Class Documentation and C++ Reference

This section provides a breakdown of the cpp classes and what each of their functions provide. It is partially generated and augomented from the Doxygen autodoc content. You can also go directly to the raw doxygen docs.

Below is a diagram that provides insights on the relationship between Vulkan Kompute objects and Vulkan resources, which primarily encompass ownership of either CPU and/or GPU memory.

Manager

The Kompute Manager provides a high level interface to simplify interaction with underlying kp::Sequences of kp::Operations.

class kp::Manager

Base orchestrator which creates and manages device and child components

Public Functions

Manager()

Base constructor and default used which creates the base resources including choosing the device 0 by default.

Manager(uint32_t physicalDeviceIndex, const std::vector<uint32_t> &familyQueueIndices = {}, const std::vector<std::string> &desiredExtensions = {})

Similar to base constructor but allows for further configuration to use when creating the Vulkan resources.

Parameters

• physicalDeviceIndex: The index of the physical device to use

• familyQueueIndices: (Optional) List of queue indices to add for explicit allocation

• desiredExtensions: The desired extensions to load from physicalDevice

Manager(std::shared_ptr<vk::Instance> instance, std::shared_ptr<vk::PhysicalDevice> physicalDevice, std::shared_ptr<vk::Device> device)

Manager constructor which allows your own vulkan application to integrate with the vulkan kompute use.

Parameters

• instance: Vulkan compute instance to base this application

• physicalDevice: Vulkan physical device to use for application

• device: Vulkan logical device to use for all base resources

• physicalDeviceIndex: Index for vulkan physical device used

~Manager()

Manager destructor which would ensure all owned resources are destroyed unless explicitly stated that resources should not be destroyed or freed.

std::shared_ptr<Sequence> sequence(uint32_t queueIndex = 0, uint32_t totalTimestamps = 0)

Create a managed sequence that will be destroyed by this manager if it hasn’t been destroyed by its reference count going to zero.

Return

Shared pointer with initialised sequence

Parameters

• queueIndex: The queue to use from the available queues

• nrOfTimestamps: The maximum number of timestamps to allocate. If zero (default), disables latching of timestamps.

template<typename T>
std::shared_ptr<TensorT<T>> tensorT(const std::vector<T> &data, Tensor::TensorTypes tensorType = Tensor::TensorTypes::eDevice)

Create a managed tensor that will be destroyed by this manager if it hasn’t been destroyed by its reference count going to zero.

Return

Shared pointer with initialised tensor

Parameters

• data: The data to initialize the tensor with

• tensorType: The type of tensor to initialize

std::shared_ptr<Algorithm> algorithm(const std::vector<std::shared_ptr<Tensor>> &tensors = {}, const std::vector<uint32_t> &spirv = {}, const Workgroup &workgroup = {}, const Constants &specializationConstants = {}, const Constants &pushConstants = {})

Create a managed algorithm that will be destroyed by this manager if it hasn’t been destroyed by its reference count going to zero.

Return

Shared pointer with initialised algorithm

Parameters

• tensors: (optional) The tensors to initialise the algorithm with

• spirv: (optional) The SPIRV bytes for the algorithm to dispatch

• workgroup: (optional) kp::Workgroup for algorithm to use, and defaults to (tensor[0].size(), 1, 1)

• specializationConstants: (optional) kp::Constant to use for specialization constants, and defaults to an empty constant

• pushConstants: (optional) kp::Constant to use for push constants, and defaults to an empty constant

void destroy()

Destroy the GPU resources and all managed resources by manager.

void clear()

Run a pseudo-garbage collection to release all the managed resources that have been already freed due to these reaching to zero ref count.

vk::PhysicalDeviceProperties getDeviceProperties() const

Information about the current device.

Return

vk::PhysicalDeviceProperties containing information about the device

std::vector<vk::PhysicalDevice> listDevices() const

List the devices available in the current vulkan instance.

