Dynamic Data Center concept

Dynamically Creating Big Data
Centers for the LHC
Frank Würthwein
Professor of Physics
University of California San Diego
September 25th, 2013

Outline
•  The Science
•  Software & Computing Challenges
•  Present Solutions
•  Future Solutions
September 25th 2013 Frank Wurthwein - ISC Big Data 2

~67% of energy is dark energy
~29% of matter is dark matter
All of what we know makes up
Only about 4% of the universe.
We have some ideas but no
proof of what this is!
We got no clue what this is.
The Universe is a strange place!

To study Dark
Matter we need to
create it in the
laboratory
Mont Blanc
Lake Geneva
ALICE
ATLAS
LHCb
CMS

Big bang in the laboratory
•  We gain insight by colliding particles at the highest
energies possible to measure:
–  Production rates
–  Masses & lifetimes
–  Decay rates
•  From this we derive the spectroscopy as well as the
dynamics of elementary particles.
•  Progress is made by going to higher energies and
brighter beams.

Explore Nature over 15 Orders of magnitude
Perfect agreement between Theory & Experiment
[GeV/c]T
Jet p
30 40 100 200 1000 2000
GeV/c
pb
dy
T
dp
σ2
d
-5
10
-3
10
-1
10
10
3
10
5
10
7
10
9
10
11
10
13
10
= 8 TeV CMS Preliminaryspp
21
(low PU runs)-1
= 5.8 pbint
open: L
(high PU runs)-1
= 10.71 fbint
filled: L
NP⊗NNPDF 2.1 NLO
)
5
10×0.0 <|y|< 0.5 (
)4
10×0.5 <|y|< 1.0 (
)
3
10×1.0 <|y|< 1.5 (
)2
10×1.5 <|y|< 2.0 (
)1
10×2.0 <|y|< 2.5 (
)
0
10×2.5 <|y|< 3.0 (
)-1
10×3.2 <|y|< 4.7 (
)
5
10×0.0 <|y|< 0.5 (
)4
10×0.5 <|y|< 1.0 (
)
3
10×1.0 <|y|< 1.5 (
)2
10×1.5 <|y|< 2.0 (
)1
10×2.0 <|y|< 2.5 (
)
0
10×2.5 <|y|< 3.0 (
)-1
10×3.2 <|y|< 4.7 (
Dark Matter expected
somewhere below this line.

And for the Sci-Fi Buffs …
Imagine our 3D world to be
confined to a 3D surface in
a 4D universe.
Imagine this surface to be
curved such that the 4th D
distance is short for locations
light years away in 3D.
Imagine space travel by
tunneling through the 4th D.
The LHC is searching for evidence of a 4th dimension of space.

Recap so far …
•  The beams cross in the ATLAS and CMS
detectors at a rate of 20MHz
•  Each crossing contains ~10 collisions
•  We are looking for rare events that are
expected to occur in roughly
1/10000000000000 collisions, or less.

Software & Computing
Challenges

The CMS Experiment
•  80 Million electronic channels
x 4 bytes
x 40MHz
-----------------------
~ 10 Petabytes/sec of information
x 1/1000 zero-suppression
x 1/100,000 online event filtering
------------------------
~ 100-1000 Megabytes/sec raw data to tape
1 to 10 Petabytes of raw data per year
written to tape, not counting simulations.
•  2000 Scientists (1200 Ph.D. in physics)
–  ~ 180 Institutions
–  ~ 40 countries
•  12,500 tons, 21m long, 16m diameter

