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Unlock the Power of High-Performance Computing and CyberGIS

Explore the world of supercomputing, data analysis, and advanced computing opportunities. Learn about parallel processing, cluster computing, and the applications of supercomputers in data mining, simulation, and more.

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Unlock the Power of High-Performance Computing and CyberGIS

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  1. High Performance Computing and CyberGIS Keith T. Weber, GISP GIS Director, ISU

  2. Goal of this presentation • Introduce you to an another world of computing, analysis, and opportunity • Encourage you to learn more!

  3. Some Terminology Up-Front • Supercomputing • HPC • HTC • CI

  4. Acknowledgements • Much of the material presented here, was originally designed by Henry Neeman at Oklahoma University and OSCER

  5. What is Supercomputing? • Supercomputing is the biggest, fastest computing right this minute. • Likewise, a supercomputeris one of the biggest, fastest computers right this minute. • So, the definition of supercomputing is constantly changing. • Rule of Thumb: A supercomputer is typically 100 X as powerful as a PC.

  6. Fastest Supercomputer and Moore’s Law

  7. What is Supercomputing About? Size Speed Laptop

  8. Size… • Many problems that are interesting to scientists and engineers can’t fit on a PC • usually because they need more than a few GB of RAM, or more than a few 100 GB of disk.

  9. Speed… • Many problems that are interesting to scientists and engineers would take a long time to run on a PC. • months or even years. • But a problem that would take 1 month on a PC might take only a few hours on a supercomputer

  10. What can Supercomputing be used for? • Data Mining • Modeling • Simulation • Visualization [1]

  11. What is a Supercomputer? • A cluster of small computers, each called a node, hooked together by an interconnection network (interconnect for short). • A cluster needs software that allows the nodes to communicate across the interconnect. • But what a cluster is … is all of these components working together as if they’re one big computer ... a super computer.

  12. For example: Dell Intel Xeon Linux Cluster • 1,076 Intel Xeon CPU chips/4288 cores • 8,800 GB RAM • ~130 TB globally accessible disk • QLogicInfiniband • Force10 Networks Gigabit Ethernet • Red Hat Enterprise Linux 5 • Peak speed: 34.5 TFLOPs* • *TFLOPs: trillion floating point operations (calculations) per second sooner.oscer.ou.edu

  13. Quantifying a Supercomputer • Number of cores • Your workstation (4?) • ISU cluster (800) • Blue Waters (300,000) • TeraFlops

  14. How a cluster works together: Parallelism

  15. Parallelism Parallelism means doing multiple things at the same time More fish! Less fish …

  16. Understanding Parallel Processing The Jigsaw Puzzle Analogy

  17. Serial Computing • We are very accustom to serial processing. It can be compared to building a jigsaw puzzle by yourself. • In other words, suppose you want to complete a jigsaw puzzle that has 1000 pieces. • We can agree this will take a certain amount of time…let’s just say, one hour

  18. Shared Memory Parallelism • If Scott sits across the table from you, then he can work on his half of the puzzle and you can work on yours. • Once in a while, you’ll both reach into the pile of pieces at the same time (you’ll contend for the same resource), which will cause you to slowdown. • And from time to time you’ll have to work together (communicate) at the interface between his half and yours. The speedup will be nearly 2-to-1: Together it will take about 35 minutes instead of 30.

  19. The More the Merrier? • Now let’s put Paul and Charlie on the other two sides of the table. • Each of you can work on a part of the puzzle, but there’ll be a lot more contention for the shared resource (the pile of puzzle pieces) and a lot more communication at the interfaces. • So you will achieve noticeably less than a 4-to-1 speedup. • But you’ll still have an improvement, maybe something like 20 minutes instead of an hour.

  20. Diminishing Returns • If we now put Dave, Tom, Horst, and Brandon at the corners of the table, there’s going to be a much more contention for the shared resource, and a lot of communication at the many interfaces. • The speedup will be much less than we’d like; you’ll be lucky to get 5-to-1. • We can see that adding more and more workers onto a shared resource is eventually going to have a diminishing return.

