klongpy 0.3.45

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 supports direct Python integration making it easy to use in many app scenarios.

NumPy is used as the runtime target because it itself is an Iverson Ghost, or rather a descendent of APL, making the mapping from Klong to NumPy relatively straightforward.

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

Klong was created by Nils M Holm and he has also written a Klong Book.

Why

KlongPy brings together the Klong terse array language notation with the performance of GPUs. The ability to mix Klong with Python libraries lets you pick and choose the tools as appropriate.

import time
import numpy as np
from klongpy import KlongInterpreter

# create a billion random ints
data = np.random.rand(10**9)
klong = KlongInterpreter()
# define average function in Klong
# Note the '+/' (sum over) uses np.add.reduce under the hood
klong('avg::{(+/x)%#x}')
# make Numpy generated data available in KlongPy as 'data' varable
klong['data'] = data
# run Klong average function
start = time.perf_counter_ns()
r = klong('avg(data)')
stop = time.perf_counter_ns()
seconds = (stop - start) / (10**9)
print(f"avg={round(r,6)} in {round(seconds,6)} seconds")

Run (CPU)

$ python3 tests/perf_avg.py
avg=0.499979 in 0.31318 seconds

Run (GPU)

$ USE_GPU=1 python3 tests/perf_avg.py

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.

Here's a contrived rough benchmark to show the magnitude differences between Python, KlongPy (CPU + GPU) and Numpy (CPU).

Spoiler: GPU-backed KlongPy is about 790x faster than naive Python and 36x faster than NumPy-backed KlongPy.

Python

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

KlongPy

# NumPy and CuPy (CuPy is enabled via USE_GPU=1 environment variable
def klong_vec(number=100):
    klong = KlongInterpreter()
    r = timeit.timeit(lambda: klong.exec("2*1+!10000000"), number=number)
    return r/number

NumPy (explicit usage)

def numpy_vec(number=100):
    r = timeit.timeit(lambda: np.multiply(np.add(np.arange(10000000), 1), 2), number=number)
    return r/number

Results

CPU (AMD Ryzen 9 7950x)

$ python3 tests/perf_vector.py
Python: 0.369111s
KlongPy USE_GPU=None: 0.017946s
Numpy: 0.017896s
Python / KlongPy => 20.568334
Numpy / KlongPy => 0.997245

GPU (Same CPU w/ NVIDIA GeForce RTX 3090)

$ USE_GPU=1 python3 tests/perf_vector.py
Python: 0.364893s
KlongPy USE_GPU=1: 0.000461s
NumPy: 0.017053s
Python / KlongPy => 790.678069
Numpy / KlongPy => 36.951443

Python integration

KlongPy supports direct Python integration, making it easy to mix Klong with Python and use it in the most suitable scenarios. For example, KlongPy can be part of an ML/Pandas workflow or be part of the website backend.

Extending KlongPy with custom functions and moving data in / out of the KlongPy interpeter is easy since the interpreter operates as a dictionary. The dictionary contents are the current KlongPy state.

Data generated elsewhere can be set in KlongPy and seamlessly accessed and processed via Klong language. Also, Python lambdas or functions may be exposed directly as Klong functions, allowing easy extensions to the Klong language.

Function example

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

Data example

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)

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.

Status

KlongPy aims to be a complete implementation of klong. It currently passes all of the integration tests provided by klong.

Primary ongoing work includes:

• Additional tests to
  • ensure proper vectorization
  • increase Klong grammar coverage
• Make REPL (kgpy) compatible with original Klong (kg) REPL

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.