CUDA DIM3 CONSTRUCTOR CODE
CUDA DIM3 CONSTRUCTOR FREE

Instruct ArrayFire to finish operations using af::eval() and af::sync().Create ArrayFire arrays from existing CUDA pointers.use cudaDeviceSynchronize() or similar stream functions) Finish any pending CUDA operations (e.g.Include arrayfire.h and af/cuda.h in your source file.

CUDA DIM3 CONSTRUCTOR CODE

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

ArrayFire does not (in general) support in-place memory transactions.

ArrayFire assumes control of all memory provided to it.

Ensure CUDA operations have finished using cudaDeviceSyncronize() or similar commands.ДЄdding ArrayFire to an existing CUDA applicationДЄdding ArrayFire to an existing CUDA application is slightly more involved and can be somewhat tricky due to several optimizations we implement.

Set arguments and run your kernel in your custom stream.

Instruct ArrayFire to finish operations using af::sync().

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:

CUDA DIM3 CONSTRUCTOR FREE

control by the unlock function, there is no need to free them using Because the device pointers, d_x and d_y, were returned to ArrayFire's Return control of af::array memory to ArrayFire using

Here launch with 10 blocks of 10 threads Set arguments and run your kernel in ArrayFire's stream Obtain device pointers from ArrayFire array objects usingД¬udaStream_t af_cuda_stream = afcu::getStream(cuda_id) Run a custom CUDA kernel in the ArrayFire CUDA stream This process is best illustrated with a fully worked example: 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.

Compile with nvcc, linking with the afcuda library.

Return control of af::array memory to ArrayFire.

Set arguments and run your kernel in ArrayFireвЂ™s stream.

Obtain device pointers from ArrayFire array objects using.

Ensure any JIT kernels have executed using af::eval().

Include the вЂaf/afcuda.hвЂ™ header in your source code.

If your kernels can share the ArrayFire CUDA stream, you should: Thus there is a slight amount of bookkeeping that needs to be done in order to integrate your custom CUDA kernel. Adding custom CUDA kernels to an existing ArrayFire applicationД«y default, ArrayFire manages its own memory and operates in its own CUDA stream. Get the current CUDA stream used by ArrayFireД«elow we provide two worked examples on how ArrayFire can be integrated into new and existing projects. Switches ArrayFire to the specified CUDA device ID Switches ArrayFire to the specified deviceД¬onverts an ArrayFire device ID to a CUDA device ID RestoreвЂ™s ArrayFireвЂ™s control over a device memory pointer Removes ArrayFireвЂ™s control of a device memory pointer Obtain a pointer to the device memory (implies lock() ArrayFire provides several functions to ease this process including: FunctionД¬onstruct an ArrayFire Array from device memory It is fairly straightforward to interface ArrayFire with your own custom CUDA code. NOTE: This blog post was written against ArrayFire 3.3.x.В We intend to implement additional convenience functions in ArrayFire 3.4 that should simplify this process further.В Be sure to check the documentation for any updates in future releases. When it comes to interoperability with CUDA, the most important thing to remember about ArrayFire is that it manages its own memory, runs in its own CUDA stream, and creates custom IDs for devices.В As such, most of the interoperability functions focus on reducing potential synchronization conflicts between ArrayFire and CUDA. TodayвЂ™sВ вЂњ Learning ArrayFire from scratchвЂњ, blog post discusses how you can interface ArrayFire and CUDA. For example, you may wish to add ArrayFire to an existing code base to increase your productivity, or you may need to supplement ArrayFireвЂ™s functionality with your own custom implementation of specific algorithms. Although ArrayFire is quite extensive, there remain many cases in which you may want to write custom kernels in CUDA or OpenCL.

Cuda dim3 constructor