Return

vector of physical devices containing their respective properties

Sequence

The Kompute Sequence consists of batches of kp::Operations, which are executed on a respective GPU queue. The execution of sequences can be synchronous or asynchronous, and it can be coordinated through its respective vk::Fence.

class kp::Sequence : public std::enable_shared_from_this<Sequence>

Container of operations that can be sent to GPU as batch

Public Functions

Sequence(std::shared_ptr<vk::PhysicalDevice> physicalDevice, std::shared_ptr<vk::Device> device, std::shared_ptr<vk::Queue> computeQueue, uint32_t queueIndex, uint32_t totalTimestamps = 0)

Main constructor for sequence which requires core vulkan components to generate all dependent resources.

Parameters

• physicalDevice: Vulkan physical device

• device: Vulkan logical device

• computeQueue: Vulkan compute queue

• queueIndex: Vulkan compute queue index in device

• totalTimestamps: Maximum number of timestamps to allocate

~Sequence()

Destructor for sequence which is responsible for cleaning all subsequent owned operations.

std::shared_ptr<Sequence> record(std::shared_ptr<OpBase> op)

Record function for operation to be added to the GPU queue in batch. This template requires classes to be derived from the OpBase class. This function also requires the Sequence to be recording, otherwise it will not be able to add the operation.

Return

shared_ptr<Sequence> of the Sequence class itself

Parameters

• op: Object derived from kp::BaseOp that will be recoreded by the sequence which will be used when the operation is evaluated.

template<typename T, typename ...TArgs>
std::shared_ptr<Sequence> record(std::vector<std::shared_ptr<Tensor>> tensors, TArgs&&... params)

Record function for operation to be added to the GPU queue in batch. This template requires classes to be derived from the OpBase class. This function also requires the Sequence to be recording, otherwise it will not be able to add the operation.

Return

shared_ptr<Sequence> of the Sequence class itself

Parameters

• tensors: Vector of tensors to use for the operation

• TArgs: Template parameters that are used to initialise operation which allows for extensible configurations on initialisation.

template<typename T, typename ...TArgs>
std::shared_ptr<Sequence> record(std::shared_ptr<Algorithm> algorithm, TArgs&&... params)

Record function for operation to be added to the GPU queue in batch. This template requires classes to be derived from the OpBase class. This function also requires the Sequence to be recording, otherwise it will not be able to add the operation.

Return

shared_ptr<Sequence> of the Sequence class itself

Parameters

• algorithm: Algorithm to use for the record often used for OpAlgo operations

• TArgs: Template parameters that are used to initialise operation which allows for extensible configurations on initialisation.

std::shared_ptr<Sequence> eval()

Eval sends all the recorded and stored operations in the vector of operations into the gpu as a submit job synchronously (with a barrier).

Return

shared_ptr<Sequence> of the Sequence class itself

std::shared_ptr<Sequence> eval(std::shared_ptr<OpBase> op)

Resets all the recorded and stored operations, records the operation provided and submits into the gpu as a submit job synchronously (with a barrier).

Return

shared_ptr<Sequence> of the Sequence class itself

template<typename T, typename ...TArgs>
std::shared_ptr<Sequence> eval(std::vector<std::shared_ptr<Tensor>> tensors, TArgs&&... params)

Eval sends all the recorded and stored operations in the vector of operations into the gpu as a submit job with a barrier.

Return

shared_ptr<Sequence> of the Sequence class itself

Parameters

• tensors: Vector of tensors to use for the operation

• TArgs: Template parameters that are used to initialise operation which allows for extensible configurations on initialisation.

template<typename T, typename ...TArgs>
std::shared_ptr<Sequence> eval(std::shared_ptr<Algorithm> algorithm, TArgs&&... params)

Eval sends all the recorded and stored operations in the vector of operations into the gpu as a submit job with a barrier.

Return

shared_ptr<Sequence> of the Sequence class itself

Parameters

• algorithm: Algorithm to use for the record often used for OpAlgo operations

• TArgs: Template parameters that are used to initialise operation which allows for extensible configurations on initialisation.

std::shared_ptr<Sequence> evalAsync()

Eval Async sends all the recorded and stored operations in the vector of operations into the gpu as a submit job without a barrier. EvalAwait() must ALWAYS be called after to ensure the sequence is terminated correctly.

Return

Boolean stating whether execution was successful.

std::shared_ptr<Sequence> evalAsync(std::shared_ptr<OpBase> op)

Clears currnet operations to record provided one in the vector of operations into the gpu as a submit job without a barrier. EvalAwait() must ALWAYS be called after to ensure the sequence is terminated correctly.

