├── .python-version ├── format.sh ├── .gitignore ├── docs ├── example_grid.png └── example_fractal.png ├── pyproject.toml ├── README.md ├── LICENSE ├── src └── cuda_fractal_renderer │ ├── kernel.cu │ └── __init__.py └── uv.lock /.python-version: -------------------------------------------------------------------------------- 1 | 3.12 2 | -------------------------------------------------------------------------------- /format.sh: -------------------------------------------------------------------------------- 1 | uv run ruff format . 2 | uv run ruff check --fix . -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | __pycache__/ 2 | .venv/ 3 | .ruff_cache/ 4 | .vscode/ 5 | output.png -------------------------------------------------------------------------------- /docs/example_grid.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tripplyons/cuda-fractal-renderer/HEAD/docs/example_grid.png -------------------------------------------------------------------------------- /docs/example_fractal.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tripplyons/cuda-fractal-renderer/HEAD/docs/example_fractal.png -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "cuda-fractal-renderer" 3 | version = "0.1.0" 4 | description = "Add your description here" 5 | readme = "README.md" 6 | requires-python = ">=3.12" 7 | dependencies = [ 8 | "numpy>=2.3.2", 9 | "pillow>=11.3.0", 10 | "pycuda>=2025.1.1", 11 | ] 12 | 13 | [project.scripts] 14 | cuda_fractal_renderer = "cuda_fractal_renderer:main" 15 | 16 | [build-system] 17 | requires = ["uv_build>=0.8.3,<0.9.0"] 18 | build-backend = "uv_build" 19 | 20 | [dependency-groups] 21 | dev = [ 22 | "ruff>=0.12.5", 23 | ] 24 | 25 | [tool.ruff.lint] 26 | # Enable the isort rules. 27 | extend-select = ["I"] 28 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # CUDA Fractal Renderer 2 | 3 | Quickly render fractals in CUDA 4 | 5 | | Example Fractal (seed=7) | Example Fractals (seeds=0..8) | 6 | | ------------------------ | ----------------------------- | 7 | | ![Example Fractal (seed=7)](docs/example_fractal.png) | ![Example Fractals (seeds=0..8)](docs/example_grid.png) | 8 | 9 | ## Setup 10 | 11 | - Install CUDA (https://developer.nvidia.com/cuda-downloads) and uv (https://docs.astral.sh/uv/). 12 | - Install dependencies and create the virtual environment: 13 | 14 | ```bash 15 | uv sync 16 | ``` 17 | 18 | ## Usage 19 | 20 | ```bash 21 | uv run cuda_fractal_renderer 22 | ``` 23 | 24 | The `--seed` argument can be used to generate a different fractal. The default seed is 0. 25 | 26 | The `--grid-size` argument can be used to generate a square grid of fractals in parallel. The default grid size is 1 (just a single fractal). 27 | 28 | View the rendered image in `output.png`. 29 | 30 | ## Formatting 31 | 32 | Format and check the code with ruff: 33 | 34 | ```bash 35 | ./format.sh 36 | ``` 37 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2025 Tripp Lyons 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /src/cuda_fractal_renderer/kernel.cu: -------------------------------------------------------------------------------- 1 | __global__ void render_fractal(int *pixels, int width, int height, float x_range, float y_range, int max_iterations, float *parameters, float power, int *seeds, int num_seeds) 2 | { 3 | // where to start in the pixel array 4 | int base_pixel_idx = blockIdx.x * width * height; 5 | // where to start in the parameter array (12 parameters per seed) 6 | int base_parameter_idx = blockIdx.x * 12; 7 | // start