COMPASS_gpu_intro.ipynb

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        "<a href=\"https://colab.research.google.com/github/evaneschneider/parallel-programming/blob/master/COMPASS_gpu_intro.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "## Introduction to GPU programming with Numba\n",
        "\n",
        "This notebook includes content from [seibert's 2018 gtc numba tutorial](https://github.com/ContinuumIO/gtc2018-numba). I highly recommend that tutorial in its entireity if you want more practice with Numba and GPUs."
      ]
    },
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      "source": [
        "##What is Numba?\n",
        "\n",
        "According to the [Numba documentation](https://numba.pydata.org/numba-doc/latest/user/index.html), \"Numba is a just-in-time compiler for Python that works best on code that uses NumPy arrays and functions, and loops. The most common way to use Numba is through its collection of decorators that can be applied to your functions to instruct Numba to compile them. When a call is made to a Numba decorated function it is compiled to machine code “just-in-time” for execution and all or part of your code can subsequently run at native machine code speed!\"\n",
        "\n",
        "For our purposes today, Numba is a python package that allows you to write python code for GPUs."
      ]
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      "source": [
        "%matplotlib inline\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "import math\n",
        "from numba import jit, njit, vectorize, cuda"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
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      "source": [
        "### Accessing the GPU\n",
        "\n",
        "1) In order to run Numba functions using google's free GPUs, we have to do a couple of things. First, go to the Runtime menu, click on 'Change Runtime Type', and in the pop-up box, under 'Hardware Accelerator', select 'GPU'. Save the Runtime.\n",
        "\n",
        "2) Ideally, that's all we should have to do. But in practice, even though the CUDA libararies are installed, as of this writing Colab can't find them automatically. So, we'll figure out where they are, and then point Colab to them."
      ]
    },
    {
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      "cell_type": "code",
      "source": [
        "!find / -iname 'libdevice'\n",
        "!find / -iname 'libnvvm.so'"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/cuda-10.0/nvvm/libdevice\n",
            "/usr/local/cuda-10.0/nvvm/lib64/libnvvm.so\n"
          ],
          "name": "stdout"
        }
      ]
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    {
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      "cell_type": "markdown",
      "source": [
        "Paste the location of the libraries into the following code box (if it's different, otherwise you can just run the code):"
      ]
    },
    {
      "metadata": {
        "id": "3m5mmDRQLF_q",
        "colab_type": "code",
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      "cell_type": "code",
      "source": [
        "import os\n",
        "os.environ['NUMBAPRO_LIBDEVICE'] = \"/usr/local/cuda-10.0/nvvm/libdevice\"\n",
        "os.environ['NUMBAPRO_NVVM'] = \"/usr/local/cuda-10.0/nvvm/lib64/libnvvm.so\""
      ],
      "execution_count": 0,
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      "source": [
        "### Vector Addition on GPUs\n",
        "\n",
        "The simplest way to access the GPU via Numba is to use a vectorized ufunc. A Numpy ufunc, or [Universal Function,](https://docs.scipy.org/doc/numpy/reference/ufuncs.html) is a function that operates on vectors, or arrays. If we use Numba's `vectorize` decorator and specify the `cuda` target, Numba will automatically write a CUDA kernel for us and run the function on the GPU! Let's try it out:"
      ]
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      "cell_type": "code",
      "source": [
        "@vectorize(['int64(int64, int64)'], target='cuda')\n",
        "def add_ufunc_gpu(x, y):\n",
        "    return x + y"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
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        "id": "3K0jty2RKoh5",
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      "cell_type": "code",
      "source": [
        "x = np.arange(10)\n",
        "y = 2 * x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "add_ufunc_gpu(x, y)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  3,  6,  9, 12, 15, 18, 21, 24, 27])"
            ]
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          "metadata": {
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          "execution_count": 6
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      "source": [
        "Cool, it worked! But what actually just happened? Well, a lot of things. Numba automatically:\n",
        "+ Compiled a CUDA kernel to execute the ufunc operation in parallel over all the input elements.\n",
        "+ Allocated GPU memory for the inputs and the output.\n",
        "+ Copied the input data to the GPU.\n",
        "+ Executed the CUDA kernel with the correct kernel dimensions given the input sizes.\n",
        "+ Copied the result back from the GPU to the CPU.\n",
        "+ Returned the result as a NumPy array on the host."
