improve efficiency, better capacity planning, optimising the infrastructure for different usage patterns
Economy of scale via coordinated cloud initiatives across different sciences
Maria Girone, CERN openlab CTO

12. Maria Girone, CERN openlab CTO
Computing Performance and Software R&D
Maria Girone, CERN openlab CTO

13. COMPUTING PERFORMANCE AND SOFTWARE R&D
Modernising code plays/will play a vital role for the upgrades of experiments and LHC
Increase software performance by adopting modern coding techniques and tools
Fully exploit the features offered by modern hardware architectures
Many-core GPU platforms, acceleration co-processors and innovative hybrid combinations of CPUs and FPGAs
Maria Girone, CERN openlab CTO

14. Maria Girone, CERN openlab CTO
CHALLENGE AREAS
Computing Performance and Software R&D
Dedicated hardware and co-processing systems
Evaluating the adoption of dedicated specialized high-performance co-processors, GPUs, FPGAs and many-core co-processors to increase performance for specific sections of applications
Code Modernisation
Growing core count per CPU and the desire to take advantage of HPC centres are driving a push towards more parallel code
Major progress on both simulation (e.,g. GeantV) and reconstruction algorithms
Evaluate impact of low-latency and high-bandwidth NVRAM technology
Heterogeneous Platforms and Alternative Architectures
Investigations to better utilize resources (power, virtualized resources, …)
Optimizing code distribution using lightweight containers
Maria Girone, CERN openlab CTO

15. Maria Girone, CERN openlab CTO
Machine Learning and Data Analytics R&D
Courtesy of I. Fisk
Simons Foundation
Global Brain Project
Maria Girone, CERN openlab CTO

16. MACHINE LEARNING AND DATA ANALYTICS R&D
ML is important in HEP for analysis and expertise is well established
Industry has demonstrated the utility of machine learning techniques for monitoring, automation, anomaly detection of complex systems, visualization, …
Investigating how to benefit from industry adopted machine learning and “Big data” analytics techniques in many aspects of
Data Acquisition
Data Processing and Simulation
Data Engineering
Maria Girone, CERN openlab CTO

17. Maria Girone, CERN openlab CTO
CHALLENGE AREAS (1)
Machine Learning and Data Analytics R&D
Data Acquisition
Monitoring of accelerators and detectors
Detecting anomalous behaviour in real-time
Monitoring data quality
based on anomaly detection techniques to predict failures by monitoring patterns
Anomaly detection and the search for new physics
Searches for new phenomena that are different from those commonly accepted by the trigger system using semi supervised /supervised algorithms
Fast inference technology for trigger systems
investigating deep learning for real-time event classification at the level of the trigger
Maria Girone, CERN openlab CTO

18. Maria Girone, CERN openlab CTO
CHALLENGE AREAS (2)
Machine Learning and Data Analytics R&D
Data Processing
Simulation plays a key role in all HEP. At HL-LHC, the need of simulated data will increase enormously
It is essential to improve significantly performance and optimize existing code
Evaluating the possibility of replacing complex algorithms with deep-learning algorithms, including use of computer vision techniques, such are generative adversial networks
Jet identification and image-based event classification
to distinguish physics objects based on topology using computing vision techniques
Maria Girone, CERN openlab CTO

19. Maria Girone, CERN openlab CTO
CHALLENGE AREAS (3)
Machine Learning and Data Analytics R&D
Big Data
Data reduction and refresh for analysis
resource-heavy workflows – investigating the use of Spark and Hadoop for a more efficient use of resources through re-use of selection criteria and customized partial reconstruction
Optimisation of Computing Infrastructure
By applying statistical and machine learning methods to large sets of metrics collected from system components
CPU and batch, disk and archive storage, network topology and flows, and application throughput
Data engineering and solutions from industry
Data analytics platforms such as Apache Hadoop, Spark, Kafka, Hbase and Kudu have now large user communities
Being adopted by HEP
Streaming analysis being investigated
Maria Girone, CERN openlab CTO

20. Applications in Other Disciplines R&D
Credit: Mark Ellisman, National Center for Microscopy and Imaging Research (NCMIR) (Attribution-NonCommercial 2.0 Generic)
Maria Girone, CERN openlab CTO

21. APPLICATIONS IN OTHER DISCIPLINES R&D
Sharing the use of techniques developed in HEP with other sciences
Astrophysics
Exascale data processing at future astrophysics infrastructures, such as SKA
Platforms for Open Collaborations
A smart data-analysis platform
Life Sciences and Medical applications
Simulating biological systems in the Cloud
Large-scale analysis of genomic data and healthcare data
Smart Everything
Environmental monitoring
Traffic and Mobility
Maria Girone, CERN openlab CTO

22. Maria Girone, CERN openlab CTO
CONCLUSIONS
The CERN openlab white paper was targeted specifically with solving the challenges facing the HL-LHC
16 challenge areas and many use-cases grouped into 4 different R&D topics
Continued goal of CERN openlab is collaboration with industry to explore ICT solutions for the benefit of science, industry and society
Supporting the challenging HL-LHC programme
CERN openlab is entering phase VI, which indicates the strength and longevity of the process and the value of a continued collaboration
The white paper is a solid starting point for a very challenging and constructive Phase VI
Opportunities for mutually beneficial collaborations
Maria Girone, CERN openlab CTO

23. Maria Girone, CERN openlab CTO
ANY QUESTIONS?
Maria Girone, CERN openlab CTO