What is the optimal way to concatenate two vectors whilst transforming elements of one vector?

Question

Suppose I have

std::vector<T1> vec1 {/* filled with T1's */};
std::vector<T2> vec2 {/* filled with T2's */};

and some function T1 f(T2) which could of course be a lambda. What is the optimal way to concatenate vec1 and vec2 whilst applying f to each T2 in vec2?

The apparently obvious solution is std::transform, i.e.

vec1.reserve(vec1.size() + vec2.size());
std::transform(vec2.begin(), vec2.end(), std::back_inserter(vec1), f);

but I say this is not optimal as std::back_inserter must make an unnecessary capacity check on each inserted element. What would be optimal is something like

vec1.insert(vec1.end(), vec2.begin(), vec2.end(), f);

which could get away with a single capacity check. Sadly this is not valid C++. Essentially this is the same reason why std::vector::insert is optimal for vector concatenation, see this question and the comments in this question for further discussion on this point.

So:

1. Is std::transform the optimal method using the STL?
2. If so, can we do better?
3. Is there a good reason why the insert function described above was left out of the STL?

UPDATE

I've had a go at verifying if the multiple capacity checks do have any noticeable cost. To do this I basically just pass the id function (f(x) = x) to the std::transform and push_back methods discussed in the answers. The full code is:

#include <iostream>
#include <vector>
#include <iterator>
#include <algorithm>
#include <cstdint>
#include <chrono>
#include <numeric>
#include <random>

using std::size_t;

std::vector<int> generate_random_ints(size_t n)
{
    std::default_random_engine generator;
    auto seed1 = std::chrono::system_clock::now().time_since_epoch().count();
    generator.seed((unsigned) seed1);
    std::uniform_int_distribution<int> uniform {};
    std::vector<int> v(n);
    std::generate_n(v.begin(), n, [&] () { return uniform(generator); });
    return v;
}

template <typename D=std::chrono::nanoseconds, typename F>
D benchmark(F f, unsigned num_tests)
{
    D total {0};
    for (unsigned i = 0; i < num_tests; ++i) {
        auto start = std::chrono::system_clock::now();
        f();
        auto end = std::chrono::system_clock::now();
        total += std::chrono::duration_cast<D>(end - start);
    }
    return D {total / num_tests};
}

template <typename T>
void std_insert(std::vector<T> vec1, const std::vector<T> &vec2)
{
    vec1.insert(vec1.end(), vec2.begin(), vec2.end());
}

template <typename T1, typename T2, typename UnaryOperation>
void push_back_concat(std::vector<T1> vec1, const std::vector<T2> &vec2, UnaryOperation op)
{
    vec1.reserve(vec1.size() + vec2.size());
    for (const auto& x : vec2) {
        vec1.push_back(op(x));
    }
}

template <typename T1, typename T2, typename UnaryOperation>
void transform_concat(std::vector<T1> vec1, const std::vector<T2> &vec2, UnaryOperation op)
{
    vec1.reserve(vec1.size() + vec2.size());
    std::transform(vec2.begin(), vec2.end(), std::back_inserter(vec1), op);
}

int main(int argc, char **argv)
{
    unsigned num_tests {1000};
    size_t vec1_size {10000000};
    size_t vec2_size {10000000};

    auto vec1 = generate_random_ints(vec1_size);
    auto vec2 = generate_random_ints(vec1_size);

    auto f_std_insert = [&vec1, &vec2] () {
        std_insert(vec1, vec2);
    };
    auto f_push_back_id = [&vec1, &vec2] () {
        push_back_concat(vec1, vec2, [] (int i) { return i; });
    };
    auto f_transform_id = [&vec1, &vec2] () {
        transform_concat(vec1, vec2, [] (int i) { return i; });
    };

    auto std_insert_time   = benchmark<std::chrono::milliseconds>(f_std_insert, num_tests).count();
    auto push_back_id_time = benchmark<std::chrono::milliseconds>(f_push_back_id, num_tests).count();
    auto transform_id_time = benchmark<std::chrono::milliseconds>(f_transform_id, num_tests).count();

    std::cout << "std_insert: " << std_insert_time << "ms" << std::endl;
    std::cout << "push_back_id: " << push_back_id_time << "ms" << std::endl;
    std::cout << "transform_id: " << transform_id_time << "ms" << std::endl;

    return 0;
}

Compiled with:

g++ vector_insert_demo.cpp -std=c++11 -O3 -o vector_insert_demo

Output:

std_insert: 44ms
push_back_id: 61ms
transform_id: 61ms

The compiler will have inlined the lambda, so that cost can be safely be discounted. Unless anyone else has a viable explanation for these results (or is willing to check the assembly), I think it's reasonable to conclude there is a noticeable cost of the multiple capacity checks.