      ]
    },
    {
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      "cell_type": "markdown",
      "source": [
        "Using the `%timeit` magic function, we can determine how fast the CUDA function is:"
      ]
    },
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
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      },
      "cell_type": "code",
      "source": [
        "%timeit add_ufunc_gpu(x, y)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The slowest run took 7.92 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
            "1000 loops, best of 3: 802 µs per loop\n"
          ],
          "name": "stdout"
        }
      ]
    },
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      "source": [
        "And compare it to a version compiled for the CPU:"
      ]
    },
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        "outputId": "1bdf2bb7-ca1f-44b1-ac9e-05fee1d9ce54",
        "colab": {
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      "cell_type": "code",
      "source": [
        "@vectorize(['int64(int64, int64)'], target='cpu')\n",
        "def add_ufunc_cpu(x, y):\n",
        "    return x + y\n",
        "%timeit add_ufunc_cpu(x, y)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The slowest run took 42.88 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
            "1000000 loops, best of 3: 492 ns per loop\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
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      "source": [
        "Wow, the GPU is a LOT slower! That's because we have (deliberately) misused the GPU in several ways in this example:\n",
        "+ **Our inputs are too small**: the GPU achieves performance through parallelism, operating on thousands of values at once. Our test inputs have only 10 integers. We need a much larger array to even keep the GPU busy.\n",
        "+ **Our calculation is too simple**: Sending a calculation to the GPU involves quite a bit of overhead compared to calling a function on the CPU. If our calculation does not involve enough math operations (\"arithmetic intensity\"), then the GPU will spend most of its time waiting for data to move around.\n",
        "+ **We copy the data to and from the GPU**: While including the copy time can be realistic for a single function, often we want to run several GPU operations in sequence. In those cases, it makes sense to send data to the GPU and keep it there until all of our processing is complete.\n",
        "+ **Our data types are larger than necessary**: Our example uses int64 when we probably don't need it. Scalar code using data types that are 32 and 64-bit run basically the same speed on the CPU, but 64-bit data types have a significant performance cost on the GPU. Basic arithmetic on 64-bit floats can be anywhere from 2x (Pascal-architecture Tesla) to 24x (Maxwell-architecture GeForce) slower than 32-bit floats. NumPy defaults to 64-bit data types when creating arrays, so it is important to set the dtype attribute or use the ndarray.astype() method to pick 32-bit types when you need them.\n",
        "\n"
      ]
    },
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      "source": [
        "### Writing Cuda Kernels\n",
        "\n",
        "While targeting ufuncs with the `cuda` syntax is the most straightforward way to access the GPU with Numba, it may not be flexible enough for your needs. If you want to write a more detailed GPU program, at some point you are probably going to need to write CUDA kernels.\n",
        "\n",
        "As discussed in the lecture, the CUDA programming model allows you to abstract the GPU hardware into a software model composed of a **grid** containing **blocks** of **threads**. These threads are the smallest individual unit in the programming model, and they execute together in groups (traditionally called **warps**, consisting of 32 threads each). Determiming the best size for your grid of thread blocks is a complicated problem that often depends on the specific algorithm and hardware you're using, but here a few good rules of thumb:\n",
        "+ the size of a block should be a multiple of 32 threads, with typical block sizes between 128 and 512 threads per block.\n",
        "+ the size of the grid should ensure the full GPU is utilized where possible. Launching a grid where the number of blocks is 2x-4x the number of **streaming multiprocessors** on the GPU is a good starting place. (The Tesla K80 GPUs provided by Colaboratory have 15 SMs - more modern GPUs like the P100s on TigerGPU have 60+.)\n",
