Brainfold On Python, game development and everything

More types and results

I implemented some new types for PyEigen, including a 4x4 matrix class and benchmarks for it. Same methods as last time, but quite different results.

Benchmark results

PyEigen is still fastest by far, which is promising. This time it was about 5-10x faster than cgkit1, which was again the second fastest. You might also notice that vectypes is missing. For this test, it was so slow that I had to leave it out; other results would have been invisible if I had fitted the vectypes results in the graph. In the worst case, it was over 1000x slower than PyEigen! Euclid is missing addition and scalar multiplication scores since it doesn't support those operations. NumPy performed much better. There has to be a faster way to do vector cross product in NumPy, since matrix multiplication was much faster than cross product. But it's not a big deal anymore since PyEiglet is so much faster than anything else.

Surprising results

I did some preliminary benchmarking today and got very interesting results. I have only wrapped a 3D vector class so far, so I tested a couple of operations (add, multiply, dot & cross product) against the libs I mentioned in the previous post: NumPy, euclid, vectypes and cgkit. For cgkit, I tested both 1.2.0 and 2.0 alpha 9. All other libs were the latest version. I tested using the Python timeit module with 1,000,000 calls and 3 repeats per test and took the lowest number. Repeated test results generally differed only by milliseconds.

Benchmark results

The immediately obvious surprise is the abysmal performance of NumPy especially in cross products. I don't think NumPy optimizes for fixed-size arrays; I would have been better off with the pure-Python euclid and vectypes modules.

The other surprises were euclid vs vectypes and cgkit1 vs cgkit2. Euclid and vectypes are both by Alex Holkner of Pyglet fame. In both cases, the newer library (vectypes and cgkit2) was also slower.

Of course the most positive surprise for me was the performance of PyEigen. It's only a trivial wrapping, but was about 2-8x faster than the best alternative, cgkit1. I'm very happy with the results and definitely going to continue development of the library / wrapper.

Update: Here's the benchmark code. Also an interesting detail: PyEigen is 136x faster than NumPy with cross products.