scientific computing group
DESCRIPTION
Scientific Computing Group. Ricky A. Kendall Scientific Computing Center for Computational Sciences. Center for Computational Sciences/ Leadership Computing Facility A. Bland, Director L. Gregg, Division Secretary. Operations Council A. Stewart, Quality Assurance T. Jones, Cyber Security - PowerPoint PPT PresentationTRANSCRIPT
Presented by
Scientific Computing Group
Ricky A. Kendall
Scientific Computing
Center for Computational Sciences
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Cray Supercomputing
Center for ExcellenceJ. LevesqueT. Darland
L. DeRose4
D.Kiefer4
J. Larkin4
N. Wichmann4
Scientific ComputingR. Kendall
L. Rael
S. Ahern## V. Lynch5
S. Alam5 B. MesserE. Apra5 R. MillsR. Barrett5 G.Ostrouchov5
R. Barretto2 R. SankaranD. Banks3 A. TharringtonJ. Daniel R. ToedteM. Fahey T. WhiteS. Klasky#
J. Kuehn5
#End-to-End Solutions Lead## Viz Task Lead
User Assistanceand Outreach
J. WhiteL. Rael
Y. DingD. FrederickC. FusonC. Halloy3
S. HempflingM. HenleyJ. HinesS. Jones6
B. RenaudB. WhittenL. Williams5
K. Wong3
High-PerformanceComputing Operations
A. BakerL. Gregg*
M. Bast J. Lothian J. Becklehimer4 D. Maxwell##
J. Breazeale6 M. McNamara4
J. Brown6 J. Miller6
A. Enger4 G. Phipps, Jr.6
C. England G. PikeJ. Evanko4 V. Rothe#
M. Griffith S. Shpanskiy V. Hazlewood5 D. VasilT. Jones C. Willis4
C. Leach6 T. Wilson6
D. Londo4 S. White
#Team Lead, Computer Ops##Technical Coordinator
Technology IntegrationS. CanonL. Gregg*
T. BarronS. Carter##
K. MatneyM. MinichS. OralD. SteinertF. WangV. WhiteW. Yu5
## Networking Lead
Site PreparationK. Dempsey
Hardware AcquisitionA. Bland*
Test and AcceptanceDevelopmentS. Canon
CommissioningA. Baker
Project ManagementD. Hudson5
Project R & DA. Geist
Cray Proj. DirectorK. Kafka4
Operations CouncilA. Stewart, Quality AssuranceT. Jones, Cyber SecurityM. Dobbs, Facility Mgmt.W. McCrosky, Finance OfficerW. Besancenez, ProcurementM. Palermo, HR Mgr.K. Johnson, RecruitingR. Toedte, Safety & HealthN. Wright, Org. Mgmt. Specialist
Center for Computational Sciences/
Leadership Computing FacilityA. Bland, Director
L. Gregg, Division Secretary
Deputy Project Director
K. Boudwin
Director for ScienceD. Kothe
Director of OperationsD. Kothe*
Chief Technology OfficerA. Geist
LCF System ArchitectS. Poole
Advisory CommitteeJ. Bernholc S. KarinT. Dunning D. KeyesJ. Dongarra D. ReedK. Droegemeier T. SterlingJ. Hack W. Washington
1Student2Post Graduate3JICS4Cray, Inc.5Matrixed6Subcontract*InterimNew position within ORNLNew hire at ORNL
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Scientific computingTasks and deliverables Project partnerships in science, visualization, workflow, and data analytics
Level of integration is dependent upon the project High-level user assistance
Main staff function. Whatever it takes! Algorithmic development at scale
For scientific codes, visualization and workflow Application and library code optimization and scaling
How to use the LCF resources for science User voice within LCF planning exercises
Users are the focus Requirements gathering, documentation
What’s next for our users and our center Exploiting parallel I/O & other technologies in applications
Specific focus for targeted applications Acquiring & deploying viz and end-to-end systems
Infrastructure building Benchmarking and evaluation exercises with Vendor interaction
Learning how to best use current and new LCF resources
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Partnership with projects on LCF resources
Porting/TuningLibrary utilizationCompiler flags
OptimizationsChoice of Tools
Code Designand
algorithmic developments
@scale
Shared R&D Staff
Pilot End StationsProjects
Le
vel o
f in
teg
rati
on
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High-level user assistance
ProjectProject
Liaison End-to-End
Visualization
Compute
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Algorithmic and programming model development at scale Complex Tradeoffs for applications
Usually among storage, computation and communication
Redesign new methods Fixed grid to AMR grids
Programming model changes Message passing to one-sided model transformation
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Application and library optimization and scaling Applications for the LCF systems
Catamount issues for Apps Dual Core/Single core settings Compiler flags for optimal performance (Application/Library specific) Modifications for scaling
Libraries Built and kept up-to-date HDF5, PETSc, FFTW, etc.
Application
HDF5
PETSc
0 1024 2048 3072 4096 5120 6144 7168 8192
Number of Cores
0
1024
20483072
4096
5120
6144
7168
8192
Before
After
Ideal
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Requirements gathering and documentation Participate in Applications Requirements Council (ARC) Determine requirements for applications on the machine
Algorithmic Programming models Architectural Infrastructure
Develop ties to applications areas not currently at scale on the machine Work with developers to understand application properties
Memory footprint Algorithmic scalability Potential for future resources
Collela’s 7 Dwarfs Anaylsis The 7 algorithm types are scattered broadly among science domains, with no one
particular algorithm being ubiquitous and no one algorithm going unused; Structured grids and dense linear algebra algorithms are the most widely used
algorithms (by over half of the representative codes), hence system attributes such as node peak flops and memory capacity, memory latency, and interconnect latency will be important;
Particle-based and Monte Carlo algorithms, which have similar properties from a system standpoint, are also broadly used, and can tax interconnect latency and in some cases node memory capacity, depending upon implementation and usage.
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Collela’s 7 Dwarfs analysisScienceDomain Code Structured
GridUnstructured
Grids FFTDenseLinear
Algebra
SparseLinear
AlgebraParticle Monte
Carlo
Accelerator Physics T3P X X
Astrophysics CHIMERA X X X X
Astrophysics FLASH X X X X X
Biology LAMMPS X X
Chemistry MADNESS X X
Chemistry NWCHM X X
Chemistry OReTran X X X
Climate CAM X X X
Climate POP/CICE X X X
Combustion S3D X
Fusion AORSA X X X
Fusion GTC X X X X
Geophysics PFLOTRAN X X X
Materials Science QMC/DCA X X
Materials Science QBOX X X X
Nanoscience CASINO X X X
Nanoscience LSMS X X
Nuclear Energy NEWTRNX X X X
Nuclear Physics CCSD X
QCD MILC X X
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System attribute priority per science domain
System Attribute Climate Astrophysics Fusion Chemistry Combustion Accelerator Physics Biology Materials
Science
Node Peak Flops
Mean Time to Interrupt (MTTI)
WAN Network Bandwidth
Node Memory Capacity
Local Storage Capacity
Archival Storage Capacity
Memory Latency
Interconnect Bandwidth
Disk Latency
Interconnect Bandwidth
Memory Bandwidth
Disk Bandwidth
Highest Medium Lowest
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Contact
Ricky A. KendallScientific ComputingCenter for Computational Sciences(865) [email protected]
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