        "+ The CUDA kernel launch overhead does depend on the number of blocks, so it may not be best to launch a grid where the number of threads equals the number of input elements when the input size is very big. We'll show a pattern for dealing with large inputs below.\n",
        "\n",
        "As a first example, let's return to our vector addition function, but this time, we'll target it with the `cuda.jit` decorator:"
      ]
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      "source": [
        "@cuda.jit\n",
        "def add_kernel(x, y, out):\n",
        "    tidx = cuda.threadIdx.x # this is the unique thread ID within a 1D block\n",
        "    bidx = cuda.blockIdx.x  # Similarly, this is the unique block ID within the 1D grid\n",
        "\n",
        "    block_dimx = cuda.blockDim.x  # number of threads per block\n",
        "    grid_dimx = cuda.gridDim.x    # number of blocks in the grid\n",
        "    \n",
        "    start = tidx + bidx * block_dimx\n",
        "    stride = block_dimx * grid_dimx\n",
        "\n",
        "    # assuming x and y inputs are same length\n",
        "    for i in range(start, x.shape[0], stride):\n",
        "        out[i] = x[i] + y[i]"
      ],
      "execution_count": 0,
      "outputs": []
    },
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      "metadata": {
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      "source": [
        "That's a lot more typing than our ufunc example, and it is much more limited: it only works on 1D arrays, it doesn't verify input sizes match, etc. Most of the function is spent figuring out how to turn the block and grid indices and dimensions into unique offsets in the input arrays. The pattern of computing a starting index and a stride is a common way to ensure that your grid size is independent of the input size. The striding will maximize bandwidth by ensuring that threads with consecuitive indices are accessing consecutive memory locations as much as possible. Thread indices beyond the length of the input (x.shape[0], since x is a NumPy array) automatically skip over the for loop.\n",
        "\n",
        "Let's call the function now on some data:"
      ]
    },
    {
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        "outputId": "58e69cf4-9dd7-4351-f088-9a24d2f9f435",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
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      "cell_type": "code",
      "source": [
        "n = 100000\n",
        "x = np.arange(n).astype(np.float32)\n",
        "y = 2 * x\n",
        "out = np.empty_like(x)\n",
        "\n",
        "threads_per_block = 128\n",
        "blocks_per_grid = 30\n",
        "\n",
        "add_kernel[blocks_per_grid, threads_per_block](x, y, out)\n",
        "print(out[:10])"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 0.  3.  6.  9. 12. 15. 18. 21. 24. 27.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
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      "metadata": {
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      "source": [
        "The calling syntax is designed to mimic the way CUDA kernels are launched in C, where the number of blocks per grid and threads per block are specified in the square brackets, and the arguments to the function are specified afterwards in parentheses.\n",
        "\n",
        "Note that, unlike the ufunc, the arguments are passed to the kernel as full NumPy arrays. A thread within the kernel can access any element in the array it wants, regardless of its position in the thread grid. This is why CUDA kernels are significantly more powerful than ufuncs. (But with great power, comes a greater amount of typing...)\n",
        "\n",
        "Numba has created some [helper functions](http://numba.pydata.org/numba-doc/dev/cuda/kernels.html#absolute-positions) to cut down on the typing. We can write the previous kernel much more simply as:"
      ]
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      "cell_type": "code",
      "source": [
        "@cuda.jit\n",
        "def add_kernel(x, y, out):\n",
        "    start = cuda.grid(1)      # the 1 argument means a one dimensional thread grid, this returns a single value\n",
        "    stride = cuda.gridsize(1) # ditto\n",
        "\n",
        "    # assuming x and y inputs are same length\n",
        "    for i in range(start, x.shape[0], stride):\n",
        "        out[i] = x[i] + y[i]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
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      "cell_type": "markdown",
      "source": [
        "As before, using NumPy arrays forces Numba to allocate GPU memory, copy the arguments to the GPU, run the kernel, then copy the argument arrays back to the host. This not very efficient, so you will often want to pre-allocate device arrays."
