Xmake Cuda

Building CUDA with Xmake

Xmake detects CUDA automatically and handles device code + host code compilation, dependency scanning, and device linking.

1. Minimal project

xmake create -P test -l cuda
cd test
xmake

add_rules("mode.debug", "mode.release")

target("test")
    set_kind("binary")
    add_files("src/*.cu")

2. Target kinds

target("app")         set_kind("binary")
target("staticcuda")  set_kind("static")
target("sharedcuda")  set_kind("shared")

3. SDK / version selection

xmake f --cuda=/usr/local/cuda-12.2                 -- specific install
xmake f --cuda=12.2                                 -- version; looks up default install
xmake f --cuda_sdkver=11.8                          -- v3.0.5+, per-project SDK version
xmake f --cuda_sdkver=11.x                          -- any 11.x
xmake f --cuda_sdkver=auto                          -- auto-detect (default)

Persist globally:

xmake g --cuda=/usr/local/cuda-12.2

4. GPU architecture

target("app")
    add_files("src/*.cu")
    add_cugencodes("sm_70", "sm_86", "sm_90")          -- compile for these archs
    add_cugencodes("native")                           -- just the host GPU

add_cugencodes takes sm_XX strings or native. Multiple calls accumulate; xmake emits -gencode=arch=compute_XX,code=sm_XX per entry.

5. Device linking

Device-linking is automatic for binary and shared targets:

target("app")
    set_kind("binary")
    add_files("src/*.cu")
    -- device link runs automatically

Disable it (rarely needed):

set_policy("build.cuda.devlink", false)

static targets — manual device link

Static libraries are not device-linked by default. If a downstream binary has no .cu files but depends on a static cuda lib, you'll hit "undefined reference to __device_..." errors. Fix by opting in:

target("cudalib")
    set_kind("static")
    add_files("src/*.cu")
    add_values("cuda.build.devlink", true)         -- force device link for this static target

6. Mixing CUDA with C++

target("app")
    set_kind("binary")
    set_languages("c++17")
    add_files("src/*.cpp", "src/*.cu")
    add_cugencodes("sm_80")

Host code in .cpp and device code in .cu mix freely in one target. Xmake invokes nvcc for .cu and the normal C++ compiler for .cpp.

7. Flags

target("app")
    add_files("src/*.cu")
    add_cuflags("--use_fast_math", "-lineinfo")       -- nvcc flags
    add_cuflags("-Xcompiler=-fPIC", {force = true})   -- flags passed to host compiler via nvcc

add_cuflags = nvcc (cuda compiler) flags. For flags that must reach the host compiler (gcc/clang/msvc), use -Xcompiler=....

8. Cross-compile / Jetson / Tegra

xmake f -p linux -a arm64 --cuda=/usr/local/cuda-cross-aarch64
xmake

Point --cuda at a cross CUDA SDK. Jetson deployment targets generally set -a arm64.

Pitfalls

• sm_xx vs compute capability. sm_86 means "Ampere RTX 30xx". add_cugencodes takes the binary code name, not the compute cap number.
• Static lib with no device link. Undefined device-symbol errors at final link. add_values("cuda.build.devlink", true) on the static target.
• Mixing hosts. Every .cu file gets compiled through nvcc, which calls the host compiler. If you mix toolchains (clang + nvcc that expects gcc), you hit header incompatibilities. Stick with the nvcc-supported host compiler for the CUDA version.
• CUDA version vs host compiler version. Each CUDA release supports a specific range of gcc/clang/MSVC. Check NVIDIA's docs — "gcc 13 unsupported by CUDA 11.8" is a common surprise.
• Too many sm_ targets. Each add_cugencodes entry doubles compile time. List only the GPUs you actually target.

When to branch out

• Cross-compile generic plumbing → xmake-cross-compilation
• Target basics, host C++ compilation → xmake-targets
• Policies (including build.cuda.devlink) → xmake-policy

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: xmake-io
• Source: xmake-io/xmake-skills
• License: Apache-2.0

Install and usage instructions live in the source repository linked above.