pyoclk 1.0.2

A easy way to run OpenCL kernel files

OpenCL Kernel Python Wrapper

Install

Install from wheel

download wheel from release and install

Compile from source

Clone this repo

clone by http

git clone --recursive https://github.com/jinmingyi1998/opencl_kernels.git

with ssh

git clone --recursive [e-mail]:jinmingyi1998/opencl_kernels.git

Install

cd opencl_kernels
python setup.py install

DO NOT move this directory after install

Requirements

• OpenCL GPU hardware
• numpy
• cmake > 3.16

Usage

Kernel File:

a file named add.cl

kernel void add(global float*a, global float*out, int int_arg, float float_arg){
    int x = get_global_id(0);
    if(x==0){
        printf(" accept int arg: %d, accept float arg: %f\n",int_arg,float_arg);
    }
    out[x] = a[x] * float_arg + int_arg;    
}

Python Code

OOP Style

import numpy as np
import oclk

a = np.random.rand(100, 100).reshape([10, -1])
a = np.float32(a)
out = np.zeros(a.shape)
out = np.float32(out)

runner = oclk.Runner()
runner.load_kernel("add.cl", "add", "")

timer = oclk.TimerArgs(
    enable=True,
    warmup=10,
    repeat=50,
    name='add_kernel'
)
runner.run(
    kernel_name="add",
    input=[
        {"name": "a", "value": a, },
        {"name": "out", "value": out, },
        {"name": "int_arg", "value": 1, },
        {"name": "float_arg", "value": 12.34}
    ],
    output=['out'],
    local_work_size=[1, 1],
    global_work_size=a.shape,
    timer=timer
)
# check result
a = a.reshape([-1])
out = out.reshape([-1])
print(a[:8])
print(out[:8])

Call with Functions

import numpy as np
import oclk

a = np.random.rand(100, 100).reshape([10, -1])
a = np.float32(a)

out = np.zeros(a.shape)
out = np.float32(out)
oclk.init()
oclk.load_kernel("add.cl", "add", "")
r = oclk.run(
    kernel_name="add",
    input=[
        {"name": "a", "value": a, },
        {"name": "out", "value": out, },
        {"name": "int_arg", "value": 1, },
        {"name": "float_arg", "value": 12.34}
    ],
    output=['out'],
    local_work_size=[1, 1],
    global_work_size=a.shape
)
# check result
a = a.reshape([-1])
out = out.reshape([-1])
print(a[:8])
print(out[:8])

Python api Usage

API

def run(*, kernel_name: str,
        input: Dict[str, Union[int, float, np.array]],
        output: List[str],
        local_work_size: List[int],
        global_work_size: List[int],
        wait: bool = True,
        timer: Union[Dict, TimerArgs] = TimerArgsDisabled) -> List[np.ndarray]: ...

• input: Dictionary to set input args, in the same order as kernel function
  • args from np.array should be contiguous array
  • constant args only support (will support more types):
    • python type: float -> c type: float
    • python type: int -> c type: long
• output: List of names to specify which array will be get back from GPU buffer
• local_work_size/global_work_work: list of integer, specified work sizes
• wait: Optional, default true, wait for GPU
• timer: Optional, arguments to set up a timer for benchmark kernels

example

a = np.zeros([16, 16, 16], dtype=np.float32)
b = np.zeros([16, 16, 16], dtype=np.float32)
c = np.zeros([16, 16, 16], dtype=np.float32)
timer = TimerArgs(enable=True,
                  warmup=10,
                  repeat=100,
                  name='timer_name'
                  )
run(kernel_name='add',
    input=[
        {"name": "a", "value": a, },
        {"name": "b", "value": b, },
        {"name": "int_arg", "value": 1, },
        {"name": "float_arg", "value": 12.34},
        {"name": "c", "value": c}
    ],
    output=['c'],
    local_work_size=[1, 1, 1],
    global_work_size=a.shape,
    timer=timer
    )