Month: November 2009

c, LLVM

A method for JIT’ing algorithms and data structures with LLVM

llvm_dragon

Hello folks, I always post about Python and EvoComp (Pyevolve), but this time it’s about C, LLVM, search algorithms and data structures. This post describes the efforts to implement an idea: to JIT (verb) algorithms and the data structures used by them, together.

AVL Tree Intro

Here is a short intro to AVL Trees from Wikipedia:

In computer science, an AVL tree is a self-balancing binary search tree, and it is the first such data structure to be invented. In an AVL tree, the heights of the two child subtrees of any node differ by at most one; therefore, it is also said to be height-balanced. Lookup, insertion, and deletion all take O(log n) time in both the average and worst cases, where n is the number of nodes in the tree prior to the operation. Insertions and deletions may require the tree to be rebalanced by one or more tree rotations.

The problem and the idea

When we have a data structure and algorithms to handle (insert, remove and lookup) that structure, the native code of our algorithm is usually full of overhead; for example, in an AVL Tree (Balanced Binary Tree), the overhead appear in: checking if we really have a left or right node while traversing the nodes for lookups, accessing nodes inside nodes, etc. This overhead creates unnecessary assembly operations which in turn, creates native code overhead, even when the compiler optimize it. This overhead directly impacts on the performance of our algorithm (this traditional approach, of course, give us a very flexible structure and the complexity (not Big-O) is easy to handle, but we pay for it: performance loss).

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News, Pyevolve, Python, Science

Pyevolve on SIGEVOlution

SIGEVOlution200901WebCover

I’m proud to announce that Pyevolve is featuring on the last issue of SIGEVOlution (Volume 4, Issue 1), a newsletter from the ACM Special Interest Group on Evolutionary Computation. I would like to thank the newsletter editor Pier Luca Lanzi and the board for the corrections in the article and for the well done reformatted version of the paper.

Pyevolve is currently in version 0.5, in a few months I’ll be releasing the new 0.6 release with the new major features that are currently implemented in the development version only (you can check it at the subversion repository in sourceforge.net).

I hope you enjoy the article !

Yours,
– Christian S. Perone