Active Scientists in CMS
agreement; the Tier-2 CPU pledge is explicitly shown (orange line). It should be noted
that the utilization curve (in red) does not include the CERN contribution (used as
analysis facility since LS1). The slight deficit in pledge utilization at Tier-2 centers since
in the first half of 2013 are mainly due to the lack of simulation requests, yet the level of
Tier-2 usage for data analysis stayed high after the end of LHC Run 1.
The Tier-2 sites continue to be very successfully used for analysis and have been the
primary analysis resource. The number of individual submitters per week submitting
jobs to the Tier-2 sites with the CMS CRAB tool is shown in Figure 10. The main dips
are explained by the CERN Christmas breaks, while the main peaks appear during
preparation periods for summer and winter conferences.
Figure 10: Individual analysis submitters per week to the grid from Sep. 2009 to Aug. 2013.
The average total number of individual submitters per month since the begging of 2013
reaches 540, which means in a typical 30-day period days around 18% of the
collaboration has submitted a grid job. This is only a 10% decrease in number of active
users compared to the LHC running period, showing the user activity on the distributed
GRID facilities is largely decoupled from the actual data taking.
The average number of job slots used at Tier-2 sites since the beginning of 2013 was of
37 K, as shown on the left hand side of Figure 11. This is a 12% increase compared to
2012, however given the 23% pledge increase at the same time, the overall pledge
utilization so far in 2013 has been smaller than in 2012, as confirmed by Figure 8. The
right hand side of Figure 11 shows the completed analysis jobs at Tier-2 in the first half
of 2013, with a measured average of ~1.4 M jobs per week (200 K jobs per day).
8
5-40% of the scientific
members are actively doing
large scale data analysis in
any given week.
~1/4 of the collaboration,
scientists and engineers,
contributed to the common
source code of ~3.6M C++ SLOC.

Evolution of LHC Science Program
150Hz 1000Hz 10000Hz
Event Rate written to tape
LHC Roadmap
September 3, 2013.
Lint~75-100 fb-1
• Physics case
• Upgrade detector design

The Challenge
How do we organize the processing of 10 s to 1000 s of
Petabytes of data by a globally distributed community
of scientists, and do so with manageable change costs
for the next 20 years ?
Guiding Principles for Solutions
Chose technical solutions that allow
computing resources as distributed as human resources.
Support distributed ownership and control,
within a global single sign-on security context.
Design for heterogeneity and adaptability.

Federation of National Infrastructures. In the U.S.A.: Open Science Grid

Among the top 500 supercomputers
there are only two that are bigger when
measured by power consumption.

Tier-3 Centers
•  Locally controlled resources not pledged to any of
the 4 collaborations.
–  Large clusters at major research Universities that are time
shared.
–  Small clusters inside departments and individual research
groups.
•  Requires global sign-on system to be open for
dynamically adding resources.
–  Easy to support APIs
–  Easy to work around unsupported APIs

Me -- My friends -- The grid/cloud
O(104) Users
O(102-3) Sites
O(101-2) VOs
Thin client
Thin Grid API
Thick VO
Middleware
& Support
Me
My friends
The anonymous
Grid or Cloud
Domain science specific Common to all sciences
and industry

“My Friends” Services
•  Dynamic Resource provisioning
•  Workload management
– schedule resource, establish runtime
environment, execute workload, handle
results, clean up
•  Data distribution and access
– Input, output, and relevant metadata
•  File catalogue

