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Parallel Programming Patterns Ralph Johnson

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Why patterns? Patterns for Parallel Programming The road ahead

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Patterns (Software) Things that repeat. Plans/schemas/motifs “Best Practices” Design vocabulary Literature - pedagogical

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Patterns Are not magic Can be misused Not a replacement for experience

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Composite Idea: make abstract "component" class. Alternative 1: every component has a (possibly empty) set of components. Component Children ParagraphChapter... Problem: many components have no components

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Composite Pattern Component container children CompositeLeaf Composite and Component have the exact same interface. interface for enumerating children Component implements children() by returning empty set interface for adding/removing children?

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Lessons learned Patterns are a means to an end Principles are more important than patterns People like to copy code Making mistakes is part of learning

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Patterns for Parallel Programming By Timothy Mattson, Beverly Sanders and Berna Massingill Technology independent – Works for MPI, OpenMP, and Java threads Domain independent A pattern language

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Categories of patterns Finding concurrency Algorithm structure Supporting structures Implementation mechanisms (not patterns)

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Algorithm structure Organize by tasks – Linear - task parallelism - reduce dependencies – Recursive - divide and conquer - manage granularity Organize by data decomposition – Linear - geometric decomposition - exchange – Recursive - recursive data - more work, faster Organize by flow of data – Regular - pipeline – Irregular - event-based coordination

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Supporting Structures SPMD Master/worker Loop parallelism Fork/join Shared data Shared queue Distributed array

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My critique In addition to these high-level patterns – Need more technology-dependent patterns – Need domain-dependent patterns – Need smaller-scale patterns High-level patterns are harder to learn – More examples – Divide into smaller patterns (pattern language)

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Other patterns Patterns at PLoP by Jorge Ortega-Arjona – “Architectural” - similar in abstraction to PPP – Communication primitives Systems that generate software from patterns – Steven Siu at Waterloo – Macdonald and Szafron at U. of Alberta

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Domain Dependent Phil Colella’s 7 dwarfs – Dense and sparse matrices – Structured and unstructured meshes – Particle systems – FFT – Monte Carlo methods Berkeley’s 13 dwarfs/motifs – Graph traversal, branch and bound, dynamic programming, combinatorial logic, FSMs, graphical models

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Particle systems Particle-particle Discrete forces Neighborhood of particles Task per interaction Particle-mesh

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Exchange in MPI Do i=1,n_neighbors Call MPI_Send(edge, len, MPI_REAL, nbr(i), tag, comm, ierr) Enddo Do i=1,n_neighbors Call MPI_Recv(edge,len,MPI_REAL,nbr(i),tag, comm,status,ierr) Enddo

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Provide buffers, receive in any order Do i=1,n_neighbors Call MPI_Irecv(edge,len,MPI_REAL,nbr(i),tag, comm,request(i),ierr) Enddo Do i=1,n_neighbors Call MPI_Send(edge, len, MPI_REAL, nbr(i), tag, comm, ierr) Enddo Call MPI_Waitall(n_neighbors, request, statuses, ierr)

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Defer synchronization Do i=1,n_neighbors Call MPI_Irecv(edge,len,MPI_REAL,nbr(i),tag, comm,request(i),ierr) Enddo Do i=1,n_neighbors Call MPI_Isend(edge, len, MPI_REAL, nbr(i), tag, comm, request(n_neighbors+i), ierr) Enddo Call MPI_Waitall(2*n_neighbors, request, statuses, ierr)

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Parallel Programming Patterns Many levels - all are needed High level patterns are hard to learn – Give many examples – Divide into smaller pieces Low level patterns might be easier to learn, but no less important

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Real patterns are discovered, not invented Quality of pattern observed by using it So, let’s – discover them, – write them, – see what happens when people try to use them, – and then fix them.

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