CuNumpy

CuNumpy lets a Python program use a NumPy-like API while choosing NumPy arrays on the CPU or CuPy arrays on an NVIDIA GPU. In the simplest case, replace import numpy as np with import cunumpy as xp; the array operations you already know then run on the selected backend.

import cunumpy as xp

values = xp.arange(5, dtype=xp.float64)
print(values * 2)
print(xp.get_backend())  # 'numpy' by default

CuNumpy selects an array library for newly requested operations. It does not move existing arrays just because the selected backend changes. This guide covers backend selection, array movement, mixed CPU/GPU workflows, and the helper APIs CuNumpy provides around NumPy and CuPy.

Install

python -m pip install cunumpy

NumPy and array-api-compat are installed as dependencies. To use a GPU, install a CuPy package compatible with your CUDA environment as well. CuPy installation depends on the CUDA version and platform; follow the CuPy installation instructions for your system. CuNumpy does not install CUDA.

array-api-compat supplies NumPy and CuPy compatibility modules with more consistent behavior for shared array operations. CuNumpy uses them internally; your arrays remain ordinary NumPy or CuPy arrays. See why CuNumpy uses array-api-compat for a plain-language explanation and examples.

Choose a backend

CuNumpy starts with NumPy unless ARRAY_BACKEND=cupy is set before import. You can also choose at runtime:

import cunumpy as xp

xp.set_backend("cupy")
print(xp.get_backend())  # 'cupy' if CuPy and CUDA are functional

values = xp.arange(5)  # created by the active backend

The accepted backend names are "numpy" and "cupy". If CuPy is requested but unavailable or not functional, CuNumpy falls back to NumPy. Always check get_backend() when the effective backend matters, such as when reporting configuration or deciding whether GPU-specific work will happen.

Use use_backend() for a temporary selection. It restores the previous selection when the block exits, including when an exception is raised:

with xp.use_backend("numpy"):
    cpu_values = xp.linspace(0, 1, 100)
    assert xp.get_backend() == "numpy"

# The previous global backend is active again here.

The backend selection is process-wide shared state. Do not switch it independently from multiple threads or async tasks; those changes can interfere. A context manager is useful for sequential code, tests, and notebooks.

Understand the two backend questions

The active backend controls which library CuNumpy exposes through its NumPy like operations. The array backend reports where one particular array lives. These can differ: changing the active backend does not convert arrays that already exist.

xp.set_backend("numpy")
cpu_values = xp.arange(3)

gpu_values = xp.to_cupy(cpu_values)  # explicit transfer
print(xp.get_backend())  # 'numpy'
print(xp.get_array_backend(gpu_values))  # 'cupy'

Use is_cpu(array), is_gpu(array), or get_array_backend(array) when dispatch should follow the array passed to a function. get_array_module() returns the matching array_api_compat module, which is useful when writing backend-generic functions:

def vector_norm(values):
    array_xp = xp.get_array_module(values)
    return array_xp.sqrt(array_xp.sum(values * values))

Move data between CPU and GPU

Transfers are explicit so it is clear when data crosses the CPU/GPU boundary:

host = xp.to_numpy(gpu_values)  # CuPy -> NumPy (host)
device = xp.to_cupy(host)  # NumPy/array-like -> CuPy (device)
active = xp.to_cunumpy(host)  # convert to the currently selected backend

to_numpy() also accepts ordinary array-like values. to_cupy() raises ImportError when CuPy or a functional CUDA runtime is unavailable. to_cunumpy() is useful at API boundaries where the consumer expects the currently selected backend. It does not change the original array.

Avoid transferring data inside a tight loop. Keep intermediate arrays on one backend and move only at boundaries such as file I/O, plotting, or a CPU-only library call. For example:

with xp.use_backend("cupy"):
    signal = xp.asarray(host_signal)
    filtered = xp.fft.rfft(signal)
    result = xp.to_numpy(filtered)  # one transfer for a CPU-only consumer

Random numbers and dtypes

get_rng(seed) returns a random generator for the active backend. NumPy and CuPy have similar generator APIs, though exact bit-for-bit sequences are not guaranteed to match between libraries:

rng = xp.get_rng(seed=42)
samples = rng.normal(size=1000)

Use default_float_dtype() when code needs to explicitly request the active backend's float64 dtype rather than rely on Python scalar inference:

x = xp.asarray([1.0, 2.0], dtype=xp.default_float_dtype())

GPU selection and memory helpers

These helpers are useful for multi-GPU programs and for understanding CuPy's memory behavior:

print("visible GPUs:", xp.device_count())
xp.set_device(0)  # selects CUDA device 0 when CuPy is active
print("memory (free, total):", xp.memory_info())

set_device() is a no-op on NumPy. device_count() checks visible CUDA hardware even if the active backend is NumPy; it returns zero when CuPy/CUDA cannot be used. memory_info() returns (free_bytes, total_bytes) on the active CuPy device and None on NumPy. set_device_for_rank(rank) is a round-robin convenience for MPI layouts where local ranks map contiguously to GPUs. If your scheduler uses a different mapping, select the device directly.

