1. 1 "Workshop 31: Developing a Hands-on Undergraduate Parallel Programming Course with Pattern Programming SIGCSE 2013 - The 44 th ACM Technical Symposium on Computer Science Education Saturday March 9, 2013, 3:00 pm - 6:00 pm Dr. Barry Wilkinson University of North Carolina Charlotte Dr. Clayton Ferner University of North Carolina Wilmington © 2013 B. Wilkinson/Clayton Ferner SIGCSE 2013 Workshop 31 Intro.ppt Modification date: March 1, 2013

2. 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. 2

3. Traditional approach Explicitly specifying message-passing (MPI), and Low-level threads APIs (Pthreads, Java threads, OpenMP, …). Need a better structured approach. 3

4. Pattern Programming Concept Programmer begins by constructing his program using established computational or algorithmic “patterns” that provide a structure. 4 “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)

5. 5 In Parallel/Distributed computing What 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.

6. Some Patterns 6 Workers Workpool Master Two-way connection Compute node Source/sink

7. 7 Workers Pipeline Master Two-way connection Compute node Source/sink One-way connection Stage 1Stage 3Stage 2

8. 8 Divide and Conquer Divide Two-way connection Compute node Source/sink Merge

9. 9 All-to-All Two-way connection Compute node Source/sink

10. 10 Stencil Two-way connection Compute node Source/sink Usually a synchronous computation - Performs number of iterations to converge on solution e.g. for solving Laplace’s/heat equation On each iteration, each node communicates with neighbors to get stored computed values

11. 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

12. 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 12

13. Our approach Focuses 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. 13

14. 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.

15. Next Session 1 Seeds Framework and Workpool Pattern Monte Carlo  computation 15

16. Questions