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"Workshop 31: Developing a Hands-on Undergraduate Parallel Programming

Course with Pattern Programming

SIGCSE 2013 - The 44th ACM Technical Symposium on Computer Science Education

Saturday  March 9, 2013, 3:00 pm - 6:00 pm

Dr. Barry WilkinsonUniversity of North Carolina Charlotte

Dr. Clayton FernerUniversity of North Carolina

Wilmington

© 2013 B. Wilkinson/Clayton Ferner SIGCSE 2013 Workshop 31 Intro.ppt Modification date: March 1, 2013

Problem Addressed

• To make parallel programming more useable and scalable.

• Parallel programming -- writing programs using multiple computers and processors collectively to solve problems -- has a very long history but still a challenge.

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Traditional approach

• Traditional approach

• Explicitly specifying message-passing (MPI), and

• Low-level threads APIs (Pthreads, Java

threads, OpenMP, …).

• Need a better structured approach.3

Pattern Programming Concept

Programmer begins by constructing his program using established computational or algorithmic “patterns” that provide a structure.

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“Design patterns” part of software engineering for many years:

• Reusable solutions to commonly occurring problems *• Patterns provide guide to “best practices”, not a final

implementation• Provides good scalable design structure• Can reason more easier about programs• Potential for automatic conversion into executable code

avoiding low-level programming – We do that here.• Particularly useful for the complexities of

parallel/distributed computing* http://en.wikipedia.org/wiki/Design_pattern_(computer_science)

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In Parallel/Distributed computingWhat patterns are we talking about?

• Low-level algorithmic patterns that might be embedded into a program such as fork-join, broadcast/scatter/gather.

• Higher level algorithm patterns for forming a complete program such as workpool, pipeline, stencil, map-reduce.

We concentrate upon higher-level “computational/algorithm ” level patterns rather than lower level patterns.

Some Patterns

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Workers

Workpool

Master Two-way connection

Compute node

Source/sink

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Workers

Pipeline

MasterTwo-way connection

Compute node

Source/sink

One-way connection

Stage 1 Stage 3Stage 2

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Divide and Conquer

DivideTwo-way connection

Compute node

Source/sink

Merge

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All-to-All

Two-way connection

Compute node

Source/sink

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Stencil

Two-way connection

Compute node

Source/sink

Usually a synchronous computation

- Performs number of iterations to converge on solutione.g. for solving Laplace’s/heat equation

On each iteration, each node communicates with neighbors to get stored computed values

Parallel Patterns• Advantages

• Possible to create parallel code from the pattern specification automatically – see later.

• Abstracts/hides underlying computing environment• Generally avoids deadlocks and race conditions• Reduces source code size (lines of code)• Hierarchical designs with patterns embedded into

patterns, and pattern operators to combine patterns

• Disadvantages• New approach to learn• Takes away some of the freedom from programmer• Performance reduced slightly(but compare using high

level languages instead of assembly language)11

Previous/Existing Work

• Patterns explored in several projects.• Industrial efforts

– Intel

Threading Building Blocks (TBB), Intel Cilk plus, Intel Array Building Blocks (ArBB).

Focus on very low level patterns such as fork-join

• Universities:– University of Illinois at Urbana-Champaign and University of

California, Berkeley– University of Torino/Università di Pisa Italy

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Our approachFocuses on a few higher level patterns of wide applicability (e.g. workpool, synchronous all-to-all, pipelined, stencil).

Software framework developed called “Seeds” to easily construct an application from established patterns without need to write low level message passing or thread based code.

Will to automatically distribute code across processor cores, computers, or geographical distributed computers and execute the parallel code.

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Acknowledgements

Extending work to teaching environment supported by the National Science Foundation under grant "Collaborative Research: Teaching Multicore and Many-Core Programming at a Higher Level of Abstraction" #1141005/1141006 (2012-2015).

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Work initiated by Jeremy Villalobos in his PhD thesis “Running Parallel Applications on a Heterogeneous Environment with Accessible Development Practices and Automatic Scalability,” UNC-Charlotte, 2011. Jeremy developed “Seeds” pattern programming software.

Next

Session 1Seeds Framework and Workpool

PatternMonte Carlo computation

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Questions