High-Performance Computing
using Campus Cluster
Please navigate to
uiuc-cse.github.io/hpc-sp14
CSE Training • cse.illinois.edu/training/
Agenda—Day 1
- Campus Cluster background
-
Logging in and first look
-
Shells & architecture, environment
-
What is HPC?
-
Strategies
-
Architecture
- Performance
-
Running code on a cluster
- Queueing
- Campus Cluster operations model
- mpiexec
Cluster Elements
HPC Strategies
vectorization
single operation across multiple data
distributed computing
multiple units on multiple data for same overall process
local threading
context-switching processes operating over
multiple control flows or multiple data
HPC Architecture
GPGPU
“If you were plowing a field, which would you rather use?
Two strong oxen or 1024 chickens?”
(Seymour Cray)
Linked CPUs
HPC Performance
Speedup
how much faster a parallel algorithm is than its sequential counterpart
S(N, P)=tN,P=1 / tN,P
HPC Performance
Strong Scaling
how the solution time varies with the number of processors P for a fixed total problem size N.
E(N) = t1 / (N tN)
HPC Performance
Weak Scaling
how the solution time varies with the number of processors P for a fixed problem size per processor N/P.
E(N)
= (t1
/ tN)
CSE Queue Target Share
Agenda—Day 2
- Scientific Coding and HPC
- Compiling & building (gcc, make)
- Code organization & data management best practices
- Responsible cluster use
- Language support and libraries for HPC
- Debugging
- Numerical error
-
Setting up Campus Cluster to be friendlier
Writing Scientific Code
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Learn your development environment.
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Write self-documenting code.
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Modularize your code.
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Use standard files: README, INSTALL, CITATION, README
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Use a compilation system.
Use version control and issue-tracking.
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Develop and use a unit-testing scheme.
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Don't reinvent the wheel—find existing libraries.
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Don't hard-code values: use configuration files instead.
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Compile with all debug flags set (-Wall -Werror -g).
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Don't create new file formats.
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Have a long-term data management plan.
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Study strong and weak scaling to not waste cluster cycles.
Debugging Scientific Code
Nobody gets it right the first time, [including you]. (Lynne Williams)
Premature optimization is the root of all evil. (Donald Knuth)
-
Confusion is a clue that something is wrong—notice it, don't ignore it. (Eliezer Yudkowsky, paraphrased)
Where can you go next?
http://hpcuniversity.org/roadmap/
Courses
-
ECE 408/CS 483 Applied Parallel Programming
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ECE 492/CS 420/CSE 402 Introduction to Parallel Programming
-
ECE 428/CS 425/CSE 424 Distributed Systems
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CS 524 Concurrent Programming Languages
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CS 525 Advanced Topics in Distributed Systems
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CS 533 Parallel Computer Architectures
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CS 554/CSE 512 Parallel Numerical Algorithms
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ECE 598HK/CS 598HK Many-Core Computing
HPC-Sp14
By uiuc-cse
