Computing Challenges in the Years to Come - Andreas Petzold, Achim Streit (Steinbuch Centre for Computing, GridKa) + many contributors - DESY Indico
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Computing Challenges in the Years to Come
Andreas Petzold, Achim Streit (Steinbuch Centre for Computing, GridKa)
+ many contributors
KIT – The Research University in the Helmholtz Association www.kit.edu
HL-LHC Computing Challenges
Source: S. Campana, Status of WLCG, 38th WLCG RRB, 27.10.2020, https://indico.cern.ch/event/957310/timetable/
2 20.11.2020 Achim Streit – Computing Challenges of the Coming Years Steinbuch Centre for Computing, GridKa
Addressing HL-LHC Computing Challenges
with a Multi-Pronged Approach
Challenge: higher data rates Experiments have to
require more storage and CPU Improve SW design & algorithms
Adapt to new methods (AI) & HW (GPUs)
Improve computing models (less copies &
special data formats)
Innovation and R&D at GridKa
Federated storage (exascale data lakes)
Optimizations for inc. data accesses
Integration of special resources (GPUs)
Access to opp. resources (HPC,cloud)
Still – additional hardware is needed
Source: ATLAS Experiment – Public Results,
https://twiki.cern.ch/twiki/bin/view/AtlasPublic/ComputingandSoftwarePublicResults
3 20.11.2020 Achim Streit – Computing Challenges of the Coming Years Steinbuch Centre for Computing, GridKa
Performance Development
2.4 EFlop/s
442 PFlop/s
1.3 PFlop/s
Fugaku @ Riken (7.6 M cores, 5.1 PB mem, 30 MW)
Summit
Taihu Light
Tianhe-2
K Computer
Earth Simulator
Source: https://top500.org/statistics/perfdevel/
4 20.11.2020 Achim Streit – Computing Challenges of the Coming Years Steinbuch Centre for Computing, GridKa
Chip Statistics
System Share
Source: Erich Strohmaier, slide 30, https://www.top500.org/media/filer_public/54/77/5477d858-1f1e-410b-994b-b7122cfd1d57/top500_2020_06_v2_web.pdf
5 20.11.2020 Achim Streit – Computing Challenges of the Coming Years Steinbuch Centre for Computing, GridKa
Chip Statistics
Performance Share
Fugaku
Source: Erich Strohmaier, slide 31, https://www.top500.org/media/filer_public/54/77/5477d858-1f1e-410b-994b-b7122cfd1d57/top500_2020_06_v2_web.pdf
6 20.11.2020 Achim Streit – Computing Challenges of the Coming Years Steinbuch Centre for Computing, GridKa
Personal View – a Paradigm Change is needed
Adapt to present and future hardware architectures (SIMD instructions,
multi-/many-core, accelerators/GPUs, distributed memory)
Apply computer science principles and algorithms
Apply continuous integration/development/testing (CI/CD/CT)
Consider software as a research infrastructure in itself
Do Research Software Engineering (RSE)
engineering
software
at the intersection of algorithms/numerics, RSE
software engineering, and community codes
More physics computing professorships
7 20.11.2020 Achim Streit – Computing Challenges of the Coming Years Steinbuch Centre for Computing, GridKa
Personal View – How to achieve this?
Many parts of software have to be rewritten to profit from modern HW
Use abstraction layers like Alpaka, OpenMP, etc. to ensure portability
(GPUs/CPUs/…) and easy access to future architectures
Probably not 100% performance, but good investment in sustainable code
“The optimization/transformation process of software for GPUs also results in
much more efficient code on CPUs” (V. Lindenstruth, 9th CERN SCF, slide 11)
more
Computing centralization vs. distributed resources? centralized
Likely model: Centralized base resources (storage heavy & and at the
network) and services + interfaces for same time
a) permanent resources accessible through central site and more
b) integration of opportunistic/dynamic (computing) resources distributed
8 20.11.2020 Achim Streit – Computing Challenges of the Coming Years Steinbuch Centre for Computing, GridKa
Personal View – Future Computing Resources
Compute
Heterogeneous architectures
Be able to run jobs almost everywhere
Some software profits from adaption to
specific resources for peak performance
Storage
Permanent storage no longer at all sites
Large fast storage at few sites with very good network connectivity
Increasing tape usage with very well orchestrated data access
Cache storage with low operations requirements
Especially important at sites w/o direct internet access from WNs
Source: WLCG Data Lake, Simone Campana, https://indico.cern.ch/event/738796/contributions/3174573/attachments/1755785/2846671/DataLake-ATCF.pdf
9 20.11.2020 Achim Streit – Computing Challenges of the Coming Years Steinbuch Centre for Computing, GridKa
Innovation and R&D at KIT – Examples
Scalable online storage technology:
GridKa as an island in the data lake throughput, IOPs, capacity
requires massive scalability of storage
and network infrastructure
Software defined online storage to
address less predictable, more remote,
more diverse data access
Powerful networks (internal, external)
Reliable offline storage
2017 2018 2019/20 2021/22
Excellent performance and reliability upgrade upgrade upgrade
https://s.kit.edu/gridka-monitoring 23 PB 35 PB 43 PB 60 PB
70 GB/s 100 GB/s 120 GB/s
https://s.kit.edu/gridka-numbers
10 20.11.2020 Achim Streit – Computing Challenges of the Coming Years Steinbuch Centre for Computing, GridKaRecent GridKa Storage Extension
+ 36h
40 5U Seagate x5u85
• 84 16TB HDDs
• dual controller
12 protocol servers
200 GB IB Switches
400/100/40G Eth
11 11/20/2020 Achim Streit – Computing Challenges of the Coming Years Steinbuch Centre for Computing, GridKaInnovation and R&D at KIT – Examples
Deep integration of GPU nodes in GridKa farm accessible via GridKa CEs
3 nodes á dual AMD EPYC 7662 64 core, 1TB RAM, 8 Nvidia V100S 32GB
COBalD/TARDIS enabling a “regional resource pool”
HPC-systems ForHLR II & HoreKA (~17 Pflop/s) @ KIT
and in Bonn, Freiburg, Munich, …
Tier-2/3 WLCG systems in Aachen, Bonn, Karlsruhe, …
Grid CE
Grid CE
Grid CE
12 20.11.2020 Achim Streit – Computing Challenges of the Coming Years Steinbuch Centre for Computing, GridKaThe PUNCH4NFDI Consortium Spokesperson: Thomas Schörner (thomas.schoerner@desy.de) DESY, Notkestr. 85, D-22607 Hamburg Contact: Mail: punch4nfdi@desy.de Web: www.punch4nfdi.de Twitter: #punch4nfdi
*Particles, Universe, NuClei & Hadrons for the NFDI
PUNCH4NFDI* in one Slide
A consortium for the NFDi
PUNCH4NFDI
Represents (astro)particle, astro,
hadron & nuclear physics in the NFDI.