Return

Boolean stating whether execution was successful.

template<typename T, typename ...TArgs>
std::shared_ptr<Sequence> evalAsync(std::vector<std::shared_ptr<Tensor>> tensors, TArgs&&... params)

Eval sends all the recorded and stored operations in the vector of operations into the gpu as a submit job with a barrier.

Return

shared_ptr<Sequence> of the Sequence class itself

Parameters

• tensors: Vector of tensors to use for the operation

• TArgs: Template parameters that are used to initialise operation which allows for extensible configurations on initialisation.

template<typename T, typename ...TArgs>
std::shared_ptr<Sequence> evalAsync(std::shared_ptr<Algorithm> algorithm, TArgs&&... params)

Eval sends all the recorded and stored operations in the vector of operations into the gpu as a submit job with a barrier.

Return

shared_ptr<Sequence> of the Sequence class itself

Parameters

• algorithm: Algorithm to use for the record often used for OpAlgo operations

• TArgs: Template parameters that are used to initialise operation which allows for extensible configurations on initialisation.

std::shared_ptr<Sequence> evalAwait(uint64_t waitFor = UINT64_MAX)

Eval Await waits for the fence to finish processing and then once it finishes, it runs the postEval of all operations.

Return

shared_ptr<Sequence> of the Sequence class itself

Parameters

• waitFor: Number of milliseconds to wait before timing out.

void clear()

Clear function clears all operations currently recorded and starts recording again.

std::vector<std::uint64_t> getTimestamps()

Return the timestamps that were latched at the beginning and after each operation during the last eval() call.

void begin()

Begins recording commands for commands to be submitted into the command buffer.

Return

Boolean stating whether execution was successful.

void end()

Ends the recording and stops recording commands when the record command is sent.

Return

Boolean stating whether execution was successful.

bool isRecording()

Returns true if the sequence is currently in recording activated.

Return

Boolean stating if recording ongoing.

bool isInit()

Returns true if the sequence has been initialised, and it’s based on the GPU resources being refrenced.

Return

Boolean stating if is initialized

void rerecord()

Clears command buffer and triggers re-record of all the current operations saved, which is useful if the underlying kp::Tensors or kp::Algorithms are modified and need to be re-recorded.

bool isRunning()

Returns true if the sequence is currently running - mostly used for async workloads.

Return

Boolean stating if currently running.

void destroy()

Destroys and frees the GPU resources which include the buffer and memory and sets the sequence as init=False.

Tensor

The kp::Tensor is the atomic unit in Kompute, and it is used primarily for handling Host and GPU Device data.

class kp::Tensor

Structured data used in GPU operations.

Tensors are the base building block in Kompute to perform operations across GPUs. Each tensor would have a respective Vulkan memory and buffer, which would be used to store their respective data. The tensors can be used for GPU data storage or transfer.

Subclassed by kp::TensorT< T >

Public Types

enum TensorTypes

Type for tensors created: Device allows memory to be transferred from staging buffers. Staging are host memory visible. Storage are device visible but are not set up to transfer or receive data (only for shader storage).

Values:

enumerator eDevice = 0

Type is device memory, source and destination.

enumerator eHost = 1

Type is host memory, source and destination.

enumerator eStorage = 2

Type is Device memory (only)

Public Functions

Tensor(std::shared_ptr<vk::PhysicalDevice> physicalDevice, std::shared_ptr<vk::Device> device, void *data, uint32_t elementTotalCount, uint32_t elementMemorySize, const TensorDataTypes &dataType, const TensorTypes &tensorType = TensorTypes::eDevice)

Constructor with data provided which would be used to create the respective vulkan buffer and memory.

Parameters

• physicalDevice: The physical device to use to fetch properties

• device: The device to use to create the buffer and memory from

• data: Non-zero-sized vector of data that will be used by the tensor

• tensorTypes: Type for the tensor which is of type TensorTypes

~Tensor()

Destructor which is in charge of freeing vulkan resources unless they have been provided externally.

void rebuild(void *data, uint32_t elementTotalCount, uint32_t elementMemorySize)

Function to trigger reinitialisation of the tensor buffer and memory with new data as well as new potential device type.

