Loop Unrolling - 2020.2 English

Vitis Unified Software Platform Documentation: Application Acceleration Development (UG1393)

Document ID

UG1393

Release Date

2021-03-22

Version

2020.2 English

The compiler can also unroll a loop, either partially or completely to perform multiple loop iterations in parallel. This is done using the pragma HLS unroll . Unrolling a loop can lead to a very fast design, with significant parallelism. However, because all the operations of the loop iterations are executed in parallel, a large amount of programmable logic resource are required to implement the hardware. As a result, the compiler can face challenges dealing with such a large number of resources and can face capacity problems that slow down the kernel compilation process. It is a good guideline to unroll loops that have a small loop body, or a small number of iterations.

vadd: for(int i = 0; i < 20; i++) {
  #pragma HLS UNROLL
  c[i] = a[i] + b[i];
}

In the preceding example, you can see pragma HLS UNROLL has been inserted into the body of the loop to instruct the compiler to unroll the loop completely. All 20 iterations of the loop are executed in parallel if that is permitted by any data dependency.

Tip: Completely unrolling a loop can consume significant device resources, while partially unrolling the loop provides some performance improvement while using fewer hardware resources.

Partially Unrolled Loop

To completely unroll a loop, the loop must have a constant bound (20 in the example above). However, partial unrolling is possible for loops with a variable bound. A partially unrolled loop means that only a certain number of loop iterations can be executed in parallel.

The following code examples illustrates how partially unrolled loops work:

array_sum:for(int i=0;i<4;i++){
  #pragma HLS UNROLL factor=2
  sum += arr[i];
}

In the above example the UNROLL pragma is given a factor of 2. This is the equivalent of manually duplicating the loop body and running the two loops concurrently for half as many iterations. The following code shows how this would be written. This transformation allows two iterations of the above loop to execute in parallel.

array_sum_unrolled:for(int i=0;i<4;i+=2){
  // Manual unroll by a factor 2
  sum += arr[i];
  sum += arr[i+1];
}

Just like data dependencies inside a loop impact the initiation interval of a pipelined loop, an unrolled loop performs operations in parallel only if data dependencies allow it. If operations in one iteration of the loop require the result from a previous iteration, they cannot execute in parallel, but execute as soon as the data from one iteration is available to the next.

Recommended: A good methodology is to PIPELINE loops first, and then UNROLL loops with small loop bodies and limited iterations to improve performance further.