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Reduction Strategy in C Language

The reduction strategy is a common algorithmic approach used in parallel programming to combine the results of parallel computations into a single result. It involves repeatedly applying a binary operation to pairs of elements in a data set until a single result is obtained.

In C language, the reduction strategy can be implemented using parallel programming frameworks such as OpenMP or MPI. The general steps involved in implementing a reduction using OpenMP are as follows:

  1. Initialize the shared variable to the identity element of the operation being performed. For example, if the operation is a summation, initialize the shared variable to zero.
  2. Divide the data set into equal-sized chunks and assign each chunk to a thread.
  3. Each thread performs the operation on its assigned chunk of the data set.
  4. Combine the results of each thread using the binary operation.
  5. Repeat steps 3 and 4 until only a single result remains.

Here’s an example of implementing the reduction strategy using OpenMP in C language for calculating the sum of an array of integers:

#include <stdio.h>
#include <omp.h>

#define ARRAY_SIZE 1000000

int main() {
  int array[ARRAY_SIZE];
  int i, sum = 0;

  // Initialize array with values
  for (i = 0; i < ARRAY_SIZE; i++) {
    array[i] = i + 1;

  #pragma omp parallel for reduction(+:sum)
  for (i = 0; i < ARRAY_SIZE; i++) {
    sum += array[i];

  printf("Sum of array elements: %d\n", sum);

  return 0;

In this code, we have an array of integers and we use OpenMP to perform the sum of the elements in parallel. The reduction clause specifies that the variable sum should be used to accumulate the result of each thread’s computation, and that the binary operation being performed is an addition (+). OpenMP automatically divides the loop iteration space among the threads and combines the results using the specified binary operation.

The reduction strategy is a useful approach for improving the performance of parallel computations by minimizing the overhead of thread synchronization and communication.

Amitesh Kumar

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