Extreme Scripting July 2009
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Ian Foster Allan Espinosa, Ioan Raicu, Mike Wilde, Zhao Zhang Computation Institute Argonne National Lab & University of Chicago Extreme scripting and other adventures in data-intensive computing
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A talk presented at an NSF Workshop on Data-Intensive Computing, July 30, 2009.Extreme scripting and other adventures in data-intensive computingData analysis in many scientific laboratories is performed via a mix of standalone analysis programs, often written in languages such as Matlab or R, and shell scripts, used to coordinate multiple invocations of these programs. These programs and scripts all run against a shared file system that is used to store both experimental data and computational results.While superficially messy, the flexibility and simplicity of this approach makes it highly popular and surprisingly effective. However, continued exponential growth in data volumes is leading to a crisis of sorts in many laboratories. Workstations and file servers, even local clusters and storage arrays, are no longer adequate. Users also struggle with the logistical challenges of managing growing numbers of files and computational tasks. In other words, they face the need to engage in data-intensive computing.We describe the Swift project, an approach to this problem that seeks not to replace the scripting approach but to scale it, from the desktop to larger clusters and ultimately to supercomputers. Motivated by applications in the physical, biological, and social sciences, we have developed methods that allow for the specification of parallel scripts that operate on large amounts of data, and the efficient and reliable execution of those scripts on different computing systems. A particular focus of this work is on methods for implementing, in an efficient and scalable manner, the Posix file system semantics that underpin scripting applications. These methods have allowed us to run applications unchanged on workstations, clusters, infrastructure as a service ("cloud") systems, and supercomputers, and to scale applications from a single workstation to a 160,000-core supercomputer.Swift is one of a variety of projects in the Computation Institute that seek individually and collectively to develop and apply software architectures and methods for data-intensive computing. Our investigations seek to treat data management and analysis as an end-to-end problem. Because interesting data often has its origins in multiple organizations, a full treatment must encompass not only data analysis but also issues of data discovery, access, and integration. Depending on context, data-intensive applications may have to compute on data at its source, move data to computing, operate on streaming data, or adopt some hybrid of these and other approaches.Thus, our projects span a wide range, from software technologies (e.g., Swift, the Nimbus infrastructure as a service system, the GridFTP and DataKoa data movement and management systems, the Globus tools for service oriented science, the PVFS parallel file system) to application-oriented projects (e.g., text analysis in the biological sciences, metagenomic analysis, image analysis in neuroscience, information integration for health care applications, management of experimental data from X-ray sources, diffusion tensor imaging for computer aided diagnosis), and the creation and operation of national-scale infrastructures, including the Earth System Grid (ESG), cancer Biomedical Informatics Grid (caBIG), Biomedical Informatics Research Network (BIRN), TeraGrid, and Open Science Grid (OSG).For more information, please see www.ci.uchicago/swift.
Transcript of Extreme Scripting July 2009
Ian FosterAllan Espinosa, Ioan Raicu, Mike Wilde, Zhao Zhang
Computation Institute
Argonne National Lab & University of Chicago
Extreme scriptingand other adventures
in data-intensive computing
2
How data analysis happens at data-intensive computing workshops
3How data analysis really happensin scientific laboratories
% foo file1 > file2
% bar file2 > file3
% foo file1 | bar > file3
% foreach f (f1 f2 f3 f4 f5 f6 f7 … f100)
foreach? foo $f.in | bar > $f.out
foreach? end
%
% Now where on earth is f98.out, and how did I generate it again?
Now: command not found.
%
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Extreme scripting
Simplescripts
Complexscripts
Bigcomputers
Smallcomputers
Many activitiesNumerous filesComplex data
Data dependencies
Many programs
Many processorsStorage hierarchy
FailureHeterogeneity
Swift
Preservingfile system semantics,
ability to call arbitrary executables
5Functional magnetic resonance imaging (fMRI) data analysis
6
AIRSN program definition
(Run snr) functional ( Run r, NormAnat a, Air shrink ) {
Run yroRun = reorientRun( r , "y" );Run roRun = reorientRun( yroRun , "x" );Volume std = roRun[0];Run rndr = random_select( roRun, 0.1 );AirVector rndAirVec = align_linearRun( rndr, std, 12, 1000, 1000, "81 3 3" );Run reslicedRndr = resliceRun( rndr, rndAirVec, "o", "k" );Volume meanRand = softmean( reslicedRndr, "y", "null" );Air mnQAAir = alignlinear( a.nHires, meanRand, 6, 1000, 4, "81 3 3" );Warp boldNormWarp = combinewarp( shrink, a.aWarp, mnQAAir );Run nr = reslice_warp_run( boldNormWarp, roRun );Volume meanAll = strictmean( nr, "y", "null" )Volume boldMask = binarize( meanAll, "y" );snr = gsmoothRun( nr, boldMask, "6 6 6" );}
(Run or) reorientRun (Run ir, string direction) { foreach Volume iv, i in ir.v { or.v[i] = reorient(iv, direction); }}
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Many many tasks:Identifying potential drug targets
2M+ ligands Protein xtarget(s)
Benoit Roux et al.