Parameters

• data: Vector of data to use to initialise vector from

• tensorType: The type to use for the tensor

void destroy()

Destroys and frees the GPU resources which include the buffer and memory.

bool isInit()

Check whether tensor is initialized based on the created gpu resources.

Return

Boolean stating whether tensor is initialized

TensorTypes tensorType()

Retrieve the tensor type of the Tensor

Return

Tensor type of tensor

void recordCopyFrom(const vk::CommandBuffer &commandBuffer, std::shared_ptr<Tensor> copyFromTensor)

Records a copy from the memory of the tensor provided to the current thensor. This is intended to pass memory into a processing, to perform a staging buffer transfer, or to gather output (between others).

Parameters

• commandBuffer: Vulkan Command Buffer to record the commands into

• copyFromTensor: Tensor to copy the data from

void recordCopyFromStagingToDevice(const vk::CommandBuffer &commandBuffer)

Records a copy from the internal staging memory to the device memory using an optional barrier to wait for the operation. This function would only be relevant for kp::Tensors of type eDevice.

Parameters

• commandBuffer: Vulkan Command Buffer to record the commands into

void recordCopyFromDeviceToStaging(const vk::CommandBuffer &commandBuffer)

Records a copy from the internal device memory to the staging memory using an optional barrier to wait for the operation. This function would only be relevant for kp::Tensors of type eDevice.

Parameters

• commandBuffer: Vulkan Command Buffer to record the commands into

void recordPrimaryBufferMemoryBarrier(const vk::CommandBuffer &commandBuffer, vk::AccessFlagBits srcAccessMask, vk::AccessFlagBits dstAccessMask, vk::PipelineStageFlagBits srcStageMask, vk::PipelineStageFlagBits dstStageMask)

Records the buffer memory barrier into the primary buffer and command buffer which ensures that relevant data transfers are carried out correctly.

Parameters

• commandBuffer: Vulkan Command Buffer to record the commands into

• srcAccessMask: Access flags for source access mask

• dstAccessMask: Access flags for destination access mask

• scrStageMask: Pipeline stage flags for source stage mask

• dstStageMask: Pipeline stage flags for destination stage mask

void recordStagingBufferMemoryBarrier(const vk::CommandBuffer &commandBuffer, vk::AccessFlagBits srcAccessMask, vk::AccessFlagBits dstAccessMask, vk::PipelineStageFlagBits srcStageMask, vk::PipelineStageFlagBits dstStageMask)

Records the buffer memory barrier into the staging buffer and command buffer which ensures that relevant data transfers are carried out correctly.

Parameters

• commandBuffer: Vulkan Command Buffer to record the commands into

• srcAccessMask: Access flags for source access mask

• dstAccessMask: Access flags for destination access mask

• scrStageMask: Pipeline stage flags for source stage mask

• dstStageMask: Pipeline stage flags for destination stage mask

vk::DescriptorBufferInfo constructDescriptorBufferInfo()

Constructs a vulkan descriptor buffer info which can be used to specify and reference the underlying buffer component of the tensor without exposing it.

Return

Descriptor buffer info with own buffer

uint32_t size()

Returns the size/magnitude of the Tensor, which will be the total number of elements across all dimensions

Return

Unsigned integer representing the total number of elements

uint32_t dataTypeMemorySize()

Returns the total size of a single element of the respective data type that this tensor holds.

Return

Unsigned integer representing the memory of a single element of the respective data type.

uint32_t memorySize()

Returns the total memory size of the data contained by the Tensor object which would equate to (this->size() * this->dataTypeMemorySize())

Return

Unsigned integer representing the memory of a single element of the respective data type.

TensorDataTypes dataType()

Retrieve the data type of the tensor (host, device, storage)

Return

Data type of tensor of type kp::Tensor::TensorDataTypes

void *rawData()

Retrieve the raw data via the pointer to the memory that contains the raw memory of this current tensor. This tensor gets changed to a nullptr when the Tensor is removed.

Return

Pointer to raw memory containing raw bytes data of Tensor.

void setRawData(const void *data)

Sets / resets the data of the tensor which is directly done on the GPU host visible memory available by the tensor.

template<typename T>
T *data()

Template to return the pointer data converted by specific type, which would be any of the supported types including float, double, int32, uint32 and bool.

