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Benjamin Perry and Martin SwanyUniversity of Delaware
Computer Information Science
Background The problem The solution The results Conclusions and Future work
MPI programs communicate via MPI data types
MPI data types are usually modeled after native data types
Payloads are often arrays of MPI data types
The sending MPI library packs payload into contiguous block
The receiving MPI library unpacks payload into original form
Non-contiguous blocks incur a copy penalty SPMD programs, particularly in homogenous
environments, can use optimized packing
Background The problem The solution The results Conclusions and Future work
Users model MPI types after native types Some fields do not need to be transmitted Users often replace dead fields with a gap in
the MPI type to align with native type
Smaller payload…. but MPI type is non-contiguous
◦ Copy penalty during packing and unpacking Multi-core machines and high-performance
networks feel the cost depending on payload
Multi-core machines are becoming ubiquitous◦ SPMD applications are ideal for these platforms
Background The problem The solution The results Conclusions and Future work
Applies only to SPMD applications Static analysis to locate MPI data types
◦ MPI_type_struct() Build internal representation of MPI data
type ◦ MPI data type defined via library call at runtime◦ Parameters indicate base types, consecutive
instances, and displacements◦ Def/use analysis to determine static definition
Look for gaps in displacement array◦ Size of base types multiplied by consecutive array
Match MPI type to native type◦ Analyze the types of the payload◦ MPI type must be subset of native data structure◦ All sends and receives with MPI type handle must
also share same base types
Perform transformation on MPI type and native type◦ Adjust parameters in MPI_type_struct◦ Relocate non-transmitted fields to bottom of type
End goal: improve library performance of packing large arrays
Safety check◦ Cast to a type◦ Address-of
Except for computing displacement◦ Non-local types
Profitability◦ Sends / receives within loops◦ Large arrays of MPI types in sends / receives◦ Cost incurred by cache misses, locality by
adjusting native type when native type is in loops
Background The problem The solution The results Conclusions and Future work
LLVM compiler pass OpenMPI Intel Core2 Quad-core 2.4gz Ubuntu Control: sending un-optimized data type
with gap using payloads of various sizes Tested: Rearranging gap in MPI type and
native type using payloads of various sizes
Background The problem The solution The results Conclusions and Future work
MPI data types modeled after native data types
Users introduce gaps, making data noncontiguous and costly to pack on fast networks
Discover this scenario at compile time Fix it if safe and profitable Greatly improves multi-core performance;
infiniband also receives boost.
Data type fission with user-injected gaps◦ Separate transmitted fields from non-transmitted
fields◦ Complete eliminates data copy during packing
Data type fission with non-used fields◦ Perform analysis on receiving end to see which
fields are actually being used◦ Cull non-used fields from data type; perform
fission
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