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start
report
DOCK6Receptor
(1 per protein:defines pocket
to bind to)
ZINC3-D
structures
ligands complexes
NAB scriptparameters
(defines flexibleresidues,
#MDsteps)
Amber Score:1. AmberizeLigand
3. AmberizeComplex5. RunNABScript
end
BuildNABScript
NABScript
NABScript
Template
Amber prep:2. AmberizeReceptor4. perl: gen nabscript
FREDReceptor
(1 per protein:defines pocket
to bind to)
Manually prepDOCK6 rec file
Manually prepFRED rec file
1 protein(1MB)
6 GB2M
structures(6 GB)
DOCK6FRED~4M x 60s x 1 cpu
~60K cpu-hrs
Amber~10K x 20m x 1 cpu
~3K cpu-hrs
Select best ~500
~500 x 10hr x 100 cpu~500K cpu-hrsGCMC
PDBprotein
descriptions
Select best ~5KSelect best ~5K
For 1 target:4 million tasks
500,000 cpu-hrs(50 cpu-years)
9
10
IBM BG/P570 Teraflop/s, 164,000 cores, 80 TB
11DOCK on BG/P: ~1M tasks on 119,000 CPUs
118784 cores
934803 tasks
Elapsed time: 7257 sec
Compute time: 21.43 CPU years
Average task: 667 sec
Relative efficiency 99.7% (from 16 to 32 racks)Utilization: 99.6% sustained, 78.3% overall
Ioan Raicu et al.
Time (sec)
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Managing 160,000 cores
Slower shared storage
High-speed local “disk”
Falkon
13Scaling Posix to
petascale
LFS Computenode
(local datasets)
LFS Computenode
(local datasets)
IFSseg
IFScompute
node
IFSseg
IFScompute
node
…
. . .
Largedataset
CN-striped intermediate file system
Torus and tree interconnects
ZOID IFS ZOID on I/O node
Global file systemChirp(multicast)
MosaStore(striping)
Staging
Intermediate
Local
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Efficiency for 4 second tasks and varying data size (1KB to 1MB) for CIO and GPFS up to 32K processors
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Provisioning for data-intensive workloads Example: on-demand
“stacking” of arbitrary locations within ~10TB sky survey
Challenges Random data access Much computing Time-varying load
Solution Dynamic acquisition
of compute & storage
Data diffusion
++++++
=
+
S SloanData
IoanRaicu
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“Sine” workload, 2M tasks, 10MB:10ms ratio, 100 nodes, GCC policy, 50GB caches/node
IoanRaicu
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Same scenario, but with dynamic resource provisioning
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Data diffusion sine-wave workload: Summary
GPFS 5.70 hrs, ~8Gb/s, 1138 CPU hrs DD+SRP 1.80 hrs, ~25Gb/s, 361 CPU hrs DD+DRP 1.86 hrs, ~24Gb/s, 253 CPU hrs
19Data-intensive computing @ Computation Institute: Example applications
Astrophysics Cognitive science East Asian studies Economics Environmental science Epidemiology Genomic medicine Neuroscience Political science Sociology Solid state physics
20
21
Sequencing outpaces Moore’s law
0.5 1 30 60 98 196 294$3,000
$30,000
$300,000
$3,000,000
$3,500
$7,000
$120,000
$240,000$300,000
$600,000$900,000
$15,000
$30,000 $15,000
$30,000
$15,000
$30,000
$45,000
BioinformaticsSequencing
Gigabases
454 Solexa
Next-gen Solexa
Folker Meyer, Computation Institute
BLASTOn EC2,
US$
22Data-intensive computing @ Computation Institute: Hardware
500 TB reliable storage (data &
metadata)
180 TB, 180 GB/s
17 Top/s analysis
Dataingest
Dynamic provisioning
Parallel analysis
Remote access
Offload to remote data centers
P A D S
Diverseusers
Diversedata
sources
1000 TBtape backup
PADS: Petascale Active Data Store (NSF MRI)
23Data-intensive computing @
Computation Institute: Software HPC systems software (MPICH, PVFS, ZeptOS) Collaborative data tagging (GLOSS) Data integration (XDTM) HPC data analytics and visualization Loosely coupled parallelism (Swift, Hadoop) Dynamic provisioning (Falkon) Service authoring (Introduce, caGrid, gRAVI) Provenance recording and query (Swift) Service composition and workflow (Taverna) Virtualization management (Workspace Service) Distributed data management (GridFTP, etc.)
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Low
LowHigh
High
Agreementabout
outcomes
Certainty about outcomes
Data-intensive computing is an end-to-end problem
Plan and
control
Chaos
Zone of
complexity
Ralph Stacey, Complexity and Creativity in Organizations, 1996
25
Low
LowHigh
High
Agreementabout
outcomes
Certainty about outcomes
We need to function in the zone of complexity
Plan and
control
Chaos
Ralph Stacey, Complexity and Creativity in Organizations, 1996
26
The Grid paradigm
1995 2000 2005 2010
Principles and mechanisms for dynamic virtual organizations
Leverage service oriented architecture Loose coupling of data and services Open software,
architecture
Computer science
Physics
Astronomy
Engineering
Biology
Biomedicine
Healthcare
27
As of Oct19, 2008:
122 participants105 services
70 data35 analytical
28
(Center for Health Informatics)
Multi-center clinical cancer trials image capture and review
29
Summary
Extreme scripting offers the potential for easy scaling of proven working practices Interesting technical problems relating to
programming and I/O models Many wonderful applications
Data-intensive computing is an end-to-end problem Data generation, integration, analysis, etc.,
is a continuous, loosely coupled process
Thank you!
Computation Institutewww.ci.uchicago.edu
