Automated Mapping of Regular Communication

68 Pages·2010·3.19 MB·English

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Abhinav Bhatele, Gagan Gupta, Laxmikant V. Kale and
I-Hsin Chung
December 20th, 2010
Motivation
•
Running a parallel application on a linear array of
processors:
December 20th, 2010 HiPC 2010 © Laxmikant Kale 2
Motivation
•
Running a parallel application on a linear array of
processors:
December 20th, 2010 HiPC 2010 © Laxmikant Kale 2
Motivation
•
Running a parallel application on a linear array of
processors:
December 20th, 2010 HiPC 2010 © Laxmikant Kale 2
Motivation
•
Running a parallel application on a linear array of
processors:
•
Typical communication is between random pairs of
processors simultaneously
December 20th, 2010 HiPC 2010 © Laxmikant Kale 2
InterconnecBLtU TE oGEpNEo AlRoCgHITieECsTURE
! is designed to support efficient execution of
massively parallel MPI programs
! Compute nodes organized as a 3D-torus
•
Three dimensional meshes
! MAIN FEATURE:
• every node is connected to its
3D Torus: Blue Gene/L, Blue Gene/P, Cray XT4/5
six neighbour nodes through
bidirectional links
•
Trees
! To maintain application performance,
correct mapping of MPI tasks onto
•
Fat-trees (Infiniband) and CLtoOruSs nnetewtowrk ois rak crsit ic(aFl efadcteorration)
•
Dense Graphs
16. Mai 2008 9
•
Kautz Graph (SiCortex), Hypercubes
•
Future Topologies?
•
Blue Waters, Blue Gene/Q
Roadrunner Technical Seminar Series, March 13th 2008, Ken Koch, LANL
December 20th, 2010 HiPC 2010 © Laxmikant Kale 3
Application Topologies
http://wrf-model.org/plots/realtime_main.php http://www.ks.uiuc.edu/Gallery/Science/
Patch
Compute
Proxy
http://oceans11.lanl.gov/twiki/bin/view/Cosim
December 20th, 2010 HiPC 2010 © Laxmikant Kale 4
Application Topologies
http://wrf-model.org/plots/realtime_main.php http://www.ks.uiuc.edu/Gallery/Science/
Patch
Compute
Proxy
We want to map
communicating
objects closer to
one another
http://oceans11.lanl.gov/twiki/bin/view/Cosim
December 20th, 2010 HiPC 2010 © Laxmikant Kale 4
The Mapping Problem
•
Applications have a communication topology and
processors have an interconnect topology
•
Definition: Given a set of communicating parallel
“entities”, map them on to physical processors to
optimize communication
•
Goals:
•
Minimize communication traffic and hence contention
•
Balance computational load (when n > p)
December 20th, 2010 HiPC 2010 © Laxmikant Kale 5
Solution - Mapping Framework
•
Input - communication graph of the application and
processor topology of the allocated job partition
•
Output - mapping of processes/objects to physical
processors
•
Parallel applications can be classified into:
•
regular/structured: n-dimensional near-neighbor (e.g. POP, WRF)
•
irregular: arbitrary communication
•
We focus on regular communication in this paper
December 20th, 2010 HiPC 2010 © Laxmikant Kale 6