Specific strengths: big data and open Broad community representation: > 40 partner institutes
data; ready to take leading role in NFDI.
Our offer: Task areas:
A layered model of data management with Data management
scalability that allows for easy FAIRification Data transformations
Numerous services to develop community- Data portal
specific approaches in this direction Data irreversiblity
The PUNCH science data platform evolving Synergies&services, Teaching&outreach
ASTRO
around advanced research products
@NFDI
Timeline and general situation:
Services:
Evolving around research
9 consortia (out of 30 max in NFDI) funded in products and their dynamic
first NFDI round – none from physics. Now competing e.g. with life cycle
FAIRMat, DAPHNE4NFDI Connecting to entire NFDI
Submission of proposal 30 Sep 2020; evaluation by review panel 10 – a cornerstone of
Dec 2020; grants by July 2021; funding start 1 Oct 2021 research data
management in D. Page 14Autor der Folien: 15 20.11.2020 Achim Streit – Computing Challenges of the Coming Years Steinbuch Centre for Computing, GridKa Gregor Kasieczka, U-Hamburg
16 20.11.2020 Achim Streit – Computing Challenges of the Coming Years Steinbuch Centre for Computing, GridKa
Autor
17 der 20.11.2020
Folien: Achim Streit – Computing Challenges of the Coming Years Steinbuch Centre for Computing, GridKa
Alexander Schmidt, RWTH18 Achim Streit – Computing Challenges of the Coming Years Steinbuch Centre for Computing, GridKa
Neuer Verbund im Bereich Computing
für Run-3 bei ATLAS und CMS
Ziel: Zuverlässiger Betrieb der zu erweiternden
Computing-Infrastruktur in Run-3 für die
ATLAS- und CMS-Experimente in Deutschland
essentiell für den Gesamterfolg der Forschung
durch Physiker*innen an deutschen Instituten
Run-3 des LHC (2022 bis 2024):
pp-Datensatz wächst um Faktor ~ 2,5
Bereitstellung und Betrieb zusätzlicher Ressourcen
Anforderungen 2024 ~ 1,5 * Hardware in 2021
(plus signifikanter Ersatz alter Hardware)
Weiterentwicklung und Optimierung des Betriebs
(Datenmanagement, Job-Scheduling, Monitoring, ...)
für sich ändernde Computingmodelle
Autor der Folien: Markus Schumacher, U-FreiburgNeuer Verbund im Bereich Computing
für Run-3 bei ATLAS und CMS
Run-3 (2022-2024): im Wesentlichen erfolgreiches Modell aus Run-1 und Run-2
HL-LHC (ab 2028): Übergang muss aber bereits in 2024 eingeleitet werden
In 2023 beschaffte Hardware, letzte die nicht in Run-4 genutzt wird
Neue Computing-Modelle im effizienten Dauerbetrieb spätestens ab 2028
Nutzung neuer Ressourcen z.B. HPC-Cluster, GPUs, opportunistische Ressourcen, ...
Neues Konzept für Datenspeicherung: wenige Standorte vor allem HGF-Zentren,
im Rahmen des Konzepts der Data Lakes u. schnelle Caches an Analysezentren (Unis)
Vorlauf von 4 Jahren notwendig für Test des Betriebs, Aufbau der Hardware, Finanzierung,...
Stärkere Rolle der HGF-Zentren zusätzliche Finanzmittel jenseits normaler Förderung
Neuer experimentübergreifende Verbund:
noch stärkere Vernetzung (jenseits GridKa-OB, GridKa-TAB, Terascale-Computing-Board)
gemeinsame Diskussion und Strategie für homogenes Betriebsmodell für HL-LHCConclusion
Challenges ahead cannot be solved with hardware investments alone
(but neither can they be solved without hardware investments)
HPC, cloud, data lake… is not enough…
Aggressive R&D = better exp. software
Created with computer science expertise
Using modern programming paradigms
Being efficient and scalable
chance for university groups?
Source: ATLAS Experiment – Public Results,
https://twiki.cern.ch/twiki/bin/view/AtlasPublic/ComputingandSoftwarePublicResults
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