1. Pattern Parallel Programming
B. Wilkinson PatternProgIntro.ppt Modification date: Feb 21, 2016 1 1

2. Traditional programming approach
Explicitly specify message-passing (MPI)
Low-level threads APIs (Pthreads, Java threads, OpenMP, …).
Both require programmers to use low-level routines
Need to make parallel programming easier, more structured and more scalable, especially in an educational environment 2

3. Pattern Programming Concept
Programmer begins by constructing his program using established computational or algorithmic “patterns” that provide a structure.
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
Avoids common problem with ad-hoc designs
Can reason more easily about programs and debug *

4. Parallel Patterns -- Advantages
Abstracts/hides underlying computing environment
Generally avoids deadlocks and race conditions
Reduces source code size (lines of code)
Leads to automated conversion into parallel programs without need to write with low level MPI message-passing routines.
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 (c.f. using high level languages instead of assembly language)

5. What parallel design patterns are we talking about?
Low-level patterns:
fork-join
point-to point
broadcast
scatter
gather, reduce, ...
Higher level patterns for forming a complete computation:
master-slave
workpool,
pipeline
divide and conquer
stencil
map-reduce, ...

6. MPI point-to-point Data Transfer (Send-Receive)
Low level MPI
message-passing patterns
MPI point-to-point Data Transfer (Send-Receive)
Source
Destination
Data

7. Collective patterns Broadcast Pattern
Sends same data to each of a group of processes. A common pattern to get same data to all processes, especially at beginning of a computation
Destinations
Same data
sent to all destinations
Source
Note: Patterns given do not mean the implementation does them as shown. Only the final result is the same in any parallel implementation. Patterns do not describe the implementation.

8. Scatter Pattern
Distributes a collection of data items to a group of processes.
A common pattern to get data to all processes.
Usually data sent are parts of an array
Different data
sent to each destinations
Source
Destinations

9. Gather pattern
Sources
Destination
Essentially reverse of scatter pattern. It receives data items from a group of processes
Data
Data
Data collected at destination in an array
Data
Common pattern especially at the end of a computation to collect results.

10. Reduce Pattern
Sources
A common pattern to get data back to master from all processes and then aggregate it by combining collected data into one answer.
Destination
Data
Reduction operation
Data
Data collected at destination and combined to get one answer with a commutative operation
Reduction needs to be associative operation (e.g. 3 + (4 + 5) = (3 + 4) + 5) to allow the implementation to do the operations in any order.
Also being communicative (e.g = 4 + 3) allows more flexibility in the parallel implementation.
Data
Note subtraction is not associative e.g. 3 – (4 – 5) != (3 – 4) – 5 but one can use addition with negative numbers

11. Collective all-to-all broadcast
Sources and destinations are the same processes
Sources
Destinations
A common all-to-all pattern, often within a computation, is to send data from all processes to all processes
Every process sends data to every other process (one-way)
Versions of this can be found in MPI.

12. Some Higher Level Message-Passing Patterns
Slaves
Master/slave
Master
Two-way connection
Computation divided into parts, which are then passed out to slaves to perform and return their results, basis of most parallel computing
Compute node
Source/sink

13. Workpool Very widely applicable pattern Slaves/Workers
Master
Task from task queue
Another task if task queue not empty
Aggregate answers
Slaves/Workers
Result
Task queue
Very widely applicable pattern
Once a slave completes a task, slave given another task from task queue master -- load-balancing quality. Need to differentiate between master-slave pattern, which does not imply a task queue, and workpool with task queue.

14. More Specialized High-level Patterns
Pipeline
Stage 1
Stage 2
Stage 3
Stage n
Slaves (workers)
One-way connection
Master
Compute node
Source/sink

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

16. All-to-All All compute nodes can communicate with all the other nodes
Two-way connection
Compute node
Source/sink
Master

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

18. Iterative synchronous patterns
When a pattern is repeated until some termination condition occurs.
Synchronization at each iteration, to establish termination condition, often a global condition.
Note this is two patterns merged together sequentially if we call iteration a pattern.
Pattern
Check termination condition
Repeat
Stop

19. Iterative synchronous stencil pattern
Stencil: All compute nodes can communicate with only neighboring nodes
Applications:
Solving Laplace’s/heat equation - perform number of iterations to converge on solution.
Repeat
Check termination condition
Stop 19

20. Iterative synchronous all-to-all pattern
Repeat
Stop
Check termination condition
Example: N-body problem needs an “iterative synchronous all-to-all” pattern, where on each iteration all processes exchange data with each other. 20

21. Previous/Existing Work
Patterns explored in several projects.
Industrial efforts
Intel Threading Building Blocks (TBB), Cilk plus, 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
“Structured Parallel Programming: Patterns for Efficient Computation,” Michael McCool, James Reinders, Arch Robison, Morgan Kaufmann, 2012
Intel tools, TBB, Cilk, ArBB

22. Our approach
We have developed several tools at different levels of abstraction that avoid using low level MPI and enable students to create working patterns very quickly.
Suzaku framework – provides pre-written pattern-based routines and macros that hide the MPI code. Low level patterns, workpool, ... .
Paraguin compiler – Compiler directive approach that creates MPI code. Patterns implemented include scatter-gather for a master slave pattern, stencil, …
Seeds framework – high-level Java-based software. Many patterns implemented including workpool, pipeline, synchronous iterative all-to-all, stencil. Self deploys and executes on any platform, local computers or distributed computers
Historical Seeds was developed first as part of a UNC-C PhD project by Jeremy Villalobos,

23. Acknowledgements
The Seeds framework was developed by Jeremy Villalobos in his PhD thesis “Running Parallel Applications on a Heterogeneous Environment with Accessible Development Practices and Automatic Scalability,” UNC-Charlotte, 2011.
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" # / ( ).
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. 23

24. Questions