Data Parallelism

In this exercise you will learn how add the values in two arrays and write the results to another array in parallel using parallel_for.

1.) Create your queue

Create a queue to enqueue your kernel function to, remember to handle errors.

2. ) Create buffers

Create buffers to manage the data of the two input arrays and output array.

Remember to ensure the range provided to the buffer is the size of the arrays.

3. ) Create accessors

Create accessors to each of the buffers within the command group function.

4. ) Write the kernel function

Now enqueue parallel kernel function by calling parallel_for on the handler.

4.1 ) Use the range and id variant

This version of parallel_for takes a range specifying the number of iterations of the kernel function to invoke and the kernel function itself must take an id which represents the current iteration.

The id can be used in the accessor subscript operator to access or assign to the corresponding element of data that the accessor represents.

4.2 ) Use the nd_range and nd_item variant

This version of parallel_for takes an nd_range which is made up of two ranges describing the global range and the local range (work-group size). The kernel function must take an nd_item, which cannot be passed directly to the subscript operator of an accessor. Instead, retrieve the id using the get_global_id member function.

Build And Execution Hints

For DevCloud via JupiterLab follow these instructions.

For DPC++: instructions.

For AdaptiveCpp: instructions.