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/// | ||
/// @file PrimeGenerator.hpp | ||
/// Generates the primes inside [start, stop] and stores them | ||
/// in a vector. After the primes have been stored in the | ||
/// vector primesieve::iterator iterates over the vector and | ||
/// returns the primes. When there are no more primes left in | ||
/// the vector PrimeGenerator generates new primes. | ||
/// @file PrimeGenerator.hpp | ||
/// @brief Generates the primes inside [start, stop] and stores them | ||
/// in a vector. After the primes have been stored in the | ||
/// vector primesieve::iterator iterates over the vector and | ||
/// returns the primes. When there are no more primes left in | ||
/// the vector PrimeGenerator generates new primes. | ||
/// | ||
/// Copyright (C) 2023 Kim Walisch, <[email protected]> | ||
/// | ||
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/// | ||
/// @file intrinsics.hpp | ||
/// @brief Wrappers for compiler intrinsics. | ||
/// @file intrinsics.hpp | ||
/// @brief Wrappers for compiler intrinsics. | ||
/// | ||
/// Copyright (C) 2022 Kim Walisch, <[email protected]> | ||
/// | ||
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/// | ||
/// @file MemoryPool.cpp | ||
/// EratMedium and EratBig may use millions of buckets for | ||
/// storing the sieving primes that are required to cross off | ||
/// multiples. As many memory allocations/deallocations are | ||
/// bad for performance the MemoryPool initially allocates a | ||
/// large number of buckets (using a single memory allocation) | ||
/// and puts the buckets into its stock. The MemoryPool can | ||
/// then serve buckets to EratMedium and EratBig without | ||
/// doing any memory allocation as long as the MemoryPool's | ||
/// stock is not empty. | ||
/// @file MemoryPool.cpp | ||
/// @brief EratMedium and EratBig may use millions of buckets for | ||
/// storing the sieving primes that are required to cross off | ||
/// multiples. As many memory allocations/deallocations are | ||
/// bad for performance the MemoryPool initially allocates a | ||
/// large number of buckets (using a single memory allocation) | ||
/// and puts the buckets into its stock. The MemoryPool can | ||
/// then serve buckets to EratMedium and EratBig without | ||
/// doing any memory allocation as long as the MemoryPool's | ||
/// stock is not empty. | ||
/// | ||
/// Copyright (C) 2023 Kim Walisch, <[email protected]> | ||
/// | ||
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/// | ||
/// @file RiemannR.cpp | ||
/// This file contains an implementation of the Riemann R | ||
/// function which is a very accurate approximation of PrimePi(x). | ||
/// The accuracy of this implementation depends on width of the | ||
/// long double type. If the long double type has 80 bits (e.g. | ||
/// Linux) then RiemannR(x) is accurate up to 10^19, if the long | ||
/// double type has 64 bits (e.g. MSVC & macOS) then RiemannR(x) | ||
/// is accurate up to 10^15. This implementation could be made | ||
/// more accurate using the non standard __float128 type, but for | ||
/// primesieve's purpose speed is more important than accuracy. | ||
/// @file RiemannR.cpp | ||
/// @brief This file contains an implementation of the Riemann R | ||
/// function which is a very accurate approximation of PrimePi(x). | ||
/// The accuracy of this implementation depends on width of the | ||
/// long double type. If the long double type has 80 bits (e.g. | ||
/// Linux) then RiemannR(x) is accurate up to 10^19, if the long | ||
/// double type has 64 bits (e.g. MSVC & macOS) then RiemannR(x) | ||
/// is accurate up to 10^15. This implementation could be made | ||
/// more accurate using the non standard __float128 type, but for | ||
/// primesieve's purpose speed is more important than accuracy. | ||
/// | ||
/// More details about this Riemann R function implementation: | ||
/// https://github.com/kimwalisch/primesieve/pull/144 | ||
/// More details about this Riemann R function implementation: | ||
/// https://github.com/kimwalisch/primesieve/pull/144 | ||
/// | ||
/// Copyright (C) 2024 Kim Walisch, <[email protected]> | ||
/// Copyright (C) 2024 @nipzu, https://github.com/nipzu | ||
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