!"#$%&'!&()"&*+*,-.&/&-)&0/&
!"#$%&"'#()*#+)",#-.//&-0."*#.1#.23&-'*#45'(#+)",#-.+6."&"'*7#
!"#$%&$)11*$2),3 &4&!"#$%/5&!"#$%65&7&!"#$%8-&9 &#&(*:&/;;.&'*"&*+*,-&
<*-&$)11*$2),3 & &4&<*-/5&<*-65&7&<*-8=&9& & &#&(*:&/;.&'*"&*+*,-&
01*$-"),&$)11*$2),3&4&01*$-"),/5&7&9& & & &#&$)>'1*5&?(&#-&#11&
!"#$%&@5&*+A&/&-)&0/&!"#$%&'B5&*+A&/&-)&0/&
<*-&0C#D"),?$5&*+A&/&-)&0/& 01*$-"),&00E5&*+A&/&-)&0/&
7&
7&7&
$1>.-*"/&
!"#$%&'!&()"&*+*,-.&0/&-)&06& !"#$%&@5&*+A&0/&-)&06&!"#$%&'B5&*+A&0/&-)&06&
<*-&0C#D"),?$5&*+A&0/&-)&06& 01*$-"),&00E5&*+A&0/&-)&06&
7&
7&7&
$1>.-*"6&
•  0#$C&FG#.%*-H&$)I'"*..*D&.*'#"#-*1J&KL&)'2I?B*D&-)&*M$?*,-1J&"*#D3&
–  '#"2#1&*+*,-5&*ANA5&),1J&!"#$%.O&
–  '#"2#1&)G=*$-5&*ANA5&),1J&01*$-"),&00E&-)&D*$?D*&?(&-C*&*+*,-&?.&?,-*"*.2,N&#-&#11&&
•  P)"&QER5&$1>.-*"&.?B*&),&D?.%&?.&S&T&6;&EU&)"&/;&T&V;&*+*,-.&
•  !)-#1&W1*&.?B*&(")I&/;;&EU&-)&/;&XU&
FU#.%*-H&
Q1>.-*".&)(&0+*,-.&
Optimize Data Structure for
Partial Reads

Fraction of file read [%]
0 0.2 0.4 0.6 0.8 1
N
4
10
5
10
6
10
7
10
Fraction of a file that is read
#offilesread
For vast majority of files, less than 20% of the file is read.
20%
Average 20-35%
Median 3-7%
(depending on type of file)
Overflow bin

From present to future
•  Initially, we operated a largely static system.
–  Data was placed quasi-static before it can be analyzed.
–  Analysis centers have contractual agreements with the collaboration.
–  All reconstruction is done at centers with custodial archives.
•  Increasingly, we have too much data to afford this.
–  Dynamic data placement
•  Data is placed at T2s based on job backlog in global queues.
–  WAN access: ”Any Data, Anytime, Anywhere”
•  Jobs are started on the same continent as the data instead of the same
cluster attached to the data.
–  Dynamic creation of data processing centers
•  Tier-1 hardware bought to satisfy steady state needs instead of peak needs.
•  Primary processing as data comes off the detector => steady state
•  Annual Reprocessing of accumulated data => peak needs

Any Data, Anytime, Anywhere
Site A Site B Site C
Global Xrootd
Redirector
Xrootd Xrootd Xrootd
Lustre Storage Hadoop Storage dCache Storage
User
Application Q: Open /store/foo
A: Check Site A
Q: Open /store/foo
A: Success!
Cmsd Cmsd Cmsd
Xrootd Cmsd
Global redirection system to unify all CMS data
into one globally accessible namespace.
Is made possible by paying careful attention to IO layer
to avoid inefficiencies due to IO related latencies.

Tape Archive!
@ FNAL!
Tier-2 Centers!
@ OSG!
Steady State!
Processing!
@ FNAL!
Peak!
Processing!
@ SDSC!
Cloud and/or OSG!
Resources!
Simulated!
Data!
Vision going forward
Implemented vision for 1st time in Spring 2013
using Gordon Supercomputer at SDSC.

