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TRANSCRIPT
ESMF/V3: [email protected], [email protected], [email protected]/V3: [email protected], [email protected], [email protected] Managed by UT-Battelle
for the Department of Energy
11
ESMF
9/23/2008
Scott Klasky, Jay Lofstead, Mladen Vouk
ORNL, Georgia Tech, NCSU
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EFFIS (Klasky)
ADIOS.– ADIOS Overview (Klasky)– ADIOS Advanced Topics (Lofstead)
Workflow. (Vouk)
Dashboard. (Vouk)
Conclusions. (Klasky)
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Some simulations are starting to produce 100TB/day on the 270 TF Cray XT at ORNL.
Old way of run now, and look at results later has problems.– Data will be eventually archived on tape.
Lots of files from 1 run with multiple users gives us a data management headache.
Need to keep track of data over multiple system.
Extracting information from files needs to be easy.– Example: min/max of 100GB arrays needs to be almost
instant.
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Problem: Managing the data from a petascale simulation, and debugging the simulation, and extracting the science involves.– Tracking the codes: Simulation, Analysis.– Tracking the input files/parameters– Tracking the output files, from the simulation and then
analysis programs.– Tracking the machines and environment the codes
ran on.– Gluing everything together.– Visualizing the results, and analyzing the results
without requiring users to know all of the file names.– Fast I/O which can be easily tracked.
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– Workflow Automation to automate all of the mundane tasks.
– Analyzing the results, without knowing all of the file locations/names.
– Moving data from the simulation side to remote locations without knowledge of filename(s)/locations.
– Monitoring results in real-time,
Requirements.– Want technologies integrated together; easy to talk to
one another.
– Want to make the system scalable in the I/O workflow, analysis, visualization, data management.
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EFFIS
ADIOS.– ADIOS Overview– BP format, and compatibility with hdf5/netcdf.
Workflow.
Dashboard.
Conclusions.
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“Those fine fort.* files!”
Multiple HPC architectures– BlueGene, Cray, IB-based clusters
Multiple Parallel Filesystems– Lustre, PVFS2, GPFS, Panasas, PNFS
Many different APIs– MPI-IO, POSIX, HDF5, netCDF– GTC (fusion) has changed IO routines 8 times so far based on
performance when moving to different platforms.
Different IO patterns– Restarts, analysis, diagnostics– Different combinations provide different levels of IO performance
Compensate for inefficiencies in the current IO infrastructures to improve overall performance
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Allows plug-ins for different I/O implementations. Abstracts the API from the method used for I/O. Simple API, almost as easy as F90 write statement. Best practices/optimize IO routines for all supported
transports “for free”
Componentization. Thin API XML file
– data groupings with annotation– IO method selection– buffer sizes
Common tools– Buffering– Scheduling
Pluggable IO routines
ExternalMetadata(XML file)
Scientific Codes
ADIOS API
MPI-CIO
LIVE/DataTap
MPI-IO
POSIX IO
pHD
F-5
pnetCDF
Viz Engines
Others (plug-in)
buffering schedule feedback
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• ADIOS is an IO componentization, which allows us to– Abstract the API from the IO implementation.– Switch from synchronous to asynchronous IO at runtime.– Change from real-time visualization to fast IO at runtime.
• Combines.– Fast I/O routines.– Easy to use.– Scalable architecture
(100s cores) millions of procs.– QoS.– Metadata rich output.– Visualization applied during simulations.– Analysis, compression techniques applied during simulations.– Provenance tracking.
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Simple API very similar to standard Fortran or C POSIX IO calls.– As close to identical as possible for C and Fortran API– open, read/write, close is the core– set_path, end_iteration, begin/end_computation, init/finalize are the
auxiliaries
No changes in the API for different transport methods.
Metadata and configuration defined in an external XML file parsed once on startup.– Describe the various IO grouping including attributes and
hierarchical path structures for elements as an adios-group– Define the transport method used for each adios-group and give
parameters for communication/writing/reading– Change on a per element basis what is written– Change on a per adios-group basis how the IO is handled
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ADIOS Fortran and C based API almost as simple as standard POSIX IO
External configuration to describe metadata and control IO settings
Take advantage of existing IO techniques (no new native IO methods)
Fast, simple-to-write, efficient IO for multiple platforms without changing the source code
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Data groupings– logical groups of related items written at the same time.
