NVIDIA/thrustArchived
Thrust
Thrust 是一个 C++ 并行算法库，提供了一套受标准库启发的接口，用于在多核和加速器硬件上执行。它作为一个 CUDA 加速数据库和通用并行编程接口，旨在实现跨 GPU 和 CPU 的高性能数据处理。
该项目实现了一个便携式抽象层，允许异构计算工作流，使相同的核心算法逻辑能够在不同的硬件加速器上运行。这是通过通用编程策略设计和后端无关的执行模型实现的，该模型将高级函数调用映射到并行硬件。
该库涵盖了广泛的高性能计算能力，包括并行数据操作、数值归约和设备内存管理。它提供了专门的工具，用于在主机系统内存和离散设备内存之间传输数据，以促进排序和搜索等大规模操作。
Features
- C++ Parallelism Libraries - Provides a comprehensive library of parallel primitives and data-parallel templates for high-performance GPU programming in C++.
- Kernel Dispatchers - Maps high-level functional calls to parallel grids of threads on GPU hardware accelerators.
- Cross-Platform Compute Abstractions - Enables a heterogeneous computing workflow where the same core algorithm logic runs on different hardware accelerators.
- Host-to-Device Data Transfers - Provides specialized tools for moving data between host system memory and parallel device memory.
- CPU-GPU Backend Switching Abstractions - Provides abstractions that allow algorithms to switch execution between CPU and GPU backends through a unified interface.
- Device Memory Abstraction Layers - Offers abstraction layers to manage data transfers between system RAM and GPU memory for fast parallel processing.
- Iterator-Based Abstractions - Uses specialized iterator classes to provide a unified interface for varying memory layouts across different hardware.
- Policy-Based Design - Utilizes policy-based design to configure hardware-specific execution and memory models at compile time.
- Parallel Algorithms - Provides fundamental data-parallel operations such as sorting and reduction for high-performance computing.
- Standard C++ Parallel Algorithm Offloads - Provides parallel versions of C++ Standard Template Library (STL) algorithms optimized for device-side execution.
- Parallel Programming Interfaces - Implements a portable abstraction layer for parallel reductions and data transformations across different hardware accelerators.
- CUDA-Accelerated Libraries - Provides a suite of tools for managing memory transfers and executing computations specifically on NVIDIA CUDA hardware.
- Accelerator-Based Data Parallelism - Implements large-scale parallel operations like sorting and searching across massive datasets using hardware accelerators.
- Parallel Reductions - Provides parallel reduction operations to calculate aggregate values like sums and maximums across many processor cores.
- Device-Specific Memory Allocators - Implements customizable memory allocators to decouple memory management from algorithm logic for different device spaces.
- Compile-Time Metaprogramming - Employs compile-time metaprogramming to resolve hardware-specific types and dispatching logic without runtime overhead.
