Possible foreseeable measures for tera-scale data handling
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Transcript of Possible foreseeable measures for tera-scale data handling
Possible foreseeable measures for tera-scale data handling
Kazutoshi Horiuchi*1
Keiko Takahashi*1
Hirofumi Sakuma*1
Shigemune Kitawaki*2
*1 Frontier Research System for Global Change
*2 Earth Simulator Research and Development Center
Global Change Prediction by an Integrated Three-in-one Research
Observation
Numerical Simulation
Process Study & Modeling
Accurate & spatially representative data
Optimal monitoring plan
Sophisticated high resolution model
High performance computing
Assimilation data for validation
Accurate & spatially representative data
ESRDC FRSGC
FORSGC
Frontier Research System for Global Change (Project)
Funding Bodies
• Japan Marine Science and Technology Center (JAMSTEC)
• National Space Development Agency (NASDA)
Activities
• Process Study • Model Development
(Common) Goal Global Change Prediction
On-Going Process Studies
Climate Variations ResearchHydrological Cycle ResearchGlobal Warming ResearchAtmospheric Composition ResearchEcosystem Change ResearchResearch of International Pacific Research CenterResearch of International Arctic Research Center
Current Target of Model Development Group
Coupled Model (now based on CCSR/NIES, MOM3)for Climate Change Experiment
Cloud Cluster Resolving Ultra High Resolution Model for Prediction of Typhoon/Baiu Evolution
Coupled Chemistry - Global Climate Modelfor Prediction of Atmospheric Composition Change
Next Generation Model (Cubic/Icosahedral Grid, CIP method)
4DVAR Ocean Data Assimilation Model (based on MOM3)
Current Target of Coupled Model
Development on ES
High Resolution: Atmosphere Model part: T213L50 Ocean Model part: 1/10 deg. 53 layers
High Performance
Estimation of Acceleration ratio
• Atmosphere Model: (under estimation)
• Ocean Model: 300 ~ 400 times (480PEs;60Ns) 5 days for 100 years integration
Earth Simulator Research Development Center (Project)
Funding Bodies
•Japan Atomic Energy Research Institute (JAERI)•National Space Development Agency (NASDA)•Japan Marine Science and Technology Center (JAMSTEC)
Activities
• Development of High Speed Parallel Computer• Understanding and Prediction of Global Change
(Common) Goal Global Change Prediction
Characteristics of Earth Simulator
Peak Performance: 40TFLOPSNumber of Processor Nodes: 640Number of PEs: 5120 (8PEs/Node)Interconnection Network: 16GB/s Total Memory: 10TBTotal Secondary Storage: 600-700TBTotal Mass Storage: 1PB (84Drives)
16 Nodes
Architecture of Earth Simulator
16 Nodes
Interconnection Network
TSS Cluster *1 Batch Cluster *39
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Fiber Channel Switch
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MM
VP
0
VP
1
VP
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16 Nodes
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MM
VP
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VP
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Fiber Channel Switch
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16 Nodes
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MM
VP
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Mass Storage System
WAN
WS
WS
WS
FS
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84 Drives
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I/O Model in Distributed Memory Parallel Computer
P P P…
communication
P P P… P P P…
(Unix) File (Data-Distributed Unix) Files
Parallel File
Parallel File System on ESTo handle distributed data as a logically single file has advantages to develop application softwares and to process post processings.
1234567
1 2 34 5 67 8 9
1 4 72 5 83 6 9
M1 M2
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File Image
P
P
PProcessors Disks
Proc no.: Np
Dist. Size: Sd
Dist. Pattern: P= BLOCK/CYCLIC
Disk no.: Nd
Striping Size: Ss
Distribution MechanismD1
D2
D3
P1
P2
P3
1 2 3
4 5 6
7 8 9
Support for Parallel File on Several Levels
……
Unix File Parallel File
Operating System (with PFS)
FA
L
MPI-IO
F90
HPF-RTPF90-RTP
Library
Compiler
C
User Program
PFS UFS PFS UFS PFS
Hardware
HPF
Review of Model Development Flow
Improvement of Model
Execution with Model
Evaluation of Results
Input Data
Output Data
Resources for Process Study
Results of Process Study
•Analysis•Visualization
Is it Satisfactory about I/O performance?