      ]
    },
    {
      "metadata": {
        "id": "jS6HTzeMSHZU",
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      "cell_type": "markdown",
      "source": [
        "### Memory Management\n",
        "\n",
        "Numba can automatically handle transferring data to and from the GPU for us. However, that's not always what we want. Sometimes we will want to perform several functions in a row on the GPU without transferring the data back to the CPU in between. To address this, Numba provides the `to_device` function in the `cuda` module to allocate and copy arrays to the GPU:"
      ]
    },
    {
      "metadata": {
        "id": "l5zj87YCR7wo",
        "colab_type": "code",
        "outputId": "f7019084-15dd-40ea-f75f-e5b1000312e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "cell_type": "code",
      "source": [
        "x_device = cuda.to_device(x)\n",
        "y_device = cuda.to_device(y)\n",
        "\n",
        "print(x_device)\n",
        "print(x_device.shape)\n",
        "print(x_device.dtype)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<numba.cuda.cudadrv.devicearray.DeviceNDArray object at 0x7f14a1beb5f8>\n",
            "(100000,)\n",
            "float32\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "_wNw1KPgSnHa",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "`x_device` and `y_device` are now Numba \"device arrays\" that are in many ways equivalent to Numpy ndarrays except that they live in the GPU's global memory, rather than on the CPU. These device arrays can be passed to Numba cuda functions just the way Numpy arrays can, but without the memory copying overhead."
      ]
    },
    {
      "metadata": {
        "id": "YYsTJ6I_SshQ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We can also create an output buffer on the GPU with the numba.cuda.device_array() function:"
      ]
    },
    {
      "metadata": {
        "id": "wn9cV7vjSXzT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "out_device = cuda.device_array(shape=(n,), dtype=np.float32)  # does not initialize the contents, much like np.empty()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "IUNR3GrdTHrl",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Now we can try out our Cuda kernel using the pre-copied device arrays, and compare the time to a version without moving the data first."
      ]
    },
    {
      "metadata": {
        "id": "KhmFwAjnSzFn",
        "colab_type": "code",
        "outputId": "f12c9e48-e81a-4453-d028-07fc89d5de8c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "%timeit add_kernel[blocks_per_grid, threads_per_block](x_device, y_device, out_device)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The slowest run took 843.32 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
            "10000 loops, best of 3: 192 µs per loop\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "B8FAUC9qUJ7E",
        "colab_type": "code",
        "outputId": "c61ba35c-95fa-4946-bad4-478b9e548e91",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "%timeit add_kernel[blocks_per_grid, threads_per_block](x, y, out)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The slowest run took 119.72 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
            "1000 loops, best of 3: 1.39 ms per loop\n"
          ],
          "name": "stdout"
        }
      ]
    },
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      "metadata": {
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      "cell_type": "markdown",
      "source": [
        "As you can see, moving data back and forth is *expensive*. In general, you will want to keep your data on the GPU as long as possible and do as many calculations as you can before moving it back. Of course, at some point you will have to move the data back to the CPU, whether to output to a file, perform some other CPU functions, etc.. Numba provides the `copy_to_host` function for that:"
      ]
    },
    {
      "metadata": {
        "id": "iamX7eLNT6XN",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "out = out_device.copy_to_host()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "vsDC1ChBVbnq",
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      },
      "cell_type": "markdown",
      "source": [
        "After all of this extra work, you may be wondering what the point of doing calculations on the GPU is. After all, if we compare the time it takes to run our fancy Cuda kernel with the pre-allocated GPU arrays, to the CPU version provided by python, we find that the CPU version is still faster:"
      ]
    },
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "%timeit x + y"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The slowest run took 4.58 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
            "10000 loops, best of 3: 68.6 µs per loop\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "-N1CTqpPV5U-",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "But that's because we *still* haven't given the GPU enough work to do. If we go back and try the same problem, but with arrays that are 10 times larger..."