Not necessarily one group per writing event
IO Methods– Choose what works best for each grouping
– Vetted, improved, and/or written by experts for each POSIX (Wei-keng Liao, Northwestern) MPI-IO (Steve Hodson, ORNL) MPI-IO Collective (Wei-keng Liao, Northwestern) NULL (Jay Lofstead, GT) Ga Tech DataTap Asynchronous (HasanAbbasi, GT) phdf5 others.. (pnetcdf on the way).
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Specialty APIs– HDF-5 – complex API– Parallel netCDF – no structure
File system aware middleware– MPI ADIO layer – File system connection, complex API
Parallel File systems– Lustre – Metadata server issues– PVFS2 – client complexity– LWFS – client complexity– GPFS, pNFS, Panasas – may have other issues
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Platforms tested– Cray CNL (ORNL Jaguar)– Cray Catamount (SNL Redstorm)– Linux Infiniband/Gigabit (ORNL Ewok)– BlueGene P now being tested/debugged.– Looking for future OSX support.
Native IO Methods– MPI-IO independent, MPI-IO collective,
POSIX, NULL, Ga Tech DataTap asynchronous, Rutgers DART asynchronous, Posix-NxM, phdf5, pnetcdf, kepler-db
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MPI-IO method.– GTC and GTS codes have achieved over 20 GB/sec on
Cray XT at ORNL. 30GB diagnostic files every 3 minutes, 1.2 TB restart files every
30 minutes, 300MB other diagnostic files every 3 minutes.
DART: <2% overhead forwriting 2 TB/hour withXGC code.
DataTap vs. Posix– 1 file per process (Posix).– 5 secs for GTC
computation.– ~25 seconds for Posix IO– ~4 seconds with DataTap
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June 7, 2008: 24 hour GTC run on Jaguar at ORNL– 93% of machine (28,672 cores)
– MPI-OpenMP mixed model on quad-core nodes (7168 MPI procs)
– three interruptions total (simple node failure) with 2 10+ hour runs
– Wrote 65 TB of data at >20 GB/sec (25 TB for post analysis)
– IO overhead ~3% of wall clock time.
– Mixed IO methods of synchronous MPI-IO and POSIX IO configured in the XML file
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Chimera IO Performance (Supernova code)
2x scaling
• Plot minimum value from 5 runs with 9 restarts/run• Error bars show maximum time for the method.
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Chimera Benchmark Results Why ADIOS is better than pHDF5?
ADIOS_MPI_IO vs. pHDF5 w/ MPI Indep. IO driver
ADIOS_MPI_IO
Function # of calls Time
write 2560 2218.28
MPI_File_open 2560 95.80
MPI_Recv 2555 24.68
other -- ~65
pHDF5
Function # of calls Time
write 144065 33109.67
MPI_Bcast(sync) 314800 12259.30
MPI_File_open 2560 325.17
H5P,H5D,etc -- 8.71
other -- ~61
Use 512 cores, 5 restart dumps.