IFS iteration at the origin 8 | float x = 0.0f; 9 | float y = 0.0f; 10 | // PRNG state depends on the thread index and the seed 11 | int xorshift_state = seeds[blockIdx.x] * 1024 + threadIdx.x; 12 | for (int i = 0; i < max_iterations; i++) 13 | { 14 | // xorshift32 PRNG step 15 | xorshift_state ^= xorshift_state << 13; 16 | xorshift_state ^= xorshift_state >> 17; 17 | xorshift_state ^= xorshift_state << 5; 18 | // randomly select one of the two 6-parameter transformations 19 | int parameters_idx = base_parameter_idx + 6 * (xorshift_state & 1); 20 | // parameters for the selected transformation 21 | float a = parameters[parameters_idx]; 22 | float b = parameters[parameters_idx + 1]; 23 | float c = parameters[parameters_idx + 2]; 24 | float d = parameters[parameters_idx + 3]; 25 | float e = parameters[parameters_idx + 4]; 26 | float f = parameters[parameters_idx + 5]; 27 | // apply the transformation 28 | x = a * x + b * y + c; 29 | y = d * x + e * y + f; 30 | float distance = pow(x * x + y * y, power); 31 | x /= distance; 32 | y /= distance; 33 | // don't render until the system has "warmed up" away from the origin 34 | if (i < 100) { 35 | continue; 36 | } 37 | // convert the point to a pixel coordinate 38 | int pixel_x = (x + x_range) * width / (2 * x_range); 39 | // check for boundary conditions 40 | if (pixel_x < 0 || pixel_x >= width) { 41 | continue; 42 | } 43 | int pixel_y = (y + y_range) * height / (2 * y_range); 44 | // check for boundary conditions 45 | if (pixel_y < 0 || pixel_y >= height) { 46 | continue; 47 | } 48 | int pixel_idx = base_pixel_idx + pixel_x + pixel_y * width; 49 | // brighten the current pixel (thread-safe) 50 | atomicAdd(&pixels[pixel_idx], 1); 51 | } 52 | } 53 | -------------------------------------------------------------------------------- /src/cuda_fractal_renderer/__init__.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | 3 | import numpy as np 4 | 5 | # ruff: noqa: F401 6 | import pycuda.autoinit 7 | import pycuda.gpuarray as gpuarray 8 | from PIL import Image 9 | from pycuda.compiler import SourceModule 10 | 11 | WIDTH = 4096 12 | HEIGHT = 4096 13 | SCALE_FACTOR = 4 14 | ITERATIONS = 1000000 # 1 million 15 | 16 | 17 | def load_kernel(): 18 | with open("src/cuda_fractal_renderer/kernel.cu", "r") as f: 19 | cuda_code = f.read() 20 | 21 | source_module = SourceModule(cuda_code) 22 | kernel = source_module.get_function("render_fractal") 23 | 24 | return kernel 25 | 26 | 27 | def generate_parameters(seed: int) -> np.ndarray: 28 | np.random.seed(seed) 29 | 30 | # chosen by trial and error to get a nice fractal 31 | min_scale = 1 32 | max_scale = 1.5 33 | min_angle = 0 34 | max_angle = 2 * np.pi 35 | min_angle_difference = 0.5 * np.pi 36 | max_angle_difference = 1 * np.pi 37 | min_bias = 0.25 38 | max_bias = 0.75 39 | 40 | def generate_half() -> list[float]: 41 | angle1 = np.random.uniform(min_angle, max_angle) 42 | angle2 = angle1 + np.random.uniform(min_angle_difference, max_angle_difference) 43 | bias_angle = np.random.uniform(min_angle, max_angle) 44 | scale1 = np.random.uniform(min_scale, max_scale) 45 | scale2 = np.random.uniform(min_scale, max_scale) 46 | bias_scale = np.random.uniform(min_bias, max_bias) 47 | return [ 48 | np.cos(angle1) * scale1, 49 | np.cos(angle2) * scale2, 50 | np.cos(bias_angle) * bias_scale, 51 | np.sin(angle1) * scale1, 52 | np.sin(angle2) * scale2, 53 | np.sin(bias_angle) * bias_scale, 54 | ] 55 | 56 | return np.concatenate([generate_half(), generate_half()]) 57 | 58 | 59 | def