      ]
    },
    {
      "metadata": {
        "id": "8oIcVyvGU9iO",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "n = 1000000\n",
        "x = np.arange(n).astype(np.float32)\n",
        "y = 2 * x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "vXK9W6bWWGaT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "x_device = cuda.to_device(x)\n",
        "y_device = cuda.to_device(y)\n",
        "out_device = cuda.device_array(shape=(n,), dtype=np.float32)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "n1w1DtsLWNH8",
        "colab_type": "code",
        "outputId": "e1f7524e-4a01-425e-fb58-dcd28cd5b05d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "%timeit add_kernel[blocks_per_grid, threads_per_block](x_device, y_device, out_device)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000 loops, best of 3: 193 µs per loop\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "_uw0pSGdWQzA",
        "colab_type": "code",
        "outputId": "4b324641-4558-4809-eba6-5e048899c443",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "%timeit x + y"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The slowest run took 5.78 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
            "1000 loops, best of 3: 724 µs per loop\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "9RPJyHAKW5lw",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "...we start to see the power of the GPU. As you may have noticed, the GPU function called with a million array elements took approximately the same amount of time to run as the version with 100 thousand elements. That's because most of the GPU was not busy when we only had 100 thousand elements, so we still hadn't overcome the overhead of launching the kernel."
      ]
    },
    {
      "metadata": {
        "id": "ZEe4mFj2Zo03",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Fun with Fractals\n",
        "\n",
        "This is a short tutorial, but to give you an idea of an area where GPUs shine, let's play around with some slightly more complicated functions designed to create fractals. Below are two functions that create an instance of the Julia set:"
      ]
    },
    {
      "metadata": {
        "id": "1isz9dnMWgW5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def julia(x, y, max_iters):\n",
        "  \"\"\"\n",
        "  Given the real and imaginary parts of a complex number,\n",
        "  determine if it is a candidate for membership in the Julia\n",
        "  set given a fixed number of iterations.\n",
        "  \"\"\"\n",
        "  i = 0\n",
        "  c = complex(-0.8, 0.156)\n",
        "  a = complex(x,y)\n",
        "  for i in range(max_iters):\n",
        "    a = a*a + c\n",
        "    if (a.real*a.real + a.imag*a.imag) > 1000:\n",
        "      return int((i/max_iters)*255)\n",
        "  return 255"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "eGAZshA0Z_SY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def create_fractal(min_x, max_x, min_y, max_y, image, iters):\n",
        "  height = image.shape[0]\n",
        "  width = image.shape[1]\n",
        "\n",
        "  pixel_size_x = (max_x - min_x) / width\n",
        "  pixel_size_y = (max_y - min_y) / height\n",
        "  for x in range(width):\n",
        "    real = min_x + x * pixel_size_x\n",
        "    for y in range(height):\n",
        "      imag = min_y + y * pixel_size_y\n",
        "      color = julia(real, imag, iters)\n",
        "      image[y, x] = color\n",
        "\n",
        "  return image"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zTyoePO7aBzU",
        "colab_type": "code",
        "outputId": "4aac655f-ce1f-4e61-ad39-2f5b25b0d0a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "cell_type": "code",
      "source": [
        "image = np.zeros((500, 750), dtype=np.uint8)\n",
        "%timeit create_fractal(-2.0, 2.0, -1.0, 1.0, image, 200)\n",
        "plt.imshow(image)\n",
        "plt.viridis()\n",
        "plt.show()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1 loop, best of 3: 3.22 s per loop\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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57CByM1z3zCSueS6O+Vmh7L/tQ3IOhrK6z3vGA2HlOObGrGJESDoyx1gUPTgw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7IL4TLo+vAI5PASbnQXMw+hWO87+tK7TTRNJsRi3+NLu2eUg/DsiZbMyQ4g6LCLjrEKsJvYfNk/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\n4QgxzsQdDocjxPw/lbV0FYLSAhUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "pRBnncSjbjf3",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "In order to turn this into a GPU implementation, we need to have a kernel function (create_fractal) call another function (julia) on the device. Numba has a way of specifying functions that will be called from within a kernel by passing the cuda.jit decorator the `device` argument:"