Return

Pointer to raw memory containing raw bytes data of Tensor.

template<typename T>
std::vector<T> vector()

Template to get the data of the current tensor as a vector of specific type, which would be any of the supported types including float, double, int32, uint32 and bool.

Return

Vector of type provided by template.

Algorithm

The kp::Algorithm consists primarily of the components required for shader code execution, including the relevant vk::DescriptorSet relatedresources as well as vk::Pipeline and all the relevant Vulkan resources as outlined in the architectural diagram.

class kp::Algorithm

Abstraction for compute shaders that are run on top of tensors grouped via ParameterGroups (which group descriptorsets)

Public Functions

Algorithm(std::shared_ptr<vk::Device> device, const std::vector<std::shared_ptr<Tensor>> &tensors = {}, const std::vector<uint32_t> &spirv = {}, const Workgroup &workgroup = {}, const Constants &specializationConstants = {}, const Constants &pushConstants = {})

Main constructor for algorithm with configuration parameters to create the underlying resources.

Parameters

• device: The Vulkan device to use for creating resources

• tensors: (optional) The tensors to use to create the descriptor resources

• spirv: (optional) The spirv code to use to create the algorithm

• workgroup: (optional) The kp::Workgroup to use for the dispatch which defaults to kp::Workgroup(tensor[0].size(), 1, 1) if not set.

• specializationConstants: (optional) The kp::Constants to use to initialize the specialization constants which cannot be changed once set.

• pushConstants: (optional) The kp::Constants to use when initializing the pipeline, which set the size of the push constants - these can be modified but all new values must have the same vector size as this initial value.

void rebuild(const std::vector<std::shared_ptr<Tensor>> &tensors, const std::vector<uint32_t> &spirv, const Workgroup &workgroup = {}, const Constants &specializationConstants = {}, const Constants &pushConstants = {})

Rebuild function to reconstruct algorithm with configuration parameters to create the underlying resources.

Parameters

• tensors: The tensors to use to create the descriptor resources

• spirv: The spirv code to use to create the algorithm

• workgroup: (optional) The kp::Workgroup to use for the dispatch which defaults to kp::Workgroup(tensor[0].size(), 1, 1) if not set.

• specializationConstants: (optional) The kp::Constants to use to initialize the specialization constants which cannot be changed once set.

• pushConstants: (optional) The kp::Constants to use when initializing the pipeline, which set the size of the push constants - these can be modified but all new values must have the same vector size as this initial value.

~Algorithm()

Destructor for Algorithm which is responsible for freeing and desroying respective pipelines and owned parameter groups.

void recordDispatch(const vk::CommandBuffer &commandBuffer)

Records the dispatch function with the provided template parameters or alternatively using the size of the tensor by default.

Parameters

• commandBuffer: Command buffer to record the algorithm resources to

void recordBindCore(const vk::CommandBuffer &commandBuffer)

Records command that binds the “core” algorithm components which consist of binding the pipeline and binding the descriptorsets.

Parameters

• commandBuffer: Command buffer to record the algorithm resources to

void recordBindPush(const vk::CommandBuffer &commandBuffer)

Records command that binds the push constants to the command buffer provided

• it is required that the pushConstants provided are of the same size as the ones provided during initialization.

Parameters

• commandBuffer: Command buffer to record the algorithm resources to

bool isInit()

function that checks all the gpu resource components to verify if these have been created and returns true if all are valid.

Return

returns true if the algorithm is currently initialized.

void setWorkgroup(const Workgroup &workgroup, uint32_t minSize = 1)

Sets the work group to use in the recordDispatch

Parameters

• workgroup: The kp::Workgroup value to use to update the algorithm. It must have a value greater than 1 on the x value (index 1) otherwise it will be initialized on the size of the first tensor (ie. this->mTensor[0]->size())

void setPush(const Constants &pushConstants)

Sets the push constants to the new value provided to use in the next bindPush()

Parameters

• The: kp::Constant to use to set the push constants to use in the next bindPush(…) calls. The constants provided must be of the same size as the ones created during initialization.

const Workgroup &getWorkgroup()

Gets the current workgroup from the algorithm.

