Ilya Baldin RENCI UNC Chapel Hill Networked Clouds Cloud and Network Providers Observatory Wind tunnel Science Workflows ExoGENI Testbed ComputationalData Science Projects on ExoGENI ID: 797102
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Slide1
Using GENI for computational science
Ilya
Baldin
RENCI, UNC – Chapel Hill
Slide2Networked Clouds
Cloud and Network Providers
Observatory
Wind tunnel
Science Workflows
Slide3ExoGENI
Testbed
Slide4Computational/Data Science Projects on ExoGENI
ADAMANT – Building tools for enabling workflow-based scientific applications on dynamic infrastructure (RENCI, Duke, USC/ISI)
RADII – Building tools for supporting collaborative data-driven science (RENCI)
GENI
ScienceShakedown
– ADCIRC storm surge modeling on GENIGoal of presentation to demonstrate some of the things that are possible with GENI today
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Slide5ADAMANT
Presentation title goes here
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Slide6Scientific Workflows – Dynamic Use Case
Presentation title goes here
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Slide7CC-NIE ADAMANT – Pegasus/ExoGENI
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Network Infrastructure-as-a-Service (
NIaaS
) for workflow-driven applications
Tools for workflows integrated with adaptive infrastructure
Workflows triggering adaptive infrastructure
Pegasus workflows using
ExoGENI
Adapt to application demands (compute, network, storage)
Integrate data movement into
NIaaS
(on-ramps)
Target applications
Montage Galactic plane ensemble: Astronomy mosaics
Genomics: High-Throughput Sequencing
Slide88
ExoGENI
: Enabling Features for Workflows
On-Ramps /
Stitchports
Connect
ExoGENI
to existing static infrastructure to import/export
Storage slivering
Networked storage:
iSCSI
target on
dataplane
Neuca
tools attach
lun
, format and
mount filesystem
Inter-domain links, multipoint broadcast networks
Slide9Computational workflows in Genomics
Several
versions as we scaled:
S
ingle
machine
C
luster
basedMapSeq: specialized code & CondorPegasus & Condor
RNA-
Seq
WGS
Slide1010
VM
VM
VM
VM
VM
VM
VM
VM
VM
VM
VM
VM
VM
VM
VM
VM
VM
VM
VM
Cloud providers (compute, data)
Goal: learning to use NIaaS for biomedical research
VM
VM
Slice 1
VM
VM
VM
Slice 2
VM
User or workflow provisioned & isolated slices
VM
VM
VM
VM
Network providers
Slide11Goal: Management of data flows in NIaaS
RENCI
UNC
iRODS Data Grid
iCAT
RE
RE
VM
VM
VM
Slice 2
VM
Layer 2 connection within the slice
Metadata control
Lab X can compute on Project Y data in the cloud
User X can move data from Study A to the cloud
Data from Study W cannot remain on cloud resources
Ease of access
Control over access
Auditing
Provenance
Slide1212
Example
ExoGENI
requests auto-generated
Slide13Application to
NIaaS
- Architecture
Slide14RADII
Presentation title goes here
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Slide15RADII
RADII: Resource Aware Data-centric Collaboration Infrastructure
Middleware to facilitate data
-driven collaborations
for
domain researchers and a commodity to the science communityReducing the large gap between procuring
the required infrastructure and manage data transfers efficientlyIntegration of data-grid (
iRODS) and NIaaS (ORCA) technologies on ExoGENI infrastructure Novel tools to
map data processes, computations, storage and organization entities onto infrastructure with intuitive GUI based application Novel data-centric resource management mechanisms for provisioning and de-provisioning
resources dynamically through out the lifecycle of collaborations
Slide16Why iRODS in RADII?
RADII Policies to
iRODS
Rule Language
Easy to map policies to
iRODS Dynamic PEPReduced complexity for RADII
Distributed and Elastic Data Grid Resource Monitoring FrameworkGeo-aware Resource hierarchy creation via composable
iRODSMetadata tagging
Slide17Resource Awareness
iRODS
RMS provides node specific resource utilization
End-to-End parameters such as throughput, current network flow is important for judicious placement, replication and retrieval decision
Created end-to
-end Throughput, Latency and instantaneous transfer RX/TX per second monitoring.The best server selection based on end-to-end utility value:
Slide18Experiment Topology
Figure: Experimental Setup Topology
Slide19Experimental Setup
The sites were : UCD, SL, UH,
FIU
Parallel and multithreaded file ingestion from each of the clients
Total 400GB file ingestion from each client
One copy at the edge node and another replication based on utile value.
Slide20Edge Put and Remote Replication Time
Figure: Edge Node Put Time
Figure: Remote Replication Time
Slide21ScienceShakedown
Presentation title goes here
21
Slide22Motivation
Hurricane Sandy (2012)
Slide23Motivation
Real-time, on-demand computations of storm surge impacts
Hazards
to coastal areas a major concern
Hazard/Threat Information needed ASAP (
Urgently
)
Critical need for:
detailed high spatial resolution
large compute resources
Federal Forecast cycle every 6 hrsMust be
well within Cycle to be relevant/useful
I.e., New information at 5:59 is already old!!!
Slide24Computing Storm Surge
ADCIRC
Storm Surge Model
FEMA-approved for Coastal Flood Insurance Studies
Very high spatial resolution (
millions
of triangles)
Typically use
256-1024 cores for real-time (one simulation!)
ADCIRC grid for coastal North Carolina
Slide25Tackling Uncertainty
Research Ensemble
NSF Hazards SEES project
22 members
, H. Floyd (1999)
One simulation is NOT enough!
Probabilistic Assessment of Hurricanes
A “few” likely hurricanes
Fully dynamic atmosphere (WRF)
Slide26Why GENI?
Current limitations: Real-time demands for compute resource
Large demands for real-time compute resources during storms
Not enough demand to dedicate a cluster year-round
Slide27Why GENI?
Current limitations: Real-time demands for compute resource
Large demands for real-time compute resources during storms
Not enough demand to dedicate a cluster year-round
GENI enables
Federation of resources
Cloud bursting, urgent, on-demand
High-speed data transfers to/from/between remote resourcesReplicate data/compute across geographic areas
Resiliency, performance
Slide28Storm Surge Workflow
Parallel task (32 Core MPI)
Each ensemble member is a high-performance parallel
task that calculates one storm
Compute
Core
Compute
Core
Compute
Core
Compute
Core
Compute
Core
Compute
Core
Compute
Core
Compute
Core
Compute
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Compute
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Compute
Core
Compute
Core
Compute
Core
Compute
Core
Compute
Core
Compute
Core
Compute
Core
Compute
Core
Compute
Core
Compute
Core
Compute
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Compute
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Compute
Core
Compute
Core
Compute
Core
Compute
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Core
Slide29Slice Topology
11 GENI sites (1 ensemble manager, 10 compute sites)
T
opology: 92 VMs (368 cores), 10 inter-domain VLANs, 1 TB
iSCSI
storage
HPC compute nodes: 80 compute nodes (320 cores
) from 10 sites
Slide30ADCIRC Results from GENI
Storm Surge
for 6 simulations
N11
N17
N01
N14
N16
N20
Small Threat
Big Threat
Slide31Conclusions
GENI testbed represents a kind of shared infrastructure suitable for prototyping of solutions for some computational science domains
GENI technologies represent a collection of enabling mechanisms that can provide foundation for the future federated science cyberinfrastructure
Different members of GENI federations offer different capabilities for their users, suitable for a variety of problems
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Slide32Thank you!
Funders
Partners
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