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MATLAB : Committed to Excellent Algorithms. Jos Martin [email protected]. Overview. Setting up the problem – what are we trying to build? What have we got to work with? How are we going to do Design Implementation Testing. What do you want MATLAB to do?. - PowerPoint PPT Presentation
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Overview
Setting up the problem – what are we trying to build? What have we got to work with? How are we going to do
– Design– Implementation– Testing
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What do you want MATLAB to do?
MATLAB … Do the bits of my work I don’t really want to do – please!
MATLAB is a place where we build tools to help you do your work
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Development trade-off
Time
FeaturesQuality
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Attributes of good software
Bug-free, Fast, Accurate, Usable
Accurate Usable Bug-free Fast
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The Martin Software Engineering Principle
Frequently Jos is surprisingly stupid
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A software engineering methodology
What and Why? How? (for the user) How? (for the software system) How? (no seriously, I do actually need to write some code!) Did I make a mistake? (in my own code) Did I make a mistake? (in my team) Did I make a mistake? (in any code from MathWorks) …
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A software engineering methodology
Requirements
Functional Design– Function prototypes, object interface, API, etc.– Graphical User Interfaces
Architecture– Modularity– Components– Reuse
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Real situation: Make MATLAB use GPU’s
Requirements– Things the GPU is good at – maths, what else?– All GPU’s? Some subset?
Functional Design– The Nike Approach – Just Do It …– Explicit opt-in
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“Simple to use” vs. “Lots of control”
Solve A*x = b for unknown x Simple
x = A\b Lots of control
DGETRSDGBTRSDGTTRSDPOTRSDPPTRSDPBTRSDPTTRSDSYTRSDSPTRS
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“Simple to use” vs. “Lots of control”
Level of Control Feature
Simple gpuArray, maths, same syntax as MATLAB
Intermediate arrayfun(@fun, …)
Detailed Direct integration with CUDA kernels
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Architecture
GPU
GPU Driver
Mathworks GPU Runtime CUDA Runtime
Memory Mgnt
General GPU infrastructure
Algorithms 3p alg libs
gpuArrayCUDAkernelarrayfun
MATLAB – GPU JIT Compiler
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Implementation
Remember Martin’s Software Engineering Principle– You can only remember 3 ± 4 things at any one time!
Keep ALL code as simple as possible Always use standard patterns
Don’t describe what you are doing, describe why
Some details– McCabe complexity < 15– Compiler warning free, close to lint-free– Short functions (less than 2 screens)– Lots of green
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The Ronseal™ Guarantee
Everything
Does exactly what it says on the tin™ ®
Functions, variables, classes, macros, packages, files, error messages, …
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Review Everything
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Principle of Least Surprise
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Performance
Make sure the code works before tuning for performance
Identify what, if anything, needs optimization (Profiler)– Less that 4% of a program usually accounts for more than 50%
of its runtime
Be careful, tuning for performance usually decreases the readability and maintainability of code
Always consider performance at the design stage
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Testing
Test at every possible level– Individual code units– Local systems – Full system integration– Stress tests for scalability – Interactive (both competent and new user)
Automated testing– Aspire to full case coverage– Push for 100% code coverage
Performance
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Maintenance
Keep your code base clean– Don’t let it acquire cruft [1]
– Always remove all extraneous code– Always leave it better than you found it
Refactor as needed– Costly but nearly always the right thing to do
[1] Cruft is jargon for software or hardware that is of poor quality
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What to do when it doesn’t work!
Does it always fail?– Fix it in the right place in the architecture
Sporadic failures?– Oups!
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Questions?
