AnswerDotAI/gpu.cpp

• WebGPU Implementations - Provides a C++ implementation and abstraction layer based on the WebGPU specification for cross-platform GPU compute.
• GPU Hardware Abstraction Layers - Acts as a portable interface abstracting device initialization and shader dispatch across diverse operating systems and hardware.
• Compute Shader Programming - Enables the execution of general-purpose compute kernels across different hardware vendors and operating systems.
• Tensor Lifecycle Management - Provides a system for allocating and managing the lifetime of multi-dimensional memory buffers on graphics hardware.
• GPU Compute Frameworks - Offers a portable framework for executing parallel mathematical operations across various GPU hardware vendors.
• GPU Resource Management - Manages the allocation and lifecycle of GPU tensors and buffers for high-speed computation.
• Compute Shader Dispatchers - Implements mechanisms for executing general-purpose parallel processing tasks on graphics processors across platforms.
• Tensor Memory Lifetime Management - Tracks and releases hardware tensor buffers to manage the lifetime of GPU assets and prevent memory leaks.
• GPU Device Initializations - Implements the processes for requesting hardware adapters and creating logical devices to enable GPU access.
• GPU Memory Orchestration - Orchestrates the transfer of data and synchronization between host memory and GPU memory.
• GPU Staging Buffers - Coordinates bidirectional data movement between the system processor and graphics processor using intermediate staging buffers.
• GPU Kernel Orchestration - Manages the full lifecycle of defining, launching, and synchronizing parallel compute kernels on GPU hardware.
• GPU Kernel Programming - Provides low-level primitives for defining and dispatching compute kernels with custom shader code.
• Compute Shader Kernels - Defines reusable compute units by binding shader code and workgroup metadata to hardware handles.
• Kernel Definitions - Creates reusable GPU kernels from shader code by binding input and output tensors and parameters.
• GPU Command Queues - Provides a non-blocking command queue to dispatch compute kernels for asynchronous GPU execution.
• Asynchronous Kernel Launchers - Decouples task scheduling from hardware execution by submitting kernels to the GPU queue asynchronously.
• Tensor Memory Management - Specifies the rank and size of dimensions to determine the structural memory layout of tensors.
• Manual Memory Management - Allows for the explicit disposal of tensor resources to manually update the resource pool and free memory.
• Device-to-Host Data Transfers - Coordinates staging buffers and callbacks to move information from hardware memory back to system memory.
• Host-to-Device Data Transfers - Moves memory from the system processor to the graphics processor to provide inputs for compute kernels.
• Non-Owning Tensor Views - Creates non-owning views of hardware buffers using offsets and shapes to avoid duplicating GPU memory.
• Tensor Slicing - Creates non-owning views of tensors using offsets to reference specific data segments without duplicating memory.
• GPU Operation Synchronizers - Ensures data consistency by waiting for asynchronous graphics processing operations to complete.
• Shader Metadata Management - Stores compute shader code and metadata such as workgroup size and entry points to prepare for execution.
• GPU Bind Group Mappings - Implements mechanisms for mapping tensors and parameters to WebGPU bind group indices for kernel access.
• GPU Buffer Management - Allocates and maintains contiguous blocks of hardware memory for mathematical computation storage.
• Resource Bindings - Maps buffers and tensors to specific indices so a GPU kernel can access them during execution.
• GPU Memory Allocators - Creates multi-dimensional memory buffers on the graphics processor for high-speed mathematical calculations.
• Handle Management - Provides a mechanism to store and reuse compute kernel handles to avoid expensive hardware setup costs.
• Asynchronous GPU Tasking - Coordinates the dispatch of GPU kernels using asynchronous primitives to manage non-blocking execution.

gpu.cpp 是一个轻量级的 C++ 库，用于跨不同硬件供应商和操作系统执行底层通用 GPU 计算。它作为一个便携式 GPU 包装器、内核编排器和张量管理系统，利用 WebGPU 规范来抽象设备初始化、缓冲区传输和计算着色器调度。