Answer 1

UPDATE: The performance difference is due to the reserve() calls, which, in libstdc++ at least, make the capacity be exactly what you request instead of using the exponential growth factor.

---

I did some timing tests, with interesting results. Using std::vector::insert along with boost::transform_iterator was the fastest way I found by a large margin:

Version 1:

void
  appendTransformed1(
    std::vector<int> &vec1,
    const std::vector<float> &vec2
  )
{
  auto v2begin = boost::make_transform_iterator(vec2.begin(),f);
  auto v2end   = boost::make_transform_iterator(vec2.end(),f);
  vec1.insert(vec1.end(),v2begin,v2end);
}

Version 2:

void
  appendTransformed2(
    std::vector<int> &vec1,
    const std::vector<float> &vec2
  )
{
  vec1.reserve(vec1.size()+vec2.size());
  for (auto x : vec2) {
    vec1.push_back(f(x));
  }
}

Version 3:

void
  appendTransformed3(
    std::vector<int> &vec1,
    const std::vector<float> &vec2
  )
{
  vec1.reserve(vec1.size()+vec2.size());
  std::transform(vec2.begin(),vec2.end(),std::inserter(vec1,vec1.end()),f);
}

Timing:

    Version 1: 0.59s
    Version 2: 8.22s
    Version 3: 8.42s

main.cpp:

#include <algorithm>
#include <cassert>
#include <chrono>
#include <iterator>
#include <iostream>
#include <random>
#include <vector>
#include "appendtransformed.hpp"

using std::cerr;

template <typename Engine>
static std::vector<int> randomInts(Engine &engine,size_t n)
{
  auto distribution = std::uniform_int_distribution<int>(0,999);
  auto generator = [&]{return distribution(engine);};
  auto vec = std::vector<int>();
  std::generate_n(std::inserter(vec,vec.end()),n,generator);
  return vec;
}

template <typename Engine>
static std::vector<float> randomFloats(Engine &engine,size_t n)
{
  auto distribution = std::uniform_real_distribution<float>(0,1000);
  auto generator = [&]{return distribution(engine);};
  auto vec = std::vector<float>();
  std::generate_n(std::inserter(vec,vec.end()),n,generator);
  return vec;
}

static auto
  appendTransformedFunction(int version) ->
    void(*)(std::vector<int>&,const std::vector<float> &)
{
  switch (version) {
    case 1: return appendTransformed1;
    case 2: return appendTransformed2;
    case 3: return appendTransformed3;
    default:
      cerr << "Unknown version: " << version << "\n";
      exit(EXIT_FAILURE);
  }

  return 0;
}

int main(int argc,char **argv)
{
  if (argc!=2) {
    cerr << "Usage: appendtest (1|2|3)\n";
    exit(EXIT_FAILURE);
  }
  auto version = atoi(argv[1]);
  auto engine = std::default_random_engine();
  auto vec1_size = 1000000u;
  auto vec2_size = 1000000u;
  auto count = 100;
  auto vec1 = randomInts(engine,vec1_size);
  auto vec2 = randomFloats(engine,vec2_size);
  namespace chrono = std::chrono;
  using chrono::system_clock;
  auto appendTransformed = appendTransformedFunction(version);
  auto start_time = system_clock::now();
  for (auto i=0; i!=count; ++i) {
    appendTransformed(vec1,vec2);
  }
  auto end_time = system_clock::now();
  assert(vec1.size() == vec1_size+count*vec2_size);
  auto sum = std::accumulate(vec1.begin(),vec1.end(),0u);
  auto elapsed_seconds = chrono::duration<float>(end_time-start_time).count();

  cerr << "Using version " << version << ":\n";
  cerr << "  sum=" << sum << "\n";
  cerr << "  elapsed: " << elapsed_seconds << "s\n";
}

Compiler: g++ 4.9.1

Options: -std=c++11 -O2