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Merge #16986: doc: Doxygen-friendly CuckooCache comments
7aad3b68e7e1680870ca70d945eee88f790d6454 doc: Doxygen-friendly CuckooCache comments (Jon Layton) Pull request description: Similar theme to #16947. - `invalid`, `contains` now appear in Doxygen docs - `setup` refers to correct argument name `b` - Argument references in `code blocks ` - Lists markdown conformant, uniform line endings Tested with `make docs` ACKs for top commit: laanwj: ACK 7aad3b68e7e1680870ca70d945eee88f790d6454 practicalswift: ACK 7aad3b68e7e1680870ca70d945eee88f790d6454 Tree-SHA512: 70b38c10e534bad9c6ffcd88cc7a4797644afba5956d47a6c7cc655fcd5857a91f315d6da60e28ce9678d420ed4a51e22267eb8b89e26002b99cad63373dd349
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@ -14,42 +14,40 @@
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#include <vector>
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/** namespace CuckooCache provides high performance cache primitives
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/** High-performance cache primitives.
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*
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* Summary:
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*
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* 1) bit_packed_atomic_flags is bit-packed atomic flags for garbage collection
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* 1. @ref bit_packed_atomic_flags is bit-packed atomic flags for garbage collection
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*
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* 2) cache is a cache which is performant in memory usage and lookup speed. It
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* is lockfree for erase operations. Elements are lazily erased on the next
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* insert.
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* 2. @ref cache is a cache which is performant in memory usage and lookup speed. It
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* is lockfree for erase operations. Elements are lazily erased on the next insert.
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*/
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namespace CuckooCache
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{
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/** bit_packed_atomic_flags implements a container for garbage collection flags
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/** @ref bit_packed_atomic_flags implements a container for garbage collection flags
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* that is only thread unsafe on calls to setup. This class bit-packs collection
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* flags for memory efficiency.
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*
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* All operations are std::memory_order_relaxed so external mechanisms must
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* All operations are `std::memory_order_relaxed` so external mechanisms must
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* ensure that writes and reads are properly synchronized.
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*
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* On setup(n), all bits up to n are marked as collected.
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* On setup(n), all bits up to `n` are marked as collected.
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*
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* Under the hood, because it is an 8-bit type, it makes sense to use a multiple
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* of 8 for setup, but it will be safe if that is not the case as well.
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*
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*/
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class bit_packed_atomic_flags
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{
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std::unique_ptr<std::atomic<uint8_t>[]> mem;
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public:
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/** No default constructor as there must be some size */
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/** No default constructor, as there must be some size. */
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bit_packed_atomic_flags() = delete;
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/**
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* bit_packed_atomic_flags constructor creates memory to sufficiently
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* keep track of garbage collection information for size entries.
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* keep track of garbage collection information for `size` entries.
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*
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* @param size the number of elements to allocate space for
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*
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@ -68,7 +66,7 @@ public:
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};
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/** setup marks all entries and ensures that bit_packed_atomic_flags can store
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* at least size entries
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* at least `b` entries.
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*
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* @param b the number of elements to allocate space for
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* @post bit_set, bit_unset, and bit_is_set function properly forall x. x <
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@ -84,19 +82,18 @@ public:
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/** bit_set sets an entry as discardable.
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*
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* @param s the index of the entry to bit_set.
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* @param s the index of the entry to bit_set
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* @post immediately subsequent call (assuming proper external memory
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* ordering) to bit_is_set(s) == true.
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*
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*/
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inline void bit_set(uint32_t s)
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{
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mem[s >> 3].fetch_or(1 << (s & 7), std::memory_order_relaxed);
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}
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/** bit_unset marks an entry as something that should not be overwritten
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/** bit_unset marks an entry as something that should not be overwritten.
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*
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* @param s the index of the entry to bit_unset.
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* @param s the index of the entry to bit_unset
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* @post immediately subsequent call (assuming proper external memory
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* ordering) to bit_is_set(s) == false.
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*/
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@ -105,10 +102,10 @@ public:
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mem[s >> 3].fetch_and(~(1 << (s & 7)), std::memory_order_relaxed);
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}
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/** bit_is_set queries the table for discardability at s
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/** bit_is_set queries the table for discardability at `s`.
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*
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* @param s the index of the entry to read.
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* @returns if the bit at index s was set.
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* @param s the index of the entry to read
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* @returns true if the bit at index `s` was set, false otherwise
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* */
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inline bool bit_is_set(uint32_t s) const
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{
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@ -116,15 +113,15 @@ public:
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}
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};
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/** cache implements a cache with properties similar to a cuckoo-set
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/** @ref cache implements a cache with properties similar to a cuckoo-set.
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*
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* The cache is able to hold up to (~(uint32_t)0) - 1 elements.
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* The cache is able to hold up to `(~(uint32_t)0) - 1` elements.
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*
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* Read Operations:
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* - contains(*, false)
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* - contains() for `erase=false`
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*
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* Read+Erase Operations:
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* - contains(*, true)
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* - contains() for `erase=true`
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*
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* Erase Operations:
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* - allow_erase()
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@ -141,10 +138,10 @@ public:
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*
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* User Must Guarantee:
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*
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* 1) Write Requires synchronized access (e.g., a lock)
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* 2) Read Requires no concurrent Write, synchronized with the last insert.
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* 3) Erase requires no concurrent Write, synchronