Updated readme
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Alejandro Saucedo
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146
README.md
146
README.md
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@ -52,7 +52,7 @@ Kompute is provided as a single header file [`Kompute.hpp`](#setup). See [build-
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This simple example will show the basics of Kompute through the high level API.
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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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2. Run multiplication operation synchronously
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3. Map results back from GPU memory to print the results
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View the [extended version](#your-first-kompute-extended-version) or [more examples](#simple-examples).
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@ -64,21 +64,78 @@ int main() {
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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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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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// 2. Run compute shader synchronously
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// Operations provide abstractions to GPU processing steps and can be extended
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mgr.evalOpDefault<kp::OpAlgoBase<>>(
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{ tensorA, tensorB },
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shaderData); // "shaderData" defined below, and can be glsl/spirv string or path to file
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// 2. Run multiplication operation synchronously
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mgr.evalOpDefault<kp::OpMult<>>(
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{ tensorA, tensorB, tensorOut })
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// 3. Map results back from GPU memory to print the results
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mgr.evalOpDefault<kp::OpTensorSyncLocal>({ tensorA, tensorB })
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mgr.evalOpDefault<kp::OpTensorSyncLocal>({ tensorA, tensorB, tensorOut })
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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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// Prints the output which is Output: { 2, 4, 6 }
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std::cout << fmt::format("Output: {}",
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tensorOut.data()) << std::endl;
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}
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```
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## Your First Kompute (Extended Version)
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We will now show the [same example as above](#your-first-kompute-simple-version) but leveraging more advanced Kompute features:
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1. Create Kompute Manager with explicit device 0 and single queue of familyIndex 2
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2. Create a set of data tensors in host memory for processing
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3. Map the tensor host data into GPU memory with Kompute Operation
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4. Run operation with custom compute shader code asynchronously
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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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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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// 1. Create Kompute Manager with explicit device 0 and single queue of familyIndex 2
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kp::Manager mgr(0, { 2 });
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// 2. Create a set of data tensors in host memory for processing
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auto tensorInA = std::make_shared<kp::Tensor>(kp::Tensor({ 2., 2., 2. }));
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auto tensorInB = std::make_shared<kp::Tensor>(kp::Tensor({ 1., 2., 3. }));
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auto tensorOut = std::make_shared<kp::Tensor>(kp::Tensor({ 0., 0., 0. }));
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// 3. Map the tensor host data into GPU memory with Kompute Operation
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mgr.evalOpDefault<kp::OpTensorCreate>({ tensorInA, tensorInB, tensorOut });
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// 4. Run compute shader Asynchronously with explicit dispatch layout
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mgr.evalOpAsyncDefault<kp::OpAlgoBase<3, 1, 1>>(
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{ tensorInA, tensorInB, tensorOut },
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shaderData); // "shaderData" defined is below and can be glsl/spirv string, or path to file
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// 4.1. 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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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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sq->begin();
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// 5.2. Record batch commands
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sq->record<kp::OpTensorSyncLocal({ tensorInA });
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sq->record<kp::OpTensorSyncLocal({ tensorInB });
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sq->record<kp::OpTensorSyncLocal({ tensorOut });
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// 5.3. 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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sq->eval();
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// Prints the output which is Output: { 2, 4, 6 }
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std::cout << fmt::format("Output: {}",
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tensorOut.data()) << std::endl;
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}
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```
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@ -92,14 +149,14 @@ static std::string shaderString = (R"(
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layout (local_size_x = 1) in;
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// The input tensors in order provided
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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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// 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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pb[index] = pa[index];
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pa[index] = index;
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tout[index] = tina[index] * tinb[index];
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}
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)");
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static std::vector<char> shaderData(shaderString.begin(), shaderString.end());
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@ -205,61 +262,6 @@ The Vulkan SDK offers a great low level interface that enables for highly specia
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We are currently developing Vulkan Kompute not to hide the Vulkan SDK interface (as it's incredibly well designed) but to augment it with a direct focus on Vulkan's GPU computing capabilities. [This article](https://towardsdatascience.com/machine-learning-and-data-processing-in-the-gpu-with-vulkan-kompute-c9350e5e5d3a) provides a high level overview of the motivations of Kompute, together with a set of hands on examples that introduce both GPU computing as well as the core Vulkan Kompute architecture.
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## Your First Kompute (Extended Version)
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We will now show the [same example as above](#your-first-kompute-simple-version) 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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auto tensorB = std::make_shared<kp::Tensor>(kp::Tensor({ 0., 0., 0. }));
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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. 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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shaderData); // Using the same shaderData defined above in the "simple example"
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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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// 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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// 4.1. Explicitly begin recording batch commands
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sq->begin();
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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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// 4.3. Explicitly stop recording batch commands
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sq->end();
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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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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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## More examples
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### Simple examples
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