Parameters

• The: kp::Constant to use to set the push constants to use in the next bindPush(…) calls. The constants provided must be of the same size as the ones created during initialization.

const Constants &getSpecializationConstants()

Gets the specialization constants of the current algorithm.

Return

The kp::Constants currently set for specialization constants

const Constants &getPush()

Gets the specialization constants of the current algorithm.

Return

The kp::Constants currently set for push constants

const std::vector<std::shared_ptr<Tensor>> &getTensors()

Gets the current tensors that are used in the algorithm.

Return

The list of tensors used in the algorithm.

OpBase

The kp::OpBase provides a top level class for an operation in Kompute, which is the step that is executed on a GPU submission. The Kompute operations can consist of one or more kp::Tensor.

class kp::OpBase

Base Operation which provides the high level interface that Kompute operations implement in order to perform a set of actions in the GPU.

Operations can perform actions on tensors, and optionally can also own an Algorithm with respective parameters. kp::Operations with kp::Algorithms would inherit from kp::OpBaseAlgo.

Subclassed by kp::OpAlgoDispatch, kp::OpMemoryBarrier, kp::OpTensorCopy, kp::OpTensorSyncDevice, kp::OpTensorSyncLocal

Public Functions

~OpBase()

Default destructor for OpBase class. This OpBase destructor class should always be called to destroy and free owned resources unless it is intended to destroy the resources in the parent class.

void record(const vk::CommandBuffer &commandBuffer) = 0

The record function is intended to only send a record command or run commands that are expected to record operations that are to be submitted as a batch into the GPU.

Parameters

• commandBuffer: The command buffer to record the command into.

void preEval(const vk::CommandBuffer &commandBuffer) = 0

Pre eval is called before the Sequence has called eval and submitted the commands to the GPU for processing, and can be used to perform any per-eval setup steps required as the computation iteration begins. It’s worth noting that there are situations where eval can be called multiple times, so the resources that are created should be idempotent in case it’s called multiple times in a row.

Parameters

• commandBuffer: The command buffer to record the command into.

void postEval(const vk::CommandBuffer &commandBuffer) = 0

Post eval is called after the Sequence has called eval and submitted the commands to the GPU for processing, and can be used to perform any tear-down steps required as the computation iteration finishes. It’s worth noting that there are situations where eval can be called multiple times, so the resources that are destroyed should not require a re-init unless explicitly provided by the user.

Parameters

• commandBuffer: The command buffer to record the command into.

OpAlgoDispatch

The vk::OpAlgoDispatch extends the vk::OpBase class, and provides the base for shader-based operations. Besides of consisting of one or more vk::Tensor as per the vk::OpBase, it also contains a unique vk::Algorithm.

class kp::OpAlgoDispatch : public kp::OpBase

Operation that provides a general abstraction that simplifies the use of algorithm and parameter components which can be used with shaders. By default it enables the user to provide a dynamic number of tensors which are then passed as inputs.

Subclassed by kp::OpMult

Public Functions

OpAlgoDispatch(const std::shared_ptr<kp::Algorithm> &algorithm, const kp::Constants &pushConstants = {})

Constructor that stores the algorithm to use as well as the relevant push constants to override when recording.

Parameters

• algorithm: The algorithm object to use for dispatch

• pushConstants: The push constants to use for override

~OpAlgoDispatch() override

Default destructor, which is in charge of destroying the algorithm components but does not destroy the underlying tensors

void record(const vk::CommandBuffer &commandBuffer) override

This records the commands that are to be sent to the GPU. This includes the barriers that ensure the memory has been copied before going in and out of the shader, as well as the dispatch operation that sends the shader processing to the gpu. This function also records the GPU memory copy of the output data for the staging buffer so it can be read by the host.

Parameters

• commandBuffer: The command buffer to record the command into.

void preEval(const vk::CommandBuffer &commandBuffer) override

Does not perform any preEval commands.

Parameters

• commandBuffer: The command buffer to record the command into.

void postEval(const vk::CommandBuffer &commandBuffer) override

Does not perform any postEval commands.

Parameters

• commandBuffer: The command buffer to record the command into.

