Question

我正在尝试在GPU上运行模拟的简单更新循环。基本上有一些由圆圈表示的“生物”，它们在每个更新循环中都会移动，然后将检查它们是否相交。

import numpy as np
import math
from numba import cuda


@cuda.jit('void(float32[:], float32[:], float32[:], uint8[:], float32[:], float32[:], float32, uint32, uint32)')
def update(p_x, p_y, radii, types, velocities, max_velocities, acceleration, num_creatures, cycles):
    for c in range(cycles):
        for i in range(num_creatures):
            velocities[i] = velocities[i] + acceleration
            if velocities[i] > max_velocities[i]:
                velocities[i] = max_velocities[i]
            p_x[i] = p_x[i] + (math.cos(1.0) * velocities[i])
            p_y[i] = p_y[i] + (math.sin(1.0) * velocities[i])
        for i in range(num_creatures):
            for j in range(i, num_creatures):
                delta_x = p_x[j] - p_x[i]
                delta_y = p_y[j] - p_y[i]
                distance_squared = (delta_x * delta_x) + (delta_y * delta_y)
                sum_of_radii = radii[types[i]] + radii[types[i]]
                if distance_squared < sum_of_radii * sum_of_radii:
                    pass


acceleration = .1
creature_radius = 10
spacing = 20
food_radius = 3

max_num_creatures = 1500
num_creatures = 0
max_num_food = 500
num_food = 0
max_num_entities = max_num_creatures + max_num_food
num_entities = 0
cycles = 1


p_x = np.empty((max_num_entities, 1), dtype=np.float32)
p_y = np.empty((max_num_entities, 1), dtype=np.float32)
radii = np.array([creature_radius, creature_radius, food_radius], dtype=np.float32)
types = np.empty((max_num_entities, 1), dtype=np.uint8)

velocities = np.empty((max_num_creatures, 1), dtype=np.float32)
max_velocities = np.empty((max_num_creatures, 1), dtype=np.float32)
# types:
# male - 0
# female - 1
# food - 2
for x in range(1, 800 // spacing):
    for y in range(1, 600 // spacing):
        if num_creatures % 2 == 0:
            types[num_creatures] = 0
        else:
            types[num_creatures] = 1
        p_x[num_creatures] = x * spacing
        p_y[num_creatures] = y * spacing
        max_velocities[num_creatures] = 5
        num_creatures += 1


device_p_x = cuda.to_device(p_x)
device_p_y = cuda.to_device(p_y)
device_radii = cuda.to_device(radii)
device_types = cuda.to_device(types)
device_velocities = cuda.to_device(velocities)
device_max_velocities = cuda.to_device(max_velocities)
update(device_p_x, device_p_y, device_radii, device_types, device_velocities, device_max_velocities,
        acceleration, num_creatures, cycles)
print(device_p_x.copy_to_host()[0])

math.cos和math.sin中的1.0只是单个生物方向的占位符我有一个循环执行循环次数。如果我尝试删除它，而只留下代码块来移动这些生物，即使我向它们添加常数，p_x，p_y或速度都不会改变。为什么不呢？

Answer 1

至少有两个问题：

您没有初始化velocities：

velocities = np.empty((max_num_creatures, 1), dtype=np.float32)

我们可以使用以下方法修复该问题，以进行简单的测试：

velocities = np.ones((max_num_creatures, 1), dtype=np.float32)

这不是正确的数组形状：
```
p_x = np.empty((max_num_entities, 1), dtype=np.float32)
               ^^^^^^^^^^^^^^^^^^^^^
```
匹配您的内核签名：
```
@cuda.jit('void(float32[:], float32[:], float32[:], uint8[:], float32[:], float32[:], float32, uint32, uint32)')
                ^^^^^^^^^^
```
我们可以通过以下方式解决此问题：
```
p_x = np.empty(max_num_entities, dtype=np.float32)
```
，同样适用于p_y，types，velocities和max_velocities。（我想radii可能也要进行一些更改，但是尚不清楚您打算做什么，因为您似乎想要一个多维数组，但正在内核中作为单个数组访问它维数组，AFAICT。此外，您的内核代码部分是一事无成，因此与手头的问题或多或少无关紧要。

进行这些更改时，我得到的似乎是合理的输出：

$ cat t9.py
import numpy as np
import math
from numba import cuda


@cuda.jit('void(float32[:], float32[:], float32[:], uint8[:], float32[:], float32[:], float32, uint32, uint32)')
def update(p_x, p_y, radii, types, velocities, max_velocities, acceleration, num_creatures, cycles):
    for c in range(cycles):
        for i in range(num_creatures):
            velocities[i] = velocities[i] + acceleration
            if velocities[i] > max_velocities[i]:
                velocities[i] = max_velocities[i]
            p_x[i] = p_x[i] + (math.cos(1.0) * velocities[i])
            p_y[i] = p_y[i] + (math.sin(1.0) * velocities[i])
        for i in range(num_creatures):
            for j in range(i, num_creatures):
                delta_x = p_x[j] - p_x[i]
                delta_y = p_y[j] - p_y[i]
                distance_squared = (delta_x * delta_x) + (delta_y * delta_y)
                sum_of_radii = radii[types[i]] + radii[types[i]]
                if distance_squared < sum_of_radii * sum_of_radii:
                    pass


acceleration = .1
creature_radius = 10
spacing = 20
food_radius = 3

max_num_creatures = 1500
num_creatures = 0
max_num_food = 500
num_food = 0
max_num_entities = max_num_creatures + max_num_food
num_entities = 0
cycles = 1


p_x = np.empty(max_num_entities, dtype=np.float32)
p_y = np.empty(max_num_entities, dtype=np.float32)
radii = np.array([creature_radius, creature_radius, food_radius], dtype=np.float32)
types = np.empty(max_num_entities, dtype=np.uint8)

velocities = np.ones(max_num_creatures, dtype=np.float32)
max_velocities = np.empty(max_num_creatures, dtype=np.float32)
# types:
# male - 0
# female - 1
# food - 2
for x in range(1, 800 // spacing):
    for y in range(1, 600 // spacing):
        if num_creatures % 2 == 0:
            types[num_creatures] = 0
        else:
            types[num_creatures] = 1
        p_x[num_creatures] = x * spacing
        p_y[num_creatures] = y * spacing
        max_velocities[num_creatures] = 5
        num_creatures += 1


device_p_x = cuda.to_device(p_x)
device_p_y = cuda.to_device(p_y)
device_radii = cuda.to_device(radii)
device_types = cuda.to_device(types)
device_velocities = cuda.to_device(velocities)
device_max_velocities = cuda.to_device(max_velocities)
update(device_p_x, device_p_y, device_radii, device_types, device_velocities, device_max_velocities,
        acceleration, num_creatures, cycles)
print(device_p_x.copy_to_host())
$ python t9.py
[  2.05943317e+01   2.05943317e+01   2.05943317e+01 ...,   3.64769361e-11
   1.52645868e-19   1.80563260e+28]
$

还请注意，当前您仅启动一个线程的一个块，但我认为当前与您的请求无关。

CUDA函数不会在带有Numba的Python上执行For循环

1 个答案: