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Integrating GATLAS with minimal modifications

No software development this last month… I was studying the standard financial theory. I had to learn more about finance to validate my vision for quantitative GPGPU. If my vision was wrong, then I might work towards a certain dead end. (By the way, Professor Geanakoplos’ course is awesome. It’s a lot of fun. He is a lively speaker with deep practical experience. The math isn’t hard but enough to derive results more rigorously if you want.)

The production black box systems I have seen were designed from a viewpoint of statistical filtering and clustering (and did not use any GPUs). Computationally, this required solving regression problems with training data while trying to avoid overfitting. The arithmetic intensity arose from machine learning.

Options pricing is the well known financial application. Prices are calculated from backward induction over a tree or lattice. What are the transition probabilities due to uncertainty inside the tree? They must be found with machine learning from historical data.

I feel confident of my machine learning driven vision for quantitative GPGPU now.

Anyway, now I will integrate GATLAS with minimal modifications into the JIT. That’s the right thing to do first. A modular virtual machine and JIT is better even if it is less efficient. Extensibility, flexibility and maintainability are more important at this point than optimizing for performance, so long as performance is good enough.


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