  21. Amdahl’s Law CPU utilization Source: http://codeidol.com/java/java-concurrency/Performance-and-Scalability/Amdahls-Law/

  22. Distributed Parallelism • Let’s try something a little different. • Let’s set up two tables • You will sit at one table and Scott at the other. • We will put half of the puzzle pieces on your table and the other half of the pieces on Scott’s. • Now you can work completely independently, without any contention for a shared resource. • BUT, the cost per communication is MUCHhigher, and you need the ability to split up (decompose) the puzzle correctly, which can be tricky.

  23. More Distributed Processors • It’s easy to add more processors in distributed parallelism. • But you must be aware of the need to: • decompose the problem and • communicate among the processors. • Also, as you add more processors, it may be harder to load balance the amount of work that each processor gets.

  24. FYI…Kinds of Parallelism • Instruction Level Parallelism • Shared Memory Multithreading • Distributed Memory Multiprocessing • GPU Parallelism • Hybrid Parallelism (Shared + Distributed + GPU)

  25. Why Parallelism Is Good • The Trees: We like parallelism because, as the number of processing units working on a problem grows, we can solve the same problem in less time. • The Forest: We like parallelism because, as the number of processing units working on a problem grows, we can solve bigger problems.

  26. Jargon • Threads are execution sequences that share a single memory area • Processes are execution sequences with their own independent, private memory areas • Multithreading: parallelism via multiple threads • Multiprocessing: parallelism via multiple processes • Shared Memory Parallelism is concerned with threads • Distributed Parallelism is concerned with processes.

  27. Basic Strategies • Data Parallelism: Each processor does exactly the same tasks on its unique subset of the data • jigsaw puzzles or big datasets that need to be processed now! • Task Parallelism: Each processor does different tasks on exactly the same set of data • which algorithm is best?

  28. An Example: Embarrassingly Parallel • An application is known as embarrassingly parallel if its parallel implementation: • Can straightforwardly be broken up into equal amounts of work per processor, AND • Has minimal parallel overhead (i.e., communication among processors) FYI…Embarrassingly parallel applications are also known as loosely coupled.

  29. Monte Carlo Methods • Monte Carlo methods are ways of simulating or calculating actual phenomena based on randomness within known error limits. • In GIS, we use Monte Carlo simulations to calculate error propagation effects • How? • Monte Carlo simulations are typically, embarrassingly parallel applications.

  30. Monte Carlo Methods • In a Monte Carlo method, you randomly generate a large number of example cases (realizations), and then compare the results of these realizations • When the average of the realizations converges that is, your answer doesn’t change substantially if new realizations are generated, then the Monte Carlo simulation can stop.

  31. Embarrassingly Parallel • Monte Carlo simulations are embarrassingly parallel, because each realization is independent of all other realizations

  32. A Quiz… • Q: Is this an example of Data Parallelism or Task Parallelism? • A: Task Parallelism: Each processor does different tasks on exactly the same set of data

  33. Questions so far?

  34. A bit more to know… What is A GPGPU?Or thank you gaming industry

  35. It’s an Accelerator No, not this ....

  36. Accelerators • In HPC, an accelerator is hardware whose role it is to speed up some aspect of the computing workload. • In the olden days (1980s), PCs sometimes had floating point accelerators (aka, the math coprocessor)

  37. Why Accelerators are Good • They make your code run faster.

  38. Why Accelerators are Bad Because: • They’re expensive (or they were) • They’re harder to program (NVIDIA CUDA) • Your code may not be portable to other accelerators, so the labor you invest in programming may have a very short life.

  39. The King of the Accelerators The undisputed king of accelerators is the graphics processing unit (GPU).

  40. Why GPU? • Graphics Processing Units (GPUs) were originally designed to accelerate graphics tasks like image rendering for gaming. • They became very popular with gamers, because they produced better and better images, at lightning fast refresh speeds • As a result, prices have become extremely reasonable, ranging from three figures at the low end to four figures at the high end.

  41. GPU’s Do Arithmetic • GPUs render images • This is done through floating point arithmetic – As it turns out, this is the same stuff people use supercomputing for!

  42. Interested? Curious? • To learn more, or to get involved with supercomputing there is a host of opportunities awaiting you • Get to know your Campus Champions • Ask about internships (BWUPEP) • Learn C (not C++, but C) or Fortran • Learn UNIX

  43. Questions?

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