Sorting in Cilk
In Cilk, we can use the C++ Standard Library for sorting operations. The <algorithm>
header provides sorting functionality for various container types.
#include <cilk/cilk.h>
#include <iostream>
#include <vector>
#include <string>
#include <algorithm>
int main() {
// Sorting functions work for any comparable type.
// For strings, we can use std::vector<string>.
std::vector<std::string> strs = {"c", "a", "b"};
std::sort(strs.begin(), strs.end());
std::cout << "Strings: ";
for (const auto& s : strs) {
std::cout << s << " ";
}
std::cout << std::endl;
// An example of sorting integers.
std::vector<int> ints = {7, 2, 4};
std::sort(ints.begin(), ints.end());
std::cout << "Ints: ";
for (const auto& i : ints) {
std::cout << i << " ";
}
std::cout << std::endl;
// We can also use the algorithm library to check if
// a vector is already in sorted order.
bool s = std::is_sorted(ints.begin(), ints.end());
std::cout << "Sorted: " << (s ? "true" : "false") << std::endl;
return 0;
}
This Cilk program demonstrates sorting functionality similar to the original example. Here’s a breakdown of the changes:
We include necessary headers:
<cilk/cilk.h>
for Cilk support,<iostream>
for input/output,<vector>
for dynamic arrays,<string>
for string operations, and<algorithm>
for sorting functions.Instead of slices, we use
std::vector
which is a dynamic array in C++.The
sort
function from the<algorithm>
library is used to sort the vectors. It takes iterators to the beginning and end of the range to be sorted.To print the sorted vectors, we use range-based for loops, which provide a more convenient way to iterate over containers in C++.
The
is_sorted
function from the<algorithm>
library is used to check if a vector is sorted, similar to theslices.IsSorted
function in the original example.
To compile and run this Cilk program:
$ cilk++ -std=c++11 sorting.cpp -o sorting
$ ./sorting
Strings: a b c
Ints: 2 4 7
Sorted: true
Note that Cilk extends C++, so we can use C++ standard library functions and containers. The Cilk runtime will automatically parallelize certain operations when possible, potentially providing performance benefits for larger datasets.