1. Parallel Computing Scenarios
and the new challenges for the Software Architect
Fabrizio Giudici Emmanuele Sordini
Tidalwave s.a.s. BloomingStars.com
880

2. 2
Goals of this presentation
> Parallel/distributed computing has been around for decades
– Mainly in some niches (science, military, finance)
– Seti@Home, BOINC, ... paved the way for the rest of the world
> It's going to become important for “general” software architects
– Because of recent technology evolution
– From (maybe) scared customers to new business opportunities
> Learn how to design WORA parallel applications
– Exploiting parallelism in different scenarios
– Good design, patterns, technologies

3. 3
AGENDA
> MOTIVATIONS
> CASE STUDY
> ARCHITECTURE
> CONCLUSION

4. 4
Motivations
> User's need grow
– Demand for increased
computing power
– Multimedia processing
– Also on the desktop
> Technological challenges
– Multi-core computers (10s of cores from Computer Architecture,
in a few years, clock stable or a Quantitative approach:
Patterson & Hennessy, 2006
decreasing)
> New opportunities
– Easy “local mini grids“ (e.g Jini, Rio)
– Massive grid computing as a
service (e.g. Sun Grid)

5. 5
Jini, Rio, Sun Grid
> Jini
– A Java technology for creating “federations of services“
– Spontaneous networking, discovery, mobile code
– http://www.jini.org
> Rio
– Based on Jini
– Adds container / component paradigm, Quality of Service, etc...
– http://rio.dev.java.net
> Sun Grid
– A massive (1.000s nodes) grid platform accessible as a service
– $1 / CPU / hour
– Since May 2007 available in 24 countries outside USA
– http://www.network.com

6. 6
Parallel computing: easy or hard?
> Harder than single-thread computing
> Largely depends on the context
– Course grain
– Fine grain
> Future language enhancements?
– Java syntax extensions
– New languages
– Virtual Machine optimizations (transparent to code?)
> Let's focus on what we have now
– Design
– Architecture
– Patterns

7. 7
An important point: ROI
> In the past distributed computing required ad-hoc hardware
– Highly expensive
– High start-up costs
– Strive for high parallelism exploitation to justify costs
 50% efficiency with 100.000€ expense means 50.000€ wasted
> Today is different
– Local “mini grids” can be easily set up with standard hardware
– Large facilities available as a service on a “pay-per-hour” basis
> A different ROI policy
– No or negligible start-up costs
– Probably you can live with medium efficiency in parallelism exploitation
 50% efficiency on the Sun Grid means $1 per hour wasted

8. 8
AGENDA
> MOTIVATIONS
> CASE STUDY
> ARCHITECTURE
> CONCLUSION

9. 9
Case study
> Concrete problem and potential community
– To be addressed with state-of-the art design
– Focused on image processing, but many things are general purpose
> Different scenarios considered
– Single core, multi-core
– “Local mini grids” made with Jini and Rio
– Sun Grid
> Mistral – the imaging framework
– http://mistral.tidalwave.it
> Pleiades – the application
– http://pleiades.bloomingstars.com

10. 10
Hires imaging of solar system bodies
> For decades only with pro equipment
> Since 90s within the reach of amateurs
– availability of decent quality optics
– good cameras at reasonable prices
> Key concept
– take multiple exposures
– stack and align them
– improve S/N ratio by averaging
> Bottom line: consumer's perspective fit with the scenario
– Recall previous slide about users and computing power

11. 11
Algorithm

12. 12
Parallel decomposition

13. 13
AGENDA
> MOTIVATIONS
> CASE STUDY
> ARCHITECTURE
> CONCLUSION

14. 14
Mistral
> Abstract Imaging layer
– Wraps Java2D, JAI, ImageJ, others
> Flexible
– Operations and/or engines can be plugged in
> Versatile Imaging Processor
– Based on the Master/Worker pattern
> Built-in support for profiling
– Benchmarking is a must

15. 15
Mistral ImageProcessor
> Master / Worker pattern
> Polymorphic implementation
– Local (multi core)
– Rio
– Sun Grid
> Images wrapped in an opaque
container (EditableImage)

16. 16
Phase controller
reference = createReference();
for each image
{
schedule new RegisterTask(image, reference);
}
when (at least 2 RegisterTasks completed)
{
image1 = registerTask1.getResult();
image2 = registerTask2.getResult();
schedule new AddTask(image1, image2);
}
when (at least 2 AddTasks completed)
{
image1 = addTask1.getResult();
image2 = addTask2.getResult();
schedule new AddTask(image1, image2);
}
// Detects last phase
when (no more pending tasks && only 1 completed AddTask has not been processed)
{
sumImage = addTask.getResult();
normalize(sumImage);
return;
}

17. 17
Best practices
> Fallacies #2, #3 of distributed computing
– “Latency is zero”
– “Bandwidth is infinite”
> In other words
– Don't pretend the network is not there
– Serialization overhead is an issue
> Memory is also a problem
– Memory is not infinite
– Typical of imaging or large-datasets problems
– Was not an issue with sequential processing
 Queuing intermediate results vs process one by one

18. 18
Adapt to scenarios
> Consume results as soon as possible
– Avoid accumulation of idle results in queues
– Post tasks with priority
> Adapt the most to the environment capabilities
– Synthetic images are re-computed at each node
– Filesystem-based exchange is used when NFS is available (e.g. Sun Grid)
– Distributed image cache otherwise
– Optimized routing

19. 19
EditableImage serialization
> Opaque holder of the real image
> Each instance has its own unique UUID
– Serializable – but only moves UUID around
> NFS implementation
– Each serialized image gets stored on the disk
– Upon use on a remote node, it is loaded from the disk
> Otherwise
– Images are pulled from a distributed cache
– Pull vs push approach for cache

20. 20
Optimized routing
> Optimize scheduling to minimize data motion
– Multi-phase: the operands come from a previous phase
– For instance, while adding two images
> The scheduler
– Queries the pending Task about the needed images
– Looks up the distributed image cache
– Finds the node where most of needed images are
– Schedule the task to that node

21. 21
AGENDA
> MOTIVATIONS
> CASE STUDY
> ARCHITECTURE
> CONCLUSION

22. 22
Conclusion
> Parallel computing is coming among us
– Hard, but in reach
– It's an opportunity, not a scary thing
> In different fashions
– Multi core
– Local mini grids (Jini, Rio)
– Massive grids as a facility (Sun Grid)
> Can be dealt with more patterns in our catalog
> You don't need always the “optimal” solution
– Just pull most out of the CPUs with trade-off design

23. 23
Desktop too – and demo
> Mistral and Pleiades
integrated in blueMarine, a
desktop photo application
> Demos at Jazoon:
– “blueMarine - a desktop
app for the open source
photographic workflow“
– Tuesday, 2007-06-26,
12:00 - 12:50, Arena 2
– Thursday, 2007-06-28,
14:00 - 14:50, Arena 1

24. 24
Q&A
> Question time

25. Fabrizio Giudici www.tidalwave.it
Tidalwave s.a.s fabrizio.giudici@tidalwave.it
Emmanuele Sordini www.bloomingstars.com
emmanuele@sordini.com