A high-performance general-purpose compute library

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Interoperability with CUDA

Although ArrayFire is quite extensive, there remain many cases in which you may want to write custom kernels in CUDA or OpenCL. For example, you may wish to add ArrayFire to an existing code base to increase your productivity, or you may need to supplement ArrayFire's functionality with your own custom implementation of specific algorithms.

ArrayFire manages its own memory, runs within its own CUDA stream, and creates custom IDs for devices. As such, most of the interoperability functions focus on reducing potential synchronization conflicts between ArrayFire and CUDA.

# Basics

It is fairly straightforward to interface ArrayFire with your own custom CUDA code. ArrayFire provides several functions to ease this process including:

Function | Purpose
--- | ---
af::array(...) | Construct an ArrayFire Array from device memory
af::array.device() | Obtain a pointer to the device memory (implies `lock()`)
af::array.lock() | Removes ArrayFire's control of a device memory pointer
af::array.unlock() | Restores ArrayFire's control over a device memory pointer
af::getDevice() | Gets the current ArrayFire device ID
af::setDevice() | Switches ArrayFire to the specified device
afcu::getNativeId() | Converts an ArrayFire device ID to a CUDA device ID
afcu::setNativeId() | Switches ArrayFire to the specified CUDA device ID
afcu::getStream() | Get the current CUDA stream used by ArrayFire

Below we provide two worked examples on how ArrayFire can be integrated into new and existing projects.

# Adding custom CUDA kernels to an existing ArrayFire application

By default, ArrayFire manages its own memory and operates in its own CUDA stream. Thus there is a slight amount of bookkeeping that needs to be done in order to integrate your custom CUDA kernel.

If your kernels can share the ArrayFire CUDA stream, you should:

1. Include the 'af/afcuda.h' header in your source code
2. Use ArrayFire as normal
3. Ensure any JIT kernels have executed using `af::eval()`
4. Obtain device pointers from ArrayFire array objects using `array::device()`
5. Determine ArrayFire's CUDA stream
6. Set arguments and run your kernel in ArrayFire's stream
7. Return control of af::array memory to ArrayFire
8. Compile with `nvcc`, linking with the `afcuda` library.

Notice that since ArrayFire and your kernels are sharing the same CUDA stream, there is no need to perform any synchronization operations as operations within a stream are executed in order.

This process is best illustrated with a fully worked example:

// 1. Add includes
#include <arrayfire.h>
#include <af/cuda.h>
int main() {
// 2. Use ArrayFire as normal
size_t num = 10;
af::array x = af::constant(0, num);
// ... many ArrayFire operaitons here
// 3. Ensure any JIT kernels have executed
x.eval();
af_print(x);
// Run a custom CUDA kernel in the ArrayFire CUDA stream
// 4. Obtain device pointers from ArrayFire array objects using
// the array::device() function:
float *d_x = x.device<float>();
// 5. Determine ArrayFire's CUDA stream
int af_id = af::getDevice();
int cuda_id = afcu::getNativeId(af_id);
cudaStream_t af_cuda_stream = afcu::getStream(cuda_id);
// 6. Set arguments and run your kernel in ArrayFire's stream
// Here launch with 1 block of 10 threads
increment<<<1, num, 0, af_cuda_stream>>>(d_x);
// 7. Return control of af::array memory to ArrayFire using
// the array::unlock() function:
x.unlock();
// ... resume ArrayFire operations
af_print(x);
// Because the device pointer `d_x` was returned to ArrayFire's
// control by the unlock function, there is no need to free them using
// cudaFree()
return 0;
}
arrayfire.h
af::array
A multi dimensional data container.
Definition array.h:37
af::array::device
T * device() const
Get the device pointer from the array and lock the buffer in memory manager.
af::array::eval
void eval() const
Evaluate any JIT expressions to generate data for the array.
af::array::unlock
void unlock() const
Unlocks the device buffer in the memory manager.
cuda.h
afcu::getStream
static cudaStream_t getStream(int id)
Get the stream for the CUDA device with id in ArrayFire context.
Definition cuda.h:110
afcu::getNativeId
static int getNativeId(int id)
Get the native device id of the CUDA device with id in ArrayFire context.
Definition cuda.h:129
af::constant
array constant(T val, const dim4 &dims, const dtype ty=(af_dtype) dtype_traits< T >::ctype)
C++ Interface to generate an array with elements set to a specified value.
af::getDevice
AFAPI int getDevice()
Gets the current device ID.
af_print
#define af_print(...)
Definition util.h:148

If your kernels needs to operate in their own CUDA stream, the process is essentially identical, except you need to instruct ArrayFire to complete its computations using the af::sync() function prior to launching your own kernel and ensure your kernels are complete using `cudaDeviceSynchronize()` (or similar) commands prior to returning control of the memory to ArrayFire:

1. Include the 'af/afcuda.h' header in your source code
2. Use ArrayFire as normal
3. Ensure any JIT kernels have executed using `af::eval()`
4. Instruct ArrayFire to finish operations using af::sync()
5. Obtain device pointers from ArrayFire array objects using
6. Determine ArrayFire's CUDA stream
7. Set arguments and run your kernel in your custom stream
8. Ensure CUDA operations have finished using `cudaDeviceSyncronize()` or similar commands.
9. Return control of af::array memory to ArrayFire
10. Compile with `nvcc`, linking with the `afcuda` library.

