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Parallelizing an Index-Based for-Loop

Explore how to transform index-based for-loops into parallel algorithms using C++17 features like std::for_each combined with std::views::iota and parallel execution policies. Understand thread safety when using lambdas in parallel, simplify parallel loops with utility wrappers, and gain an introduction to GPU-based parallel programming concepts relevant to high-performance C++ development.

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Even though we recommend using algorithms, sometimes a raw, index-based for-loop is required for a specific task. The standard library algorithms provide an equivalent of a range-based for-loop by including the algorithm std::for_each() in the library.

However, there is no algorithm equivalent of an index-based for-loop. In other words, we cannot easily parallelize code like this by simply adding a parallel policy to it:

C++
auto v = std::vector<std::string>{"A", "B", "C"};
for (auto i = 0u; i < v.size(); ++i) {
   v[i] += std::to_string(i+1);
}
// v is now { "A1", "B2", "C3" }

But let’s see how we can build one by combining algorithms. As we will have already concluded, implementing parallel algorithms is complicated. But in this case, we will build a parallel_for() algorithm using std::for_each() as a building block, thus leaving the complex parallelism to std::for_each().

Combining std::for_each() with std::views::iota()

An index-based for-loop based on a standard ...