September 25th 2013 Frank Wurthwein - ISC Big Data 29SAN DIEGO SUPERCOMPUTER CENTER
Gordon Overview!
•  !"#$%&'(#
•  "')*#&)+*#,"-#
•  ./0#12#34(564&4#789#
:%;4(#
•  <1#=%&40#/>#?@8:%;4#
•  /#A27#=%:5&%**4&(#
•  <.#22"(#
•  "')*#<B?CD#
•  2'E4&F+=&%#6%C%#
•  GH7#?4:1#
•  !BB#?@#7:54*#I<B#
4FAH#22"(#
•  !BB#$@#)JJ&4J)54#
•  <0B1/#12#K4%:#DL#
M2):;N#@&+;J4O#:%;4(#
•  <.#=%&4(0#./#?@8:%;4#
•  7:54*#P4Q4&(%:#G)((#
6%C%#
•  GH7#?4:!#
•  A)&J4#F46%&N#R2FG#
2'E4&:%;4(#
•  1$@#"-SF#
•  <B#$@#T*)(U#
V")5)#9)(+(W#
A'(5&4#GT2#
<BB#?@8(4=0#/#G@#
SAN DIEGO SUPERCOMPUTER CENTER
Accelerate LHC Science"
!
Rick Wagner!
San Diego Supercomputer Center!
XSEDE 13"
July 22-25, 2013"
San Diego, CA"
!
Brian Bockelman!
University of Nebraska-Lincoln!

CMS “My Friends” Stack
•  CMSSW release environment
–  NFS exported from Gordon IO nodes
–  Future: CernVM-FS via Squid caches
•  J. Blomer et al.; 2012 J. Phys.: Conf. Ser. 396 052013
•  Security Context (CA certs, CRLs) via OSG worker node client
•  CMS calibration data access via FroNTier
•  B. Blumenfeld et al; 2008 J. Phys.: Conf. Ser. 119 072007
–  Squid caches installed on Gordon IO nodes
•  glideinWMS
•  I. Sfiligoi et al.; doi:10.1109/CSIE.2009.950
–  Implements “late binding” provisioning of CPU and job scheduling
–  Submits pilots to Gordon via BOSCO (GSI-SSH)
•  WMAgent to manage CMS workloads
•  PhEDEx data transfer management
–  Uses SRM and gridftp
Jobenvironment
DataandJob
handling

CMS “My Friends” Stack
•  CMSSW release environment
–  NFS exported from Gordon IO nodes
–  Future: CernVM-FS via Squid caches
•  J. Blomer et al.; 2012 J. Phys.: Conf. Ser. 396 052013
•  Security Context (CA certs, CRLs) via OSG worker node client
•  CMS calibration data access via FroNTier
•  B. Blumenfeld et al; 2008 J. Phys.: Conf. Ser. 119 072007
–  Squid caches installed on Gordon IO nodes
•  glideinWMS
•  I. Sfiligoi et al.; doi:10.1109/CSIE.2009.950
–  Implements “late binding” provisioning of CPU and job scheduling
–  Submits pilots to Gordon via BOSCO (GSI-SSH)
•  WMAgent to manage CMS workloads
•  PhEDEx data transfer management
–  Uses SRM and gridftp
Jobenvironment
DataandJob
handling
This is clearly mighty complex !!!
So let’s focus only on the parts
that are specific to incorporating
Gordon as a dynamic data
processing center.

SAN DIEGO SUPERCOMPUTER CENTER
Items in red were deployed/modified to incorporate Gordon
Minor mod of
PhEDEx config file
Deploy Squid
Export CMSSW
& WN client

Gordon Results
•  Work completed in February/March 2013 as a result of
a “lunch conversation” between SDSC & US-CMS
management
–  Dynamically responding to an opportunity
•  400 Million RAW events processed
–  125 TB in and ~150 TB out
–  ~2 Million core hours of processing
•  Extremely useful for both science results as well as
proof of principle in software & computing.

Summary & Conclusions
•  Guided by the principles:
– Support distributed ownership and control in a
global single sign-on security context.
– Design for heterogeneity and adaptability
•  The LHC experiments very successfully
developed and implemented a set of new
concepts to deal with BigData.

Outlook
•  The LHC experiments had to largely invent an
island of BigData technologies with limited
interactions with industry and other domain
sciences.
•  Is it worth building bridges to other islands ?
– IO stack and HDF5 ?
– MapReduce ?
– What else ?
•  Is there a mainland emerging that is not just
another island ?

Dynamic Data Center concept

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