Conversion time on 1 processor for the 2048 core job = 3.6s (read) + 5.6s (write) + 6.9 (other) = 18.8 s
Number above are sum among all PEs (parallelism not shown)
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J. Lofstead
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XML configuration file:<adios-config>
<adios-group name=“output” coordination-communicator=“group_comm”>
<var name=“group_comm” type=“integer”/>
<var name=“g_NX” type=“integer” />
<var name=“g_NY” type=“integer”/>
<var name=“lo_x” type=“integer”/>
<var name=“lo_y” type=“integer”/>
<var name=“l_NX” type=“integer”/>
<var name=“l_NY” type=“integer”/>
<global-bounds dimensions=“g_NX,g_NY” offsets=“lo_x,lo_y”>
<var name=“temperature” dimensions=“l_NX,l_NY”/>
</global-bounds>
<attribute name=“units” path=“/temperature” value=“K”/>
</adios-group>
… <!-- declare additional adios-groups -->
<method method=“MPI” group=“output”/>
<!-- add more methods -->
<buffer size-MB=“100” allocate-time=“now”/>
</adios-config>
Fortan90 code:! initialize the system loading the configuration file
adios_init (“config.xml”, err)
! open a write path for that type
adios_open (h1, “output”, “restart.n1”, “w”, err)
adios_group_size (h1, size, total_size, comm, err)
! write the data items
adios_write (h1, “g_NX”, 1000, err)
adios_write (h1, “g_NY”, 800, err)
adios_write (h1, “lo_x”, x_offset, err)
adios_write (h1, “lo_y”, y_offset, err)
adios_write (h1, “l_NX”, x_size, err)
adios_write (h1, “l_NY”, y_size, err)
adios_write (h1, “temperature”, u, err)
! commit the writes for asynchronous transmission
adios_close (h1, err)
… ! do more work
! shutdown the system at the end of my run
adios_finalize (mype, err)
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C code:// parse the XML file and determine buffer sizes
adios_init (“config.xml”);
// open and write the retrieved type
adios_open (&h1, “restart”, “restart.n1”, “w”);
adios_group_size (h1, size, &total_size, comm);
adios_write (h1, “n”, n); // int n;
adios_write (h1, “mi”, mi); // int mi;
adios_write (h1, “zion”, zion); // float zion [10][20][30][40];
// write more variables
...
// commit the writes for synchronous transmission or
// generally initiate the write for asynchronous transmission
adios_close (h1);
// do more work
...
// shutdown the system at the end of my run
adios_finalize (mype);
XML configuration file:<adios-config host-language=“C”><adios-group name=“restart”><var name=“n” path=“/” type=“integer” /><var name=“mi” path=“/param” type=“integer”/>
… <!-- declare more data elements --><var name=“zion” type=“real” dimensions=“n,4,2,mi”/><attribute name=“units” path=“/param” value=“m/s”/></adios-group>… <!-- declare additional adios-groups -->
<method method=“MPI” group=“restart”/><method priority=“2” method=“DATATAP” iterations=“1”
type=“diagnosis”>srv=ewok001.ccs.ornl.gov</method><!-- add more methods --><buffer size-MB=“100” allocate-time=“now”/></adios-config>
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netCDF and HDF-5 are excellent, mature file formats
APIs can have trouble scaling to petascale and beyond– metadata operations bottleneck at MDS
– coordination among all processes takes time
– MPI Collective writes/reads add additional coordination
– Non-stripe-sized writes impact performance
– Read/write mode is slower than write only
– Replicate some metadata for resilience
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Solution: Use an intermediate API and format
ADIOS API and BP format– API natively writes BP format (netCDF coming)
– converters to netCDF and HDF-5 available Convert files at speeds limited by the performance of disk
and the netCDF/HDF-5 API
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File organization– Move the “header” to the end
last 28 bytes are 3 index locations and version + endian-ness flag
– Each process writes completely independently First part of file a series of “Process Groups”, each the output
from a single process for a single IO grouping
– Coordinate only twice Once at start for writing location Once at end for metadata collection to process 0 and writing by
process 0 only
– Replicate some metadata Each “Process Group” is fully self-contained with all related
meta-data Indexes contain copies of “highlights” of the metadata
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Index Structure– Process Group Index
ADIOS group, process ID, timestep, offset in file
– Vars Index Set of unique vars listing group, name, path, datatype,
characteristics (see next slide) Uniqueness based on group name, var name, var path
– Attributes Index Set of unique attributes listing group, name, path, datatype,
characteristics (see next slide) Uniqueness based on group name, attribute name, attribute
path
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Data Characteristics– Idea: collect information about the var/attribute for
quickly characterizing the data
– Examples: Offset in file Value (only for “small” data) Minimum Maximum Instance array dimensions
– Structure setup for adding more without changing file format
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Write operation (n processes)– Gather data sizes to process 0
– Process 0 generates offset to write for each process
– Scatter offsets back to processes
– Everybody write data independently
– Gather the local index from each process to process 0
– Merge all indices together
– Process 0 write indices at the end of the file
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Compromises using BP Format– Each “Process Group” can have different variables
defined and written (also an advantage)
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Advantages using BP Format– Each process writes independently
– Limited coordination
– File organization more natural for striping
– Rich index contents
– “Append” operations do not require moving data Indices read by process 0 on start and used as base index First new Process Group overwrites old indicies
– Index corruption does not potentially destroy entire file
– Process Group corruption isolated by still getting access to the rest of the process groups (via indices)
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EFFIS
ADIOS.– ADIOS Overview– BP format, and compatibility with hdf5/netcdf.
