Simplified initial examples
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Alejandro Saucedo
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README.md
180
README.md
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@ -42,9 +42,88 @@
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## Getting Started
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### Setup
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Below you can find a GPU multiplication example using the C++ and Python Kompute interfaces.
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Kompute is provided as a single header file [`Kompute.hpp`](#setup). See [build-system section](#build-overview) for configurations available.
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### Your First Kompute (C++)
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The C++ interface provides low level access to the native components of Kompute and Vulkan, enabling for [advanced optimizations](https://kompute.cc/overview/async-parallel.html) as well as [extension of components](https://kompute.cc/overview/reference.html).
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```c++
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int main() {
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// 1. Create Kompute Manager with default settings (device 0 and first compute compatible queue)
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kp::Manager mgr;
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// 2. Create and initialise Kompute Tensors through manager
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auto tensorInA = mgr.buildTensor({ 2., 2., 2. });
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auto tensorInB = mgr.buildTensor({ 1., 2., 3. });
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auto tensorOut = mgr.buildTensor({ 0., 0., 0. });
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// 3. Specify "multiply shader" code (can also be raw string, spir-v bytes or file path)
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std::string shaderString = (R"(
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#version 450
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layout (local_size_x = 1) in;
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// The input tensors bind index is relative to index in parameter passed
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layout(set = 0, binding = 0) buffer bina { float tina[]; };
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layout(set = 0, binding = 1) buffer binb { float tinb[]; };
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layout(set = 0, binding = 2) buffer bout { float tout[]; };
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void main() {
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uint index = gl_GlobalInvocationID.x;
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tout[index] = tina[index] * tinb[index];
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}
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)");
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// 3. Run operation with string shader synchronously
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mgr.evalOpDefault<kp::OpAlgoBase>(
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{ tensorInA, tensorInB, tensorOut },
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std::vector<char>(shaderString.begin(), shaderString.end()));
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// 4. Map results back from GPU memory to print the results
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mgr.evalOpDefault<kp::OpTensorSyncLocal>({ tensorInA, tensorInB, tensorOut });
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// Prints the output which is Output: { 2, 4, 6 }
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for (const float& elem : tensorOut->data()) std::cout << elem << " ";
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}
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```
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#### Your First Kompute (Python)
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The [Python package](https://kompute.cc/overview/python-package.html) provides a [high level interactive interface](https://kompute.cc/overview/python-reference.html) that enables for experimentation whilst ensuring high performance and fast development workflows.
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```python
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# 1. Create Kompute Manager with default settings (device 0 and first compute compatible queue)
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mgr = Manager()
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# 2. Create and initialise Kompute Tensors (can be initialized with List[] or np.Array)
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tensor_in_a = Tensor([2, 2, 2])
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tensor_in_b = Tensor([1, 2, 3])
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tensor_out = Tensor([0, 0, 0])
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mgr.eval_tensor_create_def([tensor_in_a, tensor_in_b, tensor_out])
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# 3. Specify "multiply shader" code (can also be raw string, spir-v bytes or file path)
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@python2shader
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def compute_shader_multiply(index=("input", "GlobalInvocationId", ivec3),
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data1=("buffer", 0, Array(f32)),
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data2=("buffer", 1, Array(f32)),
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data3=("buffer", 2, Array(f32))):
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i = index.x
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data3[i] = data1[i] * data2[i]
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# 4. Run multiplication operation synchronously
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mgr.eval_algo_data_def(
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[tensor_in_a, tensor_in_b, tensor_out], compute_shader_multiply.to_spirv())
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# 5. Map results back from GPU memory to print the results
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mgr.eval_tensor_sync_local_def([tensor_out])
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# Prints [2.0, 4.0, 6.0]
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print(tensor_out.data())
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```
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### Interactive Notebooks & Hands on Videos
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@ -114,103 +193,6 @@ Both talks have annotated sections - the intro for both is almost the same so yo
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</table>
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### Your First Kompute
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Below you can find both the C++ and Python version of a simple GPU multiplication snippet with Kompute.