OpMult

The kp::OpMult operation is a sample implementation of the kp::OpAlgoBase class. This class shows how it is possible to create a custom vk::OpAlgoBase that can compile as part of the binary. The kp::OpMult operation uses the shader-to-cpp-header-file script to convert the script into cpp header files.

class kp::OpMult : public kp::OpAlgoDispatch

Operation that performs multiplication on two tensors and outpus on third tensor.

Public Functions

OpMult(std::vector<std::shared_ptr<Tensor>> tensors, std::shared_ptr<Algorithm> algorithm)

Default constructor with parameters that provides the bare minimum requirements for the operations to be able to create and manage their sub-components.

Parameters

• tensors: Tensors that are to be used in this operation

• algorithm: An algorithm that will be overridden with the OpMult shader data and the tensors provided which are expected to be 3

~OpMult() override

Default destructor, which is in charge of destroying the algorithm components but does not destroy the underlying tensors

OpTensorCopy

The kp::OpTensorCopy is a tensor only operation that copies the GPU memory buffer data from one kp::Tensor to one or more subsequent tensors.

class kp::OpTensorCopy : public kp::OpBase

Operation that copies the data from the first tensor to the rest of the tensors provided, using a record command for all the vectors. This operation does not own/manage the memory of the tensors passed to it. The operation must only receive tensors of type

Public Functions

OpTensorCopy(const std::vector<std::shared_ptr<Tensor>> &tensors)

Default constructor with parameters that provides the core vulkan resources and the tensors that will be used in the operation.

Parameters

• tensors: Tensors that will be used to create in operation.

~OpTensorCopy() override

Default destructor. This class does not manage memory so it won’t be expecting the parent to perform a release.

void record(const vk::CommandBuffer &commandBuffer) override

Records the copy commands from the first tensor into all the other tensors provided. Also optionally records a barrier.

Parameters

• commandBuffer: The command buffer to record the command into.

void preEval(const vk::CommandBuffer &commandBuffer) override

Does not perform any preEval commands.

Parameters

• commandBuffer: The command buffer to record the command into.

void postEval(const vk::CommandBuffer &commandBuffer) override

Copies the local vectors for all the tensors to sync the data with the gpu.

Parameters

• commandBuffer: The command buffer to record the command into.

OpTensorSyncLocal

The kp::OpTensorSyncLocal is a tensor only operation that maps the data from the GPU device memory into the local host vector.

class kp::OpTensorSyncLocal : public kp::OpBase

Operation that syncs tensor’s local memory by mapping device data into the local CPU memory. For TensorTypes::eDevice it will use a record operation for the memory to be syncd into GPU memory which means that the operation will be done in sync with GPU commands. For TensorTypes::eHost it will only map the data into host memory which will happen during preEval before the recorded commands are dispatched.

Public Functions

OpTensorSyncLocal(const std::vector<std::shared_ptr<Tensor>> &tensors)

Default constructor with parameters that provides the core vulkan resources and the tensors that will be used in the operation. The tensors provided cannot be of type TensorTypes::eStorage.

Parameters

• tensors: Tensors that will be used to create in operation.

~OpTensorSyncLocal() override

Default destructor. This class does not manage memory so it won’t be expecting the parent to perform a release.

void record(const vk::CommandBuffer &commandBuffer) override

For device tensors, it records the copy command for the tensor to copy the data from its device to staging memory.

Parameters

• commandBuffer: The command buffer to record the command into.

void preEval(const vk::CommandBuffer &commandBuffer) override

Does not perform any preEval commands.

Parameters

• commandBuffer: The command buffer to record the command into.

void postEval(const vk::CommandBuffer &commandBuffer) override

For host tensors it performs the map command from the host memory into local memory.

Parameters

• commandBuffer: The command buffer to record the command into.

OpTensorSyncDevice

The kp::OpTensorSyncDevice is a tensor only operation that maps the data from the local host vector into the GPU device memory.

class kp::OpTensorSyncDevice : public kp::OpBase

Operation that syncs tensor’s device by mapping local data into the device memory. For TensorTypes::eDevice it will use a record operation for the memory to be syncd into GPU memory which means that the operation will be done in sync with GPU commands. For TensorTypes::eHost it will only map the data into host memory which will happen during preEval before the recorded commands are dispatched.