Workflow.
Dashboard.
Conclusions.
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Capture how a scientist works with data and analytical tools– data access, transformation, analysis, visualization– possible worldview: dataflow-oriented (cf. signal-processing)
Scientific workflows start where script-based data-management solutions leave off.
Scientific workflow (wf) benefits (v.s. script-based approaches):– wf automation– wf & component reuse, sharing, adaptation, archiving– wf design, documentation– built-in (model) concurrency
(task-, pipeline-parallelism)– built-in provenance support– distributed ¶llel exec: Grid & cluster support – wf fault-tolerance, reliability– Other …
Why a W/F System?Higher-level “language” vs.assembly-language natureof scripts
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Real-time Monitoring (Server Side Workflows)– Job submission.
– File movement.
– Launch Analysis Services.
– Launch Visualization Services.
– Launch Automatic Archiving.
Post Processing (Desktop Workflows).– Read in Files from different locations.
– File movement.
– Launch Analysis Services.
– Launch Visualization Services.
– Connect to Databases.
Obviously there are other types of workflows.– Parameter study/sensitivity analysis workflows.
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Process provenance.– the steps performed in the workflow,
the progress through the workflow control flow, etc.
Data provenance.– history and lineage of each data item
associated with the actual simulation (inputs, outputs, intermediate states, etc.);
Workflow provenance.– history of the workflow evolution and
structure;
System provenance.– All external (environment) information
relevant to a complete run.– Compilation history of the codes.– Information about the libraries.– Source of the codes.– Run-time environment settings.– Machine information– etc.
• Dashboard displays provenance information for- Data lineage.- Source Code for a simulation, analysis.- Performance Data from PAPI.- Workflow Provenance to determine if
something went wrong with the workflow.- Other …
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Modular Framework
Supercomputers+
Analytics Nodes
Kepler
Dash
Storage
Meta-Data about: Processes,Data,Workflows,System, Apps & Environment
Orchestration
Auth
DataStore
RecAPI
DispAPI
Management API
ADIOS is being modified to send the IO (+ coupling) metadata to Kepler
(e.g., file path, variables, control commands, …)
ADIOS is being modified to send the IO (+ coupling) metadata to Kepler
(e.g., file path, variables, control commands, …)
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Reliability (autonomics)
Usability (Must be EASY to use and functional)– Good user support, and long-term DOE support.
Universality and Reuse - The workflow should work for all of my workflows. (NOT just for the Petascale computers; multiple platforms)
Integration - Must be easy to incorporate my own services into the workflow.
Customization and adaptability - Must be customizable by the users.– Users need to easily change the workflow to work with the way users work.
Other - You tell us!
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Ptolemy II: A laboratory for investigating designKEPLER: A problem-solving support environment for Scientific Workflow development, execution, maintenance
KEPLER = “Ptolemy II + X” for Scientific Workflows
Kepler Scientific Workflow System
Kepler is a cross-project collaboration
Latest release available from the website
Builds upon the open-source Ptolemy II framework
Vergil is the GUI, but Kepler also runs in non-GUI and batch modes.
http://www.kepler-project.orghttp://www.kepler-project.org
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Vergil is the GUI for Kepler…
Actor ontology and semantic search for actors Search -> Drag and drop -> Link via ports Metadata-based search for datasets
Actor Search Data Search
… but Kepler can also run in batch mode as a command-line engine.