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In both examples the steps carried out will include:
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1. Create Kompute Manager with default settings (device 0 and first compute compatible queue)
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2. Create and initialise Kompute Tensors through manager
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3. Specify "multiply shader" code (can also be raw string, spir-v bytes or file path)
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4. Run operation with string shader synchronously
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5. Map results back from GPU memory to print the results
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#### Simple C++ Example
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The C++ interface provides lower level access to the native components of Kompute and Vulkan, enabling for advanced optimizations as well as extension of components.
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To see a full breakdown you can read further in the [C++ Class Reference](https://kompute.cc/overview/reference.html).
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```c++
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int main() {
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// 1. Create Kompute Manager with default settings (device 0 and first compute compatible queue)
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kp::Manager mgr;
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// 2. Create and initialise Kompute Tensors through manager
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auto tensorInA = mgr.buildTensor({ 2., 2., 2. });
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auto tensorInB = mgr.buildTensor({ 1., 2., 3. });
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auto tensorOut = mgr.buildTensor({ 0., 0., 0. });
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// 3. Specify "multiply shader" code (can also be raw string, spir-v bytes or file path)
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std::string shaderString = (R"(
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#version 450
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layout (local_size_x = 1) in;
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// The input tensors bind index is relative to index in parameter passed
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layout(set = 0, binding = 0) buffer bina { float tina[]; };
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layout(set = 0, binding = 1) buffer binb { float tinb[]; };
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layout(set = 0, binding = 2) buffer bout { float tout[]; };
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void main() {
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uint index = gl_GlobalInvocationID.x;
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tout[index] = tina[index] * tinb[index];
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}
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)");
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// 3. Run operation with string shader synchronously
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mgr.evalOpDefault<kp::OpAlgoBase>(
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{ tensorInA, tensorInB, tensorOut },
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std::vector<char>(shaderString.begin(), shaderString.end()));
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// 4. Map results back from GPU memory to print the results
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mgr.evalOpDefault<kp::OpTensorSyncLocal>({ tensorInA, tensorInB, tensorOut });
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// Prints the output which is Output: { 2, 4, 6 }
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for (const float& elem : tensorOut->data()) std::cout << elem << " ";
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}
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```
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#### Simple Python Example
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The Python interface provides a higher level interactive interface that enables for experimentation whilst ensuring high performance and fast development workflows.
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For further details you can read the [Python Package documentation](https://kompute.cc/overview/python-package.html) or the [Python Class Reference documentation](https://kompute.cc/overview/python-reference.html).
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```python
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# 1. Create Kompute Manager with default settings (device 0 and first compute compatible queue)
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mgr = Manager()
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# 2. Create and initialise Kompute Tensors (can be initialized with List[] or np.Array)
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tensor_in_a = Tensor([2, 2, 2])
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tensor_in_b = Tensor([1, 2, 3])
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tensor_out = Tensor([0, 0, 0])
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mgr.eval_tensor_create_def([tensor_in_a, tensor_in_b, tensor_out])
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# 3. Specify "multiply shader" code (can also be raw string, spir-v bytes or file path)
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@python2shader
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def compute_shader_multiply(index=("input", "GlobalInvocationId", ivec3),
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data1=("buffer", 0, Array(f32)),
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data2=("buffer", 1, Array(f32)),
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data3=("buffer", 2, Array(f32))):
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i = index.x
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data3[i] = data1[i] * data2[i]
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# 4. Run multiplication operation synchronously
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mgr.eval_algo_data_def(
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[tensor_in_a, tensor_in_b, tensor_out], compute_shader_multiply.to_spirv())
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# 5. Map results back from GPU memory to print the results
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mgr.eval_tensor_sync_local_def([tensor_out])
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# Prints [2.0, 4.0, 6.0]
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print(tensor_out.data())
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```
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## Architectural Overview
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The core architecture of Kompute includes the following:
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