# Adding ArrayFire to an existing CUDA application

Adding ArrayFire to an existing CUDA application is slightly more involved and can be somewhat tricky due to several optimizations we implement. The most important are as follows:

- ArrayFire assumes control of all memory provided to it.
- ArrayFire does not (in general) support in-place memory transactions.

We will discuss the implications of these items below. To add ArrayFire to existing code you need to:

1. Include `arrayfire.h` and `af/cuda.h` in your source file
2. Finish any pending CUDA operations (e.g. use cudaDeviceSynchronize() or similar stream functions)
3. Create ArrayFire arrays from existing CUDA pointers
4. Perform operations on ArrayFire arrays
5. Instruct ArrayFire to finish operations using af::eval() and af::sync()
6. Obtain pointers to important memory
7. Continue your CUDA application.
8. Free non-managed memory
9. Compile and link with the appropriate paths and the `-lafcuda` flags.

To create the af::array objects, you should use one of the following constructors with `src=afDevice`:

// 1D - 3D af::array constructors
af::array (dim_t dim0, const T *pointer, af::source src=afHost)
af::array (dim_t dim0, dim_t dim1, const T *pointer, af::source src=afHost)
af::array (dim_t dim0, dim_t dim1, dim_t dim2, const T *pointer, af::source src=afHost)
af::array (dim_t dim0, dim_t dim1, dim_t dim2, dim_t dim3, const T *pointer, af::source src=afHost)
// af::array constructor using a dim4 object
af::array (const dim4 &dims, const T *pointer, af::source src=afHost)
dim_t
long long dim_t
Definition defines.h:56
af_source
af_source
Definition defines.h:232
afHost
@ afHost
Host pointer.
Definition defines.h:234

NOTE: With all of these constructors, ArrayFire's memory manager automatically assumes responsibility for any memory provided to it. Thus ArrayFire could free or reuse the memory at any later time. If this behavior is not desired, you may call `array::unlock()` and manage the memory yourself. However, if you do so, please be cautious not to free memory when ArrayFire might be using it!

The seven steps above are best illustrated using a fully-worked example:

// 1. Add includes
#include <arrayfire.h>
#include <af/cuda.h>
using namespace std;
int main() {
// Create and populate CUDA memory objects
const int elements = 100;
size_t size = elements * sizeof(float);
float *cuda_A;
cudaMalloc((void**) &cuda_A, size);
// ... perform many CUDA operations here
// 2. Finish any pending CUDA operations
cudaDeviceSynchronize();
// 3. Create ArrayFire arrays from existing CUDA pointers.
// Be sure to specify that the memory type is afDevice.
af::array d_A(elements, cuda_A, afDevice);
// NOTE: ArrayFire now manages cuda_A
// 4. Perform operations on the ArrayFire Arrays.
d_A = d_A * 2;
// NOTE: ArrayFire does not perform the above transaction using
// in-place memory, thus the pointers containing memory to d_A have
// likely changed.
// 5. Instruct ArrayFire to finish pending operations using eval and sync.
af::eval(d_A);
af::sync();
// 6. Get pointers to important memory objects.
// Once device is called, ArrayFire will not manage the memory.
float * outputValue = d_A.device<float>();
// 7. continue CUDA application as normal
// 8. Free non-managed memory
// We removed outputValue from ArrayFire's control, we need to free it
cudaFree(outputValue);
return 0;
}
afDevice
@ afDevice
Device pointer.
Definition defines.h:233
af::eval
array & eval(array &a)
Evaluate an expression (nonblocking).
Definition array.h:1467
af::sync
AFAPI void sync(const int device=-1)
Blocks until the device is finished processing.

# Using multiple devices

If you are using multiple devices with ArrayFire and CUDA kernels, there is one "gotcha" of which you should be aware. ArrayFire implements its own internal order of compute devices, thus a CUDA device ID may not be the same as an ArrayFire device ID. Thus when switching between devices it is important that you use our interoperability functions to get/set the correct device IDs. Below is a quick listing of the various functions needed to switch between devices along with some disambiguation as to the device identifiers used with each function:

Function | ID Type | Purpose
--- | --- | ---
cudaGetDevice() | CUDA | Gets the current CUDA device ID
cudaSetDevice() | CUDA | Sets the current CUDA device
af::getDevice() | AF | Gets the current ArrayFire device ID
af::setDevice() | AF | Sets the current ArrayFire device
afcu::getNativeId() | AF -> CUDA | Convert an ArrayFire device ID to a CUDA device ID
afcu::setNativeId() | CUDA -> AF | Set the current ArrayFire device from a CUDA ID