gpucoder.atomicXor

Atomically perform bit-wise XOR between value and variable in global or shared memory

Since R2021b

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    Syntax

    A = gpucoder.atomicXor(A,B)
    [A,oldA] = gpucoder.atomicXor(A,B)

    Description

    The gpucoder.atomicXor function reads a value from a global or shared GPU memory location, performs bit-wise XOR with the value, and writes the result back to the memory location. In generated GPU code, the operation is atomic, which means that a GPU thread performs the read-modify-write operation without interference from other threads.

    A = gpucoder.atomicXor(A,B) perform bit-wise XOR between B and the value of A and writes the results back into A.

    Call the gpucoder.atomicXor function directly inside a for-loop that you want to execute on the GPU in the generated CUDA® code. Each iteration of the loop must be able to read and write to A.

    example

    [A,oldA] = gpucoder.atomicXor(A,B) returns the previous value of A as oldA. When you use this syntax, use the coder.gpu.kernel pragma before the loop that contains gpucoder.atomicXor.

    Examples

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    Perform a simple atomic addition operation by using the gpucoder.atomicXor function and generate CUDA code that calls appropriate CUDA atomicXor() APIs.

    In one file, write an entry-point function myAtomicXor that accepts matrix inputs a and b.

    function a = myAtomicXor(a,b)
coder.gpu.kernelfun;

for i =1:numel(a)
    a(i) = gpucoder.atomicXor(a(i), b);
end

end

    To create a type for a matrix of uint32 for use in code generation, use the coder.newtype function.

    A = coder.newtype('uint32', [1 30], [0 1]);
B = coder.newtype('uint32', [1 1], [0 0]);
inputArgs = {A,B};

    To generate a CUDA library, use the codegen function.

    cfg = coder.gpuConfig('lib');
cfg.GenerateReport = true;

codegen -config cfg -args inputArgs myAtomicXor -d myAtomicXor

    The generated CUDA code contains the myAtomicXor_kernel1 kernel with calls to the atomicXor() CUDA APIs.

    //
// File: myAtomicXor.cu
//
...

static __global__ __launch_bounds__(1024, 1) void myAtomicXor_kernel1(
    const uint32_T b, const int32_T i, uint32_T a_data[])
{
  uint64_T loopEnd;
  uint64_T threadId;
...

  for (uint64_T idx{threadId}; idx <= loopEnd; idx += threadStride) {
    int32_T b_i;
    b_i = static_cast<int32_T>(idx);
    atomicXor(&a_data[b_i], b);
  }
}
...

void myAtomicXor(uint32_T a_data[], int32_T a_size[2], uint32_T b)
{
  dim3 block;
  dim3 grid;
...

    cudaMemcpy(gpu_a_data, a_data, a_size[1] * sizeof(uint32_T),
               cudaMemcpyHostToDevice);
    myAtomicXor_kernel1<<<grid, block>>>(b, i, gpu_a_data);
    cudaMemcpy(a_data, gpu_a_data, a_size[1] * sizeof(uint32_T),
               cudaMemcpyDeviceToHost);
...

}

    Input Arguments

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    Reference to a shared or global GPU memory location, specified as a scalar. Use the reference as an input and output argument of gpucoder.atomicXor.

    Data Types: int32 | uint32 | uint64

    Operand, specified as a scalar.

    Data Types: int32 | uint32 | uint64

    Extended Capabilities

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    C/C++ Code Generation
    Generate C and C++ code using MATLAB® Coder™.

    GPU Code Generation
    Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

    Version History

    Introduced in R2021b

    See Also

    Functions

    Topics