Merge branch '77_update_e2e_xamples'
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Alejandro Saucedo
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1 changed files with 91 additions and 35 deletions
126
README.md
126
README.md
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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
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#### Your First Kompute (Simple)
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In this simple example we will:
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This simple example will show the basics of Kompute through the high level API, including:
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1. Create a set of data tensors in host memory for processing
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2. Map the tensor host data into GPU memory with Kompute Operation
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3. Define shader as string or spirv bytes (can also pass path to file)
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4. Run compute shader asynchronously with Async function
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5. Create managed sequence to submit batch operations to the CPU
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6. Map data back to host by running the sequence of batch operations
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1. Create and initialise a set of data tensors for processing
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2. Run compute shader synchronously
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3. Create managed sequence to submit batch operations to the CPU
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4. Map data back to host by running operation
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View [more examples](https://kompute.cc/overview/advanced-examples.html#simple-examples).
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```c++
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int main() {
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// You can allow Kompute to create the Vulkan components, or pass your existing ones
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kp::Manager mgr; // Selects device 0 and first compute queue unless explicitly requested
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// Default manager selects device 0 and first compute queue
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kp::Manager mgr;
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// 1. Create and initialise a set of data tensors for processing
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auto tensorA = mgr.buildTensor({ 3., 4., 5. });
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auto tensorB = mgr.buildTensor({ 0., 0., 0. });
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// 2. Run compute shader synchronously
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mgr.evalOpDefault<kp::OpAlgoBase<>>(
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{ tensorA, tensorB },
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shader); // "shader" explained below, and can be glsl/spirv string or path to file
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// 3. Sync results from GPU memory to print the results
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mgr.evalOpDefault<kp::OpTensorSyncLocal>({ tensorA, tensorB })
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// Prints the output which is A: { 0, 1, 2 } B: { 3, 4, 5 }
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std::cout << fmt::format("A: {}, B: {}",
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tensorA.data(), tensorB.data()) << std::endl;
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}
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```
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Your shader can be provided as raw glsl/hlsl string, SPIR-V bytes array (using our CLI), or string path to file containing either. Below are the examples of the valid ways of providing shader.
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##### Raw GLSL/HLSL as std::string
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```c++
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static std::string shaderString = (R"(
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#version 450
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layout (local_size_x = 1) in;
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layout(set = 0, binding = 0) buffer a { float pa[]; };
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layout(set = 0, binding = 1) buffer b { float pb[]; };
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void main() {
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uint index = gl_GlobalInvocationID.x;
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pb[index] = pa[index];
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pa[index] = index;
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}
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)");
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static std::vector<char> shader(shaderString.begin(), shaderString.end());
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```
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##### SPIR-V Bytes as uint8_t / char array (using our CLI)
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You can use the Kompute [shader-to-cpp-header CLI](https://kompute.cc/overview/shaders-to-headers.html) to convert your GLSL/HLSL or SPIRV shader into C++ header file (see documentation link for more info).
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```c++
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static std::vector<char> shader = { 0x03, //... spirv bytes go here)
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```
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##### File path to file containing raw glsl/hlsl or SPIRV bytes
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```c++
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static std::string shader = "path/to/shader.glsl";
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// Or SPIR-V
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static std::string shader = "path/to/shader.glsl.spv";
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```
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#### Your First Kompute (Extended)
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We will cover the same example as above but leveraging more advanced Kompute features:
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1. Create a set of data tensors in host memory for processing
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2. Map the tensor host data into GPU memory with Kompute Operation
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3. Run compute shader asynchronously with Async function
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4. Create managed sequence to submit batch operations to the CPU
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5. Map data back to host by running the sequence of batch operations
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View [more examples](https://kompute.cc/overview/advanced-examples.html#simple-examples).
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```c++
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int main() {
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// Creating manager with Device 0, and a single queue of familyIndex 2
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kp::Manager mgr(0, { 2 });
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// 1. Create a set of data tensors in host memory for processing
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auto tensorA = std::make_shared<kp::Tensor>(kp::Tensor({ 3., 4., 5. }));
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// 2. Map the tensor host data into GPU memory with Kompute Operation
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mgr.evalOpDefault<kp::OpTensorCreate>({ tensorA, tensorB });
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// 3. Define shader as string or spirv bytes (can also pass path to file)
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std::string shader(R"(
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#version 450
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layout (local_size_x = 1) in;
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layout(set = 0, binding = 0) buffer a { float pa[]; };
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layout(set = 0, binding = 1) buffer b { float pb[]; };
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void main() {
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uint index = gl_GlobalInvocationID.x;
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pb[index] = pa[index];
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pa[index] = index;
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}
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)");
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// 4. Run compute shader asynchronously with Async function
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// 3. Run compute shader Asynchronously with explicit dispatch layout
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mgr.evalOpAsyncDefault<kp::OpAlgoBase<3, 1, 1>>(
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{ tensorA, tensorB },
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std::vector<char>(shader.begin(), shader.end()));
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shader); // Using the same shader as above
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// 4.1. Before submitting sequence batch we wait for the async operation
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// 3.1. Before submitting sequence batch we wait for the async operation
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mgr.evalOpAwaitDefault();
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// 5. Create managed sequence to submit batch operations to the CPU
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// 4. Create managed sequence to submit batch operations to the CPU
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std::shared_ptr<kp::Sequence> sq = mgr.getOrCreateManagedSequence("seq").lock();
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// 5.1. Explicitly begin recording batch commands
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// 4.1. Explicitly begin recording batch commands
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sq->begin();
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// 5.2. Record batch commands
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// 4.2. Record batch commands
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sq->record<kp::OpTensorSyncLocal({ tensorA });
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sq->record<kp::OpTensorSyncLocal({ tensorB });
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// 5.3. Explicitly stop recording batch commands
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// 4.3. Explicitly stop recording batch commands
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sq->end();
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// 6. Map data back to host by running the sequence of batch operations
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// 5. Map data back to host by running the sequence of batch operations
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sq->eval();
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// Prints the output which is A: { 0, 1, 2 } B: { 3, 4, 5 }
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@ -143,7 +199,7 @@ int main() {
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The core architecture of Kompute include the following:
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* [Kompute Manager](https://kompute.cc/overview/reference.html#manager) - Base orchestrator which creates and manages device and child components
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* [Kompute Sequence](https://kompute.cc/overview/reference.html#sequence) - Container of operations that can be sent to GPU as batch
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* [Kompute Operation (Base)](https://kompute.cc/overview/reference.html#algorithm) - Individual operation which performs actions on top of tensors and (opt) algorithms
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* [Kompute Operation (Base)](https://kompute.cc/overview/reference.html#algorithm) - Base class from which all operations inherit
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* [Kompute Tensor](https://kompute.cc/overview/reference.html#tensor) - Tensor structured data used in GPU operations
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* [Kompute Algorithm](https://kompute.cc/overview/reference.html#algorithm) - Abstraction for (shader) code executed in the GPU
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