Thrust是否支持跨平台？构建方法咨询及替代GPU计算C++库推荐

Hey there! Let's tackle your questions about Thrust and cross-platform GPU computing libraries clearly:

Absolutely, Thrust is built from the ground up to be cross-platform. It supports a wide range of hardware backends, including:

• NVIDIA GPUs via CUDA
• AMD GPUs via HIP
• Intel GPUs/CPUs via oneAPI (SYCL)
• Multi-core CPUs via OpenMP, TBB, or even serial execution

This means you can write a single Thrust codebase and compile it for different hardware targets without major code changes.

I get it—official build docs can feel scattered, but here are the most reliable ways to set it up:

1. Use with pre-installed toolkits #

If you have the NVIDIA CUDA Toolkit installed, Thrust is already included—no separate build needed. Just include the required headers (like #include <thrust/device_vector.h>) and link against the CUDA runtime in your build system.

For AMD HIP, Thrust is also bundled with the HIP SDK, so you can use it out of the box the same way.

2. CMake + FetchContent (for latest versions) #

If you want access to the newest Thrust features, pull it directly into your CMake project:

include(FetchContent)
FetchContent_Declare(
  thrust
  GIT_REPOSITORY https://github.com/NVIDIA/thrust.git
  GIT_TAG main # Or pin to a specific version like v1.17.0
)
FetchContent_MakeAvailable(thrust)

# Link Thrust to your application
add_executable(your_app main.cpp)
target_link_libraries(your_app PRIVATE thrust::thrust)

This will automatically fetch, build, and link Thrust with the default backend for your system.

3. Vcpkg package manager #

If you use vcpkg, installing Thrust is super straightforward:

vcpkg install thrust

Then in your CMakeLists.txt, just find and link it:

find_package(Thrust REQUIRED)
thrust_create_target(Thrust)
target_link_libraries(your_app PRIVATE Thrust)

Here are some popular, production-ready alternatives that work across multiple GPU/CPU platforms:

• SYCL/Intel oneAPI DPC++: A Khronos standard that extends C++ for heterogeneous computing. It supports NVIDIA, AMD, Intel GPUs, and CPUs. Intel's DPC++ is a widely used implementation with robust tooling.
• HIP: Developed by AMD, HIP lets you write code nearly identical to CUDA, then compile it for AMD GPUs (via HIP) or NVIDIA GPUs (via CUDA). It also supports CPU execution for testing.
• Kokkos: A high-performance computing (HPC) focused library from Sandia National Laboratories. It abstracts hardware details and supports CUDA, HIP, SYCL, OpenMP, TBB, and more—perfect for large-scale scientific computing.
• RAJA: From Lawrence Livermore National Laboratory, RAJA is similar to Kokkos. It's designed for HPC, supports multiple backends, and uses a C++-friendly API to keep code portable without sacrificing performance.
• Boost.Compute: While it's built on OpenCL under the hood, it provides a modern C++ interface that simplifies cross-platform GPU development. It works with any OpenCL-compatible device (NVIDIA, AMD, Intel GPUs, even embedded devices).

内容的提问来源于stack exchange，提问作者jhon