Efficient Coding with Standard Library Algorithms

Optimizing Code with Standard Library Algorithms

When it comes to writing efficient code, leveraging the power of standard library algorithms can make all the difference. In this article, we’ll explore how using these algorithms can help you avoid common pitfalls and create more robust, high-performance code.

The Importance of Efficiency

Imagine you’re working on a project that requires moving a certain number of elements to the back of a container. You might think that writing a simple loop to achieve this would be sufficient. However, as we’ll see, this approach can lead to performance issues and subtle bugs.

A Naive Approach

Let’s take a look at a possible implementation of the move_n_elements_to_back function:
cpp
template <typename Container>
auto move_n_elements_to_back(Container& c, std::size_t n) {
for (size_t i = 0; i < n; ++i) {
auto value = *std::next(c.begin(), i);
c.emplace_back(std::move(value));
}
c.erase(c.begin(), std::next(c.begin(), n));
}

At first glance, this code seems to work just fine. However, upon closer inspection, we discover that it has some significant limitations. For instance, it doesn’t work with containers of a static size, such as std::array, due to the use of emplace_back(). Moreover, it might throw an exception if memory allocation fails.

Finding a Better Solution

Rather than reinventing the wheel, let’s explore the standard library to see if it provides a suitable algorithm for our problem. Conveniently, the <algorithm> header offers the std::rotate() algorithm, which does exactly what we need while avoiding the aforementioned limitations.

Here’s our revised implementation using std::rotate():
cpp
template <typename Container>
auto move_n_elements_to_back(Container& c, std::size_t n) {
auto new_begin = std::next(c.begin(), n);
std::rotate(c.begin(), new_begin, c.end());
}

The benefits of using std::rotate() are numerous:

• It doesn’t throw exceptions, as it doesn’t allocate memory.
• It works with containers whose size cannot be changed, such as std::array.
• Performance is O(n) regardless of the container it operates on.
• The implementation may be optimized with specific hardware in mind.

The Power of Standard Library Optimizations

Let’s consider another example that highlights the importance of exploiting standard library optimizations. Take the std::find() algorithm, for instance. At first glance, it seems like a simple implementation couldn’t be optimized further. However, the GNU libstdc++ implementation reveals a clever optimization.

When used with random access iterators (such as std::vector, std::string, std::deque, and std::array), the libstdc++ implementers have unrolled the main loop into chunks of four loops at a time. This results in the comparison (it!= last) being executed one-fourth as many times.

Here’s the optimized version of std::find() taken from the libstdc++ library:
cpp
template <typename It, typename Value>
auto find_fast(It first, It last, const Value& value) {
// Main loop unrolled into chunks of four
auto num_trips = (last - first) / 4;
for (auto trip_count = num_trips; trip_count > 0; --trip_count) {
if (*first == value) {return first;} ++first;
if (*first == value) {return first;} ++first;
if (*first == value) {return first;} ++first;
if (*first == value) {return first;} ++first;
}
//...
}

By leveraging these optimizations, we can write more efficient and robust code that takes advantage of the expertise and knowledge embedded in the standard library.