klongpy 0.3.44

Python implementation of Klong language.

KlongPy

KlongPy is a vectorized port of klong, a simple array lanaguage. The result is a blazingly fast array language that has direct Python integration.

Numpy is used as the target because it itself is an Iverson Ghost, or rather a descendent of APL.

Using numpy also means that via CuPy, both CPU and GPU backends are supported.

If have no idea what Klong is, read this site Klong and the Klong Book. You're welcome.

Performance

The Klong language is simple, so the overhead is low. The bulk of the compute time will likely be spent in numpy doing actual work.

Python

def python_vec(number=100):
    r = timeit.timeit(lambda: [2 * (1 + x) for x in range(10000000)], number=number)
    return r/number

NumPy (CPU) and CuPy

# both use the same implementation
def klong_vec(number=100):
    klong = KlongInterpreter()
    r = timeit.timeit(lambda: klong.exec("2*1+!10000000"), number=number)
    return r/number

Results

CPU (AMD Ryzen 9 7950x)

$ python3 tests/perf_vector.py
klong vec
0.030662008338142185
numpy vec
0.031103943731170147
klong vs numpy => 1.0144131261121019

GPU (NVIDIA GeForce RTX 3090)

USE_GPU=1 python3 tests/perf_vector.py
klong vec
0.0007399664459999968
numpy vec
0.017326295482000008
klong vs numpy => 23.41497452439902

Status

KlongPy aims to be a complete implementation of klong. It currently passes all of the integration tests provided by klong. Additional tests will continue to be added to ensure proper vectorization, etc.

Python integration

KlongPy supports direct Python integration by allowing you to use lambdas or functions, making these extensions available in the klong language - and vice versa.

klong = KlongInterpreter()
klong['f'] = lambda x, y, z: x*1000 + y - z
r = klong.exec('f(3; 10; 20)')
assert r[0] == 2990

Further, you can essentially use Klong as a complement to existing python code or vice versa.

data = np.arange(10*9)
klong['data'] = data
r = klong.exec('1+data')
assert r[0] == 1 + data

Variables may be directly retrieved from KlongPy context:

r = klong.exec('Q::1+data')
Q = klong['Q']
print(Q)

Pandas Integration

TODO: Example

Web server

TODO: Example

Installation

CPU

$ pip3 install klongpy

GPU support

$ pip3 install klongpy[gpu]

Develop

$ git clone https://github.com/briangu/klongpy.git
$ cd klongpy
$ python3 setup.py develop

REPL

$ pip3 install klongpy[repl]
$ kgpy

Welcome to klongpy REPL
author: Brian Guarraci
repo  : https://github.com/briangu/klongpy
crtl-c to quit

?> 1+1
2
?> prime::{&/x!:\2+!_x^1%2}
:monad
?> prime@2
0
?> prime@251
1

Read about the prime example here.

Differences from Klong

The main difference between Klong and KlongPy is that KlongPy doesn't infinite precision because it's backed by NumPy which is restricted to doubles.

Running tests

python3 -m unittest

Acknowledgement

Huge thanks to Nils M Holm for his work on Klong and providing the foundations for this interesting project.