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Actor-Oriented Modeling
Actors single component or task well-defined interface (signature) generally a passive entity: given input data, produces output
data
Ports– each actor has a set of input and output ports– denote the actor’s signature– produce/consume data (a.k.a. tokens)– parameters are special “static” ports
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Actor-Oriented Modeling
Dataflow Connections– actor “communication” channels
– Directed edges
– connect output ports with input ports
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Actor-Oriented Modeling
Sub-workflows / Composite Actors– composite actors “wrap” sub-workflows
– like actors, have signatures (i/o ports of sub-workflow)
– hierarchical workflows (arbitrary nesting levels)
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Actor-Oriented Modeling
Directors– define the execution semantics of workflow graphs– executes workflow graph (some schedule)– sub-workflows may have different directors– enables reusability
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Directed Acyclic Graph (DAG)– Common among Grid workflows: no loops, each actor fires at most
once (no streaming / pipeline parallelism)– Example: DAGMan
Synchronous Dataflow (SDF)– Connections have queues for sending/receiving fixed numbers of
tokens at each firing. Schedule is statically predetermined. SDF models are highly analyzable and used often in SWFs.
Process Networks (PN)– Generalize SDF. Actors execute as a separate thread/process,
with queues of unbounded size. Related to Kahn/MacQueen semantics. The workflow is executed in parallel and pipeline parallel fashion.
Continuous Time (CT)– Connections represent the value of a continuous time signal at
some point in time ... Often used to model physical processes. Discrete Event (DE)
– Actors communicate through a queue of events in time. Used for instantaneous reactions in physical systems. Dynamic Dataflow (DDF)
– Connections have queues for sending/receiving arbitrary numbers of tokens at each firing. Schedule is dynamically calculated. DDF models enable branching and looping/ (conditionals). The workflow is sequential.
…
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tokens, ports have types available types
– int, float (double precision), complex, string, boolean, object– array, record, matrix (2D only)
type resolution at workflow start-up actors can support different types– e.g. Count, Sleep, Delay work on any type
a type lattice is pre-defined to determine relationships among types (casting)
int tokens are added as intsstring and int tokens are added as strings
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Machine monitoring.
• Allow for secure logins with OTP.
• Allow for job submission.
• Allow for killing jobs.
• Search old jobs.• See collaborators
jobs.
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Base analysis which will workon both the portable dashboard and the “mother-dashboard” and will feature.– Calculator for simple math, done in
python.– Hooks into “R” for pre-set functions.– Ability to save the analysis into a
new function, available to otherusers.
– Calculator will create new movies that are viewable on the dashboard.
– First version will work with xy +(t) plots.– Second version will work with x,y,z + (t)
plots.
Advanced analysis will contain.– Parallel backend to VisIT server, VisTrails, Parallel R, and custom mpi/c/f90
code.– We will allow users to place executable code into the dashboard. (Still
working this out). How to execute, ….
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ADIOS is an IO componentization.– ADIOS is being integrated integrated into Kepler.
– Achieved over 20 GB/sec for several codes on Jaguar.
– Used daily by CPES researchers.
– Can change IO implementations at runtime.
– Metadata is contained in XML file.
Kepler is used daily for– Monitoring CPES simulations on Jaguar/Franklin/ewok.
– Runs with 24 hour jobs, on large number of processors.
Dashboard uses enterprise (LAMP) technology.– Linux, Apache, MySQL, PHP
ESMF/V3: [email protected], [email protected], [email protected]/V3: [email protected], [email protected], [email protected] Managed by UT-Battelle
for the Department of Energy
4848
From SDM center*– Workflow engine – Kepler– Provenance support– Wide-area data movement
From universities– Code coupling (Rutgers)– Visualization (Rutgers)
Newly developed technologies– Adaptable I/O (ADIOS)
(with Georgia Tech)– Dashboard (with SDM center)
Visualization
Code Coupling
Wide-areaData Movement
DashboardDashboard
WorkflowWorkflow
Adaptable I/OAdaptable I/O
ProvenanceandMetadata
Foundation Technologies
Enabling Technologies
Approach: place highly annotated, fast, easy-to-use I/O methods in the code, which can be monitored and controlled, have a workflow engine record all of the information, visualize this on a dashboard, move desired data to user’s site, and have everything reported to a database.