gpucoder.atomicDec - Atomically decrement a variable in global or shared memory within a specified upper

  bound  - MATLAB ([original](https://in.mathworks.com/help/gpucoder/ref/gpucoder.atomicdec.html)) ([raw](?raw))

Atomically decrement a variable in global or shared memory within a specified upper bound

Since R2021b

Syntax

Description

[A,oldA] = gpucoder.atomicDec([A](#mw%5Fd2e13b13-c45a-4e8a-90df-08f759d21445),[B](#mw%5Fd2e13b13-c45a-4e8a-90df-08f759d21445)) decrements the value of A in global or shared memory within the upper bound B. The operation is atomic in a sense that the entire read-modify-write operation is guaranteed to be performed without interference from other threads. The order of the input and output arguments must match the syntax provided.

example

Examples

collapse all

Perform a simple atomic wrap around decrement operation by using thegpucoder.atomicDec function and generate CUDA® code that calls corresponding CUDAatomicDec() APIs.

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

function a = myAtomicDec(a,b)

coder.gpu.kernelfun; for i =1:numel(a) [a(i),~] = gpucoder.atomicDec(a(i), b); end

end

To create a type for a matrix of doubles for use in code generation, use thecoder.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 myAtomicDec -d myAtomicDec

The generated CUDA code contains the myAtomicDec_kernel1 kernel with calls to the atomicDec() CUDA APIs.

// // File: myAtomicDec.cu // ...

static global launch_bounds(1024, 1) void myAtomicDec_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(idx); atomicDec(&a_data[b_i], b); } } ...

void myAtomicDec(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);
myAtomicDec_kernel1<<<grid, block>>>(b, i, gpu_a_data);
cudaMemcpy(a_data, gpu_a_data, a_size[1] * sizeof(uint32_T),
           cudaMemcpyDeviceToHost);

...

}

Input Arguments

collapse all

Operands, specified as scalars, vectors, matrices, or multidimensional arrays. Inputs A and B must satisfy the following requirements:

Data Types: uint32

Version History

Introduced in R2021b

See Also

Functions

Topics