      ]
    },
    {
      "metadata": {
        "id": "FJyjvq8vbeD9",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "@cuda.jit(device=True)\n",
        "def julia(x, y, max_iters):\n",
        "  \"\"\"\n",
        "  Given the real and imaginary parts of a complex number,\n",
        "  determine if it is a candidate for membership in the Julia\n",
        "  set given a fixed number of iterations.\n",
        "  \"\"\"\n",
        "  i = 0\n",
        "  c = complex(-0.8, 0.156)\n",
        "  a = complex(x,y)\n",
        "  for i in range(max_iters):\n",
        "    a = a*a + c\n",
        "    if (a.real*a.real + a.imag*a.imag) > 1000:\n",
        "      return int((i/max_iters)*255)\n",
        "  return 255"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "346P5H9cbyUi",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Multi-dimensional grids\n",
        "\n",
        "For some problems, it makes sense to define a two- or three-dimensional grid of thread blocks. That way, when you're indexing a single thread, you can map it to, say, the pixel position in an image. Multi-dimensional grids are created by passing tuples to the kernel function. You can ensure that you launch a big enough grid by calculating the size of each dimension as a function of the array size and number of threads per block:"
      ]
    },
    {
      "metadata": {
        "id": "UzQtAeVfbsle",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "threadsperblock = 16\n",
        "xblocks = (image.shape[1] + (threadsperblock - 1)) // threadsperblock\n",
        "yblocks = (image.shape[0] + (threadsperblock - 1)) // threadsperblock"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "D0Zr98eHcCol",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Then, within a kernel, you can determine the absolute thread position by calling the `grid` helper function, as in `x, y = cuda.grid(2)`."
      ]
    },
    {
      "metadata": {
        "id": "Iz5GvibKcHJ4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Below is a modified version of the create_fractal function that launches as a kernel on the GPU and calls the device function, julia. We use a 2D grid of thread blocks to launch the kernel and determine which threads are responsible for each pixel in the image."
      ]
    },
    {
      "metadata": {
        "id": "KC4-KVZQb4-p",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "@cuda.jit\n",
        "def create_fractal(min_x, max_x, min_y, max_y, image, iters):\n",
        "  height = image.shape[0]\n",
        "  width = image.shape[1]\n",
        "\n",
        "  pixel_size_x = (max_x - min_x) / width\n",
        "  pixel_size_y = (max_y - min_y) / height\n",
        "    \n",
        "  x, y = cuda.grid(2)\n",
        "    \n",
        "  if x < width and y < height:\n",
        "    real = min_x + x * pixel_size_x\n",
        "    imag = min_y + y * pixel_size_y\n",
        "    color = julia(real, imag, iters)\n",
        "    image[y, x] = color"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1ub1r0mfc3aw",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "image = np.zeros((500, 750), dtype=np.uint8)\n",
        "threadsperblock = 16\n",
        "threads_per_block = (16, 16)\n",
        "xblocks = (image.shape[1] + (threadsperblock - 1)) // threadsperblock\n",
        "yblocks = (image.shape[0] + (threadsperblock - 1)) // threadsperblock\n",
        "blocks_per_grid = (xblocks, yblocks)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "QXAW2lN6c-uh",
        "colab_type": "code",
        "outputId": "91c6a103-f342-4ae4-840f-c91c82bbe0c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        }
      },
      "cell_type": "code",
      "source": [
        "%timeit create_fractal[blocks_per_grid, threads_per_block](-2.0, 2.0, -1.0, 1.0, image, 200)\n",
        "plt.imshow(image)\n",
        "plt.viridis()\n",
        "plt.show()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The slowest run took 35.52 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
            "1 loop, best of 3: 5.83 ms per loop\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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57CByM1z3zCSueS6O+Vmh7L/tQ3IOhrK6z3vGA2HlOObGrGJESDoyx1gUPTgw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7IL4TLo+vAI5PASbnQXMw+hWO87+tK7TTRNJsRi3+NLu2eUg/DsiZbMyQ4g6LCLjrEKsJvYfNk/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\n4QgxzsQdDocjxPw/lbV0FYLSAhUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "2xxFEgv6dJI8",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "How does the GPU implementation compare to the CPU version?"
      ]
    },
    {
      "metadata": {
        "id": "orIrqK_odD27",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}