The faster super computers are, the larger the amount of the output data generated by large-scale simulations.
The large amount of data is stored to secondary storages and/or mass storages whose devices are slower.
Is it satisfactory about I/O performance ?
Amount of Input/Output Data -Coupled Model
To answer the question, the following cases are investigated . Case I:
1000 Years Integration for the Prediction of Global Warming, Decadal Variability, etc.
Case II: 50 Years Integration for the Analysis of El nino, Dipole Mode Events, Asian Monsoon, etc.
Amount of Input/Output Data -Coupled Model
Atmosphere Model part
NOTE: The amount of output data is estimated as 2 byte integer elements
Vars Amount [GB] Times Vars Amount [GB]I 1000 T213L50 5+3 14 monthly 7+27 1,726II 50 T213L50 5+3 1 6hourly 7+27 10,498
Input OutputCase Periods [Y] Resolution
Vars Amount [GB] Times Vars Amount [GB]I 1000 0.1deg,L53 4+4 582 monthly 5+8 39,541II 50 0.1deg,L53 4+4 89 10days 5+8 6,096
Case Periods [Y] Input OutputResolution
Ocean Model part
Estimated I/O time - Coupled Model
NOTE: Time is estimated only on drive’s I/O rate. Multiple drives are assumed to be independent.
8 16 84Input 0.05 0.02 0.005 0.001Output 6.0 3.0 0.6 0.08Input 0.003 0.001 0.0003 0.00004Output 36.5 18.2 3.5 0.49Input 2.0 1.0 0.2 0.03Output 137.3 68.6 13.1 1.83Input 0.3 0.2 0.03 0.004Output 21.2 10.6 2.0 0.28
Time of ModelRun (Estimation)
Disk I/ O time(Estimation)
[unit:hour]
Atmos-phere
Model Case In/ Out I/ O time with Tape Drives:
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1200
60Ocean
I
II
I
II
Summary of I/O Performance (from the viewpoint of Model Development)
Disk I/O time might be satisfactory. 0.2% of the simulation time Less than 2 hours as a total
Tape I/O time might be conspicuous. 11-35% of the simulation time for 8 tape drives 1-6 days for 8 tape drives
This inefficiency might be critical for iterative works such as model development
How to Shorten Turn Around Time of Model Development
Give up outputting numerical data.Output necessary minimum data.Output full data, with executing tape I/O
and simulations in parallel, and with tape I/O library being able to extract necessary minimum data for post processing.
Shortening of TAT by Giving up Outputting Numerical Data
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3.0
1.9
67.5
68.1
68.0
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0.98
0.79
Elapsed time (h)
Time increase (%)
Elapsed time (h)
Time increase (%)
CFD solver only
+ fixed camera
+ moving camera
1.6M grid (169x92x101) 6.2M grid (337x183x101)
14.5
14.7
14.9
Elapsed time for the concurrent visualization with RVSLIB in the batch processing mode on SX-4
*The number of computational time steps was 10000.*Contour and tracers were displayed at every 10 time steps and visualized animation was stored in a file.*Time integration for moving the tracers was done at every time step for greater accuracy.
* This result was provided by NEC
Shortening of TAT by Outputting Necessary Minimum Data
“Browse sets”, into which the large amount of output data is abstracted (spatially and/or temporally) within simulations, should be stored.
Specific regions of output data should be stored.
This may be Know-Hows of using ES
Shortening of TAT by Enhancement of Tape I/O Library for Full Output Data
Tape I/O should be executed with simulations in parallel.
In mass storages, output data should be re-organized, and small subsets which are needed for post processing should be able to be extracted.
This may be requirements for the improvement of ES
Conclusion
I/O performance was roughly estimated and I/O problem was apprehended.
I/O problem would be avoided with concurrent visualization and/or know-how of the usage.
However we would like to examine the efficient technique for handling the large amount of data continuously to realize comfortable environment for global change prediction.