For MPI programs with one rank per GPU, bind_local_device() selects the GPU from the node-local rank that the MPI launcher exports (local_rank()), so it can run before MPI is initialized, as CUDA-aware MPI requires. Before passing device buffers to MPI, call synchronize_for_mpi(*buffers): kernels run asynchronously, and MPI would otherwise send a buffer a kernel is still writing, without an error.

xp.set_backend("cupy")
xp.bind_local_device()  # before MPI_Init
from mpi4py import MPI

xp.synchronize_for_mpi(send, recv)
MPI.COMM_WORLD.Sendrecv(send, dest, recvbuf=recv, source=source)

CuPy caches released allocations in memory pools. This can make process-level GPU memory appear occupied after arrays go out of scope. free_memory() asks CuPy to release currently free cached blocks; it does not free memory still referenced by live arrays.

pin_memory(host_array) makes a pinned host copy, which can improve transfer throughput for workloads that explicitly manage asynchronous transfers. stream() creates a non-blocking CuPy stream and yields it; it yields None on NumPy. GPU work is asynchronous, so synchronize before reading results on the host:

with xp.stream():
    device = xp.to_cupy(host)
    transformed = xp.fft.fft(device)

xp.synchronize()
result = xp.to_numpy(transformed)

Use NumPy-only kernels with CuPy arrays

PyccelKernel adapts a callable that expects NumPy arrays. When conversion is needed, CuNumpy copies CuPy inputs to the host, calls the wrapped function, copies in-place output changes back to the device, and moves returned NumPy arrays to CuPy. With NumPy inputs, the wrapper calls the function directly. CuNumpy does not compile functions or import Pyccel for you.

import cunumpy as xp


def scale_in_place(values, factor):
    values[:] *= factor
    return values


scale = xp.PyccelKernel(scale_in_place, outputs=(0,))

with xp.use_backend("cupy"):
    values = xp.arange(5, dtype=xp.float64)
    returned = scale(values, 3.0)
    xp.synchronize()

By default every converted argument is copied back, because the wrapper cannot know which arguments the kernel changed. outputs=(0,) declares that positional argument 0 is written, avoiding unnecessary copy-back for read-only inputs. For a keyword call, declare the keyword name, such as outputs=("out",). A wrong declaration can leave GPU output values stale. The wrapper can also traverse arrays nested in lists, tuples, dictionaries, and selected application objects; see the full API reference for object_modules, is_array, aliasing, and output declarations.

Write CUDA kernels next to host kernels

CudaKernel wraps a CUDA C kernel (compiled with NVRTC through cupy.RawKernel) so that it is called with the same arguments as the host kernel it mirrors, plus the number of threads. Arrays are never copied: they must be CuPy arrays. The extern "C" __global__ signature is parsed once and every call is checked against it: Python scalars are cast to the declared C types, and a wrong argument count, an array of the wrong dtype, or a scalar that does not fit its type raises instead of silently producing wrong values.

Kernel pairs a host kernel with its CUDA kernel and calls the one matching the active backend, so kernels can be ported to CUDA one at a time:

import cunumpy as xp

AXPY = r"""
extern "C" __global__
void axpy(double a, const double* x, double* y, int n) {
    int i = blockDim.x * blockIdx.x + threadIdx.x;
    if (i < n) y[i] += a * x[i];
}
"""


def axpy(a, x, y, n):  # host version, e.g. compiled with Pyccel
    for i in range(n):
        y[i] += a * x[i]


kernel = xp.Kernel(axpy, xp.CudaKernel(AXPY, "axpy"))

with xp.use_backend("cupy"):
    x = xp.arange(1000, dtype=xp.float64)
    y = xp.zeros(1000)
    kernel(2.0, x, y, 1000, n_threads=1000)  # runs the CUDA kernel

On the CuPy backend, a Kernel without CUDA kernel raises NotImplementedError (or, with missing_cuda="fallback", runs the host kernel through PyccelKernel, with host copies; host_options configure that PyccelKernel). KernelCatalog.from_package() collects kernel pairs from a package with one folder per kernel (name/name_kernels.py and name/name_cuda.cu), and catalog.compile_all() compiles all CUDA kernels at setup.

Groups of arguments can be passed as one: objects implementing __cuda_args__() (see CudaArguments) are flattened into several kernel arguments, and CudaStruct defines a C struct once (its C declaration and the matching memory layout) and packs values into it, which the kernel takes as one parameter:

Vec = xp.CudaStruct("Vec", [("data", "double*"), ("n", "int")])
scale = xp.CudaKernel(
    Vec.declaration
    + r"""
    extern "C" __global__ void scale(Vec v, double a) {
        int i = blockDim.x * blockIdx.x + threadIdx.x;
        if (i < v.n) v.data[i] *= a;
    }""",
    "scale",
    structs=[Vec],
)
scale(Vec(data=y, n=y.size), 0.5, n_threads=y.size)

Launches can be 1D to 3D (n_threads=(nx, ny), block_size=(16, 16)) or use an explicit grid, with dynamic shared memory (shared_mem) and a stream. C++ function templates are instantiated with template_args, and CudaKernelVariants caches kernels whose source is generated per variant (e.g. per dimension and dtype). See the API reference for details.

Pyodide

CuNumpy supports the NumPy backend in Pyodide. It does not provide CuPy/CUDA there. Ordinary Python callables can be wrapped with PyccelKernel without compilation. See the Pyodide guide for a complete installation example and compatibility notes.

Documentation

The user guide explains common workflows. The API reference documents each helper and its behavior. The Pyodide guide covers WebAssembly usage.