Mostly all examples of Numba, CuPy and etc available online are simple array additions, showing the speedup from going to cpu singles core/thread to a gpu. And commands documentations mostly lack good examples. This post is intended to provide a more comprehensive example.
The initial code is provided here. Its a simple model for the classic Cellular Automata. Originally, it doesn't even uses numpy, just plain python and the Pyglet module for visualization.
My goal is to extend this code to a specific problem (that will be very large), but first i thought its best to already optimize for GPU usage.
The game_of_life.py is this:
import random as rnd
import pyglet
#import numpy as np
#from numba import vectorize, cuda, jit
class GameOfLife:
def __init__(self, window_width, window_height, cell_size, percent_fill):
self.grid_width = int(window_width / cell_size) # cell_size
self.grid_height = int(window_height / cell_size) #
self.cell_size = cell_size
self.percent_fill = percent_fill
self.cells = []
self.generate_cells()
def generate_cells(self):
for row in range(0, self.grid_height):
self.cells.append([])
for col in range(0, self.grid_width):
if rnd.random() < self.percent_fill:
self.cells[row].append(1)
else:
self.cells[row].append(0)
def run_rules(self):
temp = []
for row in range(0, self.grid_height):
temp.append([])
for col in range(0, self.grid_width):
cell_sum = sum([self.get_cell_value(row - 1, col),
self.get_cell_value(row - 1, col - 1),
self.get_cell_value(row, col - 1),
self.get_cell_value(row + 1, col - 1),
self.get_cell_value(row + 1, col),
self.get_cell_value(row + 1, col + 1),
self.get_cell_value(row, col + 1),
self.get_cell_value(row - 1, col + 1)])
if self.cells[row][col] == 0 and cell_sum == 3:
temp[row].append(1)
elif self.cells[row][col] == 1 and (cell_sum == 3 or cell_sum == 2):
temp[row].append(1)
else:
temp[row].append(0)
self.cells = temp
def get_cell_value(self, row, col):
if row >= 0 and row < self.grid_height and col >= 0 and col < self.grid_width:
return self.cells[row][col]
return 0
def draw(self):
for row in range(0, self.grid_height):
for col in range(0, self.grid_width):
if self.cells[row][col] == 1:
#(0, 0) (0, 20) (20, 0) (20, 20)
square_coords = (row * self.cell_size, col * self.cell_size,
row * self.cell_size, col * self.cell_size + self.cell_size,
row * self.cell_size + self.cell_size, col * self.cell_size,
row * self.cell_size + self.cell_size, col * self.cell_size + self.cell_size)
pyglet.graphics.draw_indexed(4, pyglet.gl.GL_TRIANGLES,
[0, 1, 2, 1, 2, 3],
('v2i', square_coords))
Firstly, i could use numpy adding at the end of generate_cells this self.cells = np.asarray(self.cells) and at end of run_rules this self.cells = np.asarray(temp), since doing this before wouldn't present speedups, as presented here.(Actually changing to numpy didn't present a noticeable speedup)
Regarding gpu's, for example, i added @jit before every function, and became very slow.
Also tried to use @vectorize(['float32(float32, float32)'], target='cuda'), but this raised a question: how to use @vectorize in functions that only have self as input argument?
I also tried substituting numpy for cupy, like self.cells = cupy.asarray(self.cells), but also became very slow.
Following the initial idea of a extended example of gpu usage, what would be the proper approach to the problem? Where is the right place to put the modifications/vectorizations/parallelizations/numba/cupy etc? And most important, why?
Additional info: besides the code provided, here's the main.py file:
import pyglet
from game_of_life import GameOfLife
class Window(pyglet.window.Window):
def __init__(self):
super().__init__(800,800)
self.gameOfLife = GameOfLife(self.get_size()[0],
self.get_size()[1],
15, # the lesser this value, more computation intensive will be
0.5)
pyglet.clock.schedule_interval(self.update, 1.0/24.0) # 24 frames per second
def on_draw(self):
self.clear()
self.gameOfLife.draw()
def update(self, dt):
self.gameOfLife.run_rules()
if __name__ == '__main__':
window = Window()
pyglet.app.run()
