===================================================================
@@ -3741,6 +3741,37 @@
#ifdef _GLIBCXX_USE_C99_STDINT_TR1
/**
+ * @brief Generate two uniformly distributed integers using a
+ * single distribution invocation.
+ * @param __b0 The upper bound for the first integer.
+ * @param __b1 The upper bound for the second integer.
+ * @param __g A UniformRandomBitGenerator.
+ * @return A pair (i, j) with i and j uniformly distributed
+ * over [0, __b0) and [0, __b1), respectively.
+ *
+ * Requires: __b0 * __b1 <= __g.max() - __g.min().
+ *
+ * Using uniform_int_distribution with a range that is very
+ * small relative to the range of the generator ends up wasting
+ * potentially expensively generated randomness, since
+ * uniform_int_distribution does not store leftover randomness
+ * between invocations.
+ *
+ * If we know we want two integers in ranges that are sufficiently
+ * small, we can compose the ranges, use a single distribution
+ * invocation, and significantly reduce the waste.
+ */
+ template<typename _IntType,
+ typename _UniformRandomBitGenerator>
+ std::pair<_IntType, _IntType>
+ __gen_two_uniform_ints(_IntType __b0, _IntType __b1,
+ _UniformRandomBitGenerator&& __g)
+ {
+ _IntType __x = uniform_int_distribution<_IntType>{0, (__b0 * __b1) - 1}(__g);
+ return std::make_pair(__x / __b1, __x % __b1);
+ }
+
+ /**
* @brief Shuffle the elements of a sequence using a uniform random
* number generator.
* @ingroup mutating_algorithms
@@ -3801,13 +3832,12 @@
while (__i != __last)
{
const __uc_type __swap_range = __uc_type(__i - __first) + 1;
- const __uc_type __comp_range = __swap_range * (__swap_range + 1);
- std::uniform_int_distribution<__uc_type> __d{0, __comp_range - 1};
- const __uc_type __pospos = __d(__g);
+ const std::pair<__uc_type, __uc_type> __pospos =
+ __gen_two_uniform_ints(__swap_range, __swap_range + 1);
- std::iter_swap(__i++, __first + (__pospos % __swap_range));
- std::iter_swap(__i++, __first + (__pospos / __swap_range));
+ std::iter_swap(__i++, __first + __pospos.first);
+ std::iter_swap(__i++, __first + __pospos.second);
}
return;
@@ -5695,9 +5725,51 @@
{
using __distrib_type = uniform_int_distribution<_Size>;
using __param_type = typename __distrib_type::param_type;
+ using _USize = typename std::make_unsigned<_Size>::type;
+ using _Gen = typename std::remove_reference<_UniformRandomBitGenerator>::type;
+ using __uc_type = typename std::common_type<typename _Gen::result_type, _USize>::type;
+
__distrib_type __d{};
_Size __unsampled_sz = std::distance(__first, __last);
- for (__n = std::min(__n, __unsampled_sz); __n != 0; ++__first)
+ __n = std::min(__n, __unsampled_sz);
+
+ // If possible, we use __gen_two_uniform_ints to efficiently produce
+ // two random numbers using a single distribution invocation:
+
+ const __uc_type __urngrange = __g.max() - __g.min();
+ if (__urngrange / __uc_type(__unsampled_sz) >= __uc_type(__unsampled_sz))
+ // I.e. (__urngrange >= __unsampled_sz * __unsampled_sz) but without wrap issues.
+ {
+ while (__n != 0 && __unsampled_sz >= 2)
+ {
+ const std::pair<_Size, _Size> p =
+ __gen_two_uniform_ints(__unsampled_sz, __unsampled_sz - 1, __g);
+
+ --__unsampled_sz;
+ if (p.first < __n)
+ {
+ *__out++ = *__first;
+ --__n;
+ }
+
+ ++__first;
+
+ if (__n == 0) break;
+
+ --__unsampled_sz;
+ if (p.second < __n)
+ {
+ *__out++ = *__first;
+ --__n;
+ }
+
+ ++__first;
+ }
+ }
+
+ // The loop above is otherwise equivalent to this one-at-a-time version:
+
+ for (; __n != 0; ++__first)
if (__d(__g, __param_type{0, --__unsampled_sz}) < __n)
{
*__out++ = *__first;
This is the same optimization as was recently applied to std::shuffle. It reduces the runtime of the following program by 20% on my machine: int main() { std::vector<uint64_t> a(10000), b(1000); std::mt19937 gen; uint64_t c = 0; for (int i = 0; i != 1000; ++i) { std::sample(a.begin(), a.end(), b.begin(), b.size(), gen); c += uint64_t(b[32]); } std::cout << c; }