CUDA Standard Algorithms » Parallel Transforms

Include the Header

You need to include the header file, taskflow/cuda/algorithm/transform.hpp, for using the parallel-transform algorithm.

#include <taskflow/cuda/algorithm/transform.hpp>

Transform a Range of Items

Parallel-transform algorithm applies the given transform function to a range of items and store the result in another range specified by two iterators, first and last. The task created by tf::cuda_transform(P&& p, I first, I last, O output, C op) represents a parallel execution for the following loop:

while (first != last) {
  *output++ = op(*first++);
}

The following example creates a transform kernel that transforms an input range of N items to an output range by multiplying each item by 10.

tf::cudaDefaultExecutionPolicy policy;

// output[i] = input[i]*10
tf::cuda_transform(
  policy, input, input + N, output, [] __device__ (int x) { return x*10; }
);

// synchronize the execution
policy.synchronize();

Each iteration is independent of each other and is assigned one kernel thread to run the callable. The transform algorithm runs asynchronously through the stream specified in the execution policy. You need to synchronize the stream to obtain correct results.

Transform Two Ranges of Items

You can transform two ranges of items to an output range through a binary operator. The task created by tf::cuda_transform(P&& p, I1 first1, I1 last1, I2 first2, O output, C op) represents a parallel execution for the following loop:

while (first1 != last1) {
  *output++ = op(*first1++, *first2++);
}

The following example creates a transform kernel that transforms two input ranges of N items to an output range by summing each pair of items in the input ranges.

tf::cudaDefaultExecutionPolicy policy;

// output[i] = input1[i] + inpu2[i]
tf::cuda_transform(policy,
  input1, input1+N, input2, output, []__device__(int a, int b) { return a+b; }
); 

// synchronize the execution
policy.synchronize();