Public Functions

OpTensorSyncDevice(const std::vector<std::shared_ptr<Tensor>> &tensors)

Default constructor with parameters that provides the core vulkan resources and the tensors that will be used in the operation. The tensos provided cannot be of type TensorTypes::eStorage.

Parameters

• tensors: Tensors that will be used to create in operation.

~OpTensorSyncDevice() override

Default destructor. This class does not manage memory so it won’t be expecting the parent to perform a release.

void record(const vk::CommandBuffer &commandBuffer) override

For device tensors, it records the copy command for the tensor to copy the data from its staging to device memory.

Parameters

• commandBuffer: The command buffer to record the command into.

void preEval(const vk::CommandBuffer &commandBuffer) override

Does not perform any preEval commands.

Parameters

• commandBuffer: The command buffer to record the command into.

void postEval(const vk::CommandBuffer &commandBuffer) override

Does not perform any postEval commands.

Parameters

• commandBuffer: The command buffer to record the command into.

OpMemoryBarrier

The kp::OpMemoryBarrier is a tensor only operation which adds memory barriers to the tensors provided with the access and stage masks provided.

class kp::OpTensorSyncDevice : public kp::OpBase

Operation that syncs tensor’s device by mapping local data into the device memory. For TensorTypes::eDevice it will use a record operation for the memory to be syncd into GPU memory which means that the operation will be done in sync with GPU commands. For TensorTypes::eHost it will only map the data into host memory which will happen during preEval before the recorded commands are dispatched.

Public Functions

OpTensorSyncDevice(const std::vector<std::shared_ptr<Tensor>> &tensors)

Default constructor with parameters that provides the core vulkan resources and the tensors that will be used in the operation. The tensos provided cannot be of type TensorTypes::eStorage.

Parameters

• tensors: Tensors that will be used to create in operation.

~OpTensorSyncDevice() override

Default destructor. This class does not manage memory so it won’t be expecting the parent to perform a release.

void record(const vk::CommandBuffer &commandBuffer) override

For device tensors, it records the copy command for the tensor to copy the data from its staging to device memory.

Parameters

• commandBuffer: The command buffer to record the command into.

void preEval(const vk::CommandBuffer &commandBuffer) override

Does not perform any preEval commands.

Parameters

• commandBuffer: The command buffer to record the command into.

void postEval(const vk::CommandBuffer &commandBuffer) override

Does not perform any postEval commands.

Parameters

• commandBuffer: The command buffer to record the command into.

Shader

The kp::Shader class contains a set of utilities to compile and process shaders.

class kp::Shader

Shader utily class with functions to compile and process glsl files.

Public Static Functions

std::vector<uint32_t> compileSources(const std::vector<std::string> &sources, const std::vector<std::string> &files = {}, const std::string &entryPoint = "main", std::vector<std::pair<std::string, std::string>> definitions = {}, const TBuiltInResource &resources = Shader::defaultResource)

Compile multiple sources with optional filenames. Currently this function uses the glslang C++ interface which is not thread safe so this funciton should not be called from multiple threads concurrently. If you have a online shader processing multithreading use-case that can’t use offline compilation please open an issue.

Return

The compiled SPIR-V binary in unsigned int32 format

Parameters

• sources: A list of raw glsl shaders in string format

• files: A list of file names respective to each of the sources

• entryPoint: The function name to use as entry point

• definitions: List of pairs containing key value definitions

• resourcesLimit: A list that contains the resource limits for the GLSL compiler

std::vector<uint32_t> compileSource(const std::string &source, const std::string &entryPoint = "main", std::vector<std::pair<std::string, std::string>> definitions = {}, const TBuiltInResource &resources = Shader::defaultResource)

Compile a single glslang source from string value. Currently this function uses the glslang C++ interface which is not thread safe so this funciton should not be called from multiple threads concurrently. If you have a online shader processing multithreading use-case that can’t use offline compilation please open an issue.

Return

The compiled SPIR-V binary in unsigned int32 format

Parameters

• source: An individual raw glsl shader in string format

• entryPoint: The function name to use as entry point

• definitions: List of pairs containing key value definitions

• resourcesLimit: A list that contains the resource limits for the GLSL compiler