A JIT compiler for geometric algebra in python

Using Gaalop and numba this package symbolically optimises geomteric algebra algorithms, generates numpy code and JITs the resultant code. Currently only supports Cl(4,1), still very experimental and hacky. Currently produces around an order of magnitude speed up for normally written algorithms and around 2-4x speed up over hand optimised code. Many further optimisations are possible that so far have not been implemented, Pull Requests welcomed.

The package comes in the form of a decorator that can be applied to normal functions written with the clifford GA syntax. Grade masks can be specified as arguements this is highly recommended for both run and compile time speedups verbosity of the JIT process can be adjusted for debugging:

from gajit import *
from clifford.g3c import *

@gajit(mask_list=[layout.grade_mask(1), layout.grade_mask(3)], verbose=1)
def pp_algo_wrapped(P, C):
    Cplane = C ^ einf
    Cd = C * (Cplane)
    L = (Cplane * P * Cplane) ^ Cd ^ einf
    pp = Cd ^ (L * e12345)
    return pp

def pp_algo(P, C):
    Cplane = C ^ einf
    Cd = C * (Cplane)
    L = (Cplane * P * Cplane) ^ Cd ^ einf
    pp = Cd ^ (L * e12345)
    return pp

The jitted function can be called as normal and much faster than the original

from clifford.tools.g3c import *
import time

P = random_conformal_point()
C = random_circle()

print( pp_algo_wrapped(P, C) )
print( pp_algo(P, C) )
   
start_time = time.time()
for i in range(10000):
    pp_algo_wrapped(P, C)
t_gaalop = time.time() - start_time
print('gaJIT ALGO: ', t_gaalop)

start_time = time.time()
for i in range(10000):
    pp_algo(P, C)
t_trad = time.time() - start_time
print('TRADITIONAL: ', t_trad)

print('T/G: ', t_trad / t_gaalop)

This requires Gaalop to be compiled in command line interface (cli) mode, instructions on setting this up are on the TODO list

• Describe the Gaalop CLI setup and environment variables
• Provide a pip installable setup.py
• Host on pypi
• Implement more transpilation of CLUSCRIPT language features
• Implement a non-mapped metric for speed + simplicity
• Implement a proper codegen cache system
• Add a blade mask inference option