generate_fractals(kernel, seeds: list[int]) -> list[Image.Image]: 60 | parameters = [] 61 | for seed in seeds: 62 | parameters.append(generate_parameters(seed)) 63 | parameters = np.concatenate(parameters) 64 | 65 | seeds_gpu = gpuarray.to_gpu(np.array(seeds, dtype=np.int32)) 66 | 67 | parameters_gpu = gpuarray.to_gpu(parameters.astype(np.float32)) 68 | 69 | width = np.int32(WIDTH) 70 | height = np.int32(HEIGHT) 71 | 72 | x_range = np.float32(2.0) 73 | y_range = np.float32(2.0) 74 | max_iterations = np.int32(ITERATIONS) 75 | # chosen by trial and error to get a nice fractal 76 | power = np.float32(0.2) 77 | 78 | threads_per_block = 1024 79 | num_seeds = np.int32(len(seeds)) 80 | blocks_per_grid = len(seeds) 81 | 82 | pixels = gpuarray.zeros(width * height * len(seeds), dtype=np.int32) 83 | 84 | print("rendering fractals") 85 | kernel( 86 | pixels, 87 | width, 88 | height, 89 | x_range, 90 | y_range, 91 | max_iterations, 92 | parameters_gpu, 93 | power, 94 | seeds_gpu, 95 | num_seeds, 96 | block=(threads_per_block, 1, 1), 97 | grid=(blocks_per_grid, 1), 98 | ) 99 | 100 | pixels = pixels.get() 101 | print("done rendering fractals, converting to images") 102 | 103 | pixels = pixels.reshape((len(seeds), HEIGHT * WIDTH)) 104 | 105 | images = [] 106 | for image_index in range(len(seeds)): 107 | # normalize the pixels to 0-255 108 | pixel = pixels[image_index].astype(np.float32) 109 | quantile = np.quantile(pixel, 0.95) 110 | 111 | if quantile == 0: 112 | positive_pixels = pixel[pixel > 0] 113 | if len(positive_pixels) == 0: 114 | quantile = 1 115 | else: 116 | quantile = np.min(positive_pixels) 117 | 118 | pixel /= quantile 119 | pixel = np.clip(pixel, 0, 1) 120 | pixel *= 255 121 | pixel = pixel.astype(np.uint8) 122 | 123 | pixel = pixel.reshape((HEIGHT, WIDTH)) 124 | # scale the image down 125 | image = Image.fromarray(pixel, mode="L").resize( 126 | (WIDTH // SCALE_FACTOR, HEIGHT // SCALE_FACTOR) 127 | ) 128 | images.append(image) 129 | 130 | return images 131 | 132 | 133 | def main(): 134 | parser = argparse.ArgumentParser() 135 | parser.add_argument("--seed", type=int, default=0) 136 | parser.add_argument("--grid-size", type=int, default=1) 137 | args = parser.parse_args() 138 | 139 | kernel = load_kernel() 140 | 141 | images = [] 142 | seeds = [] 143 | 144 | for row in range(args.grid_size): 145 | for col in range(args.grid_size): 146 | seed = args.seed + row * args.grid_size + col 147 | seeds.append(seed) 148 | 149 | images = generate_fractals(kernel, seeds) 150 | 151 | images_grid = [] 152 | 153 | for row in range(args.grid_size): 154 | images_row = [] 155 | for col in range(args.grid_size): 156 | image = images[row * args.grid_size + col] 157 | images_row.append(image) 158 | images_grid.append(images_row) 159 | 160 | image_width = WIDTH // SCALE_FACTOR 161 | image_height = HEIGHT // SCALE_FACTOR 162 | 163 | # combine the images into a single image 164 | combined_image = Image.new( 165 | "RGB", 166 | (args.grid_size * image_width, args.grid_size * image_height), 167 | ) 168 | for row in range(args.grid_size): 169 | for col in range(args.grid_size): 170 | combined_image.paste( 171 | images_grid[row][col], (col * image_width, row * image_height) 172 | ) 173 | # resize the combined image to image size 174 | combined_image = combined_image.resize((image_width, image_height)) 175 | 176 | combined_image.save("output.png") 177 | -------------------------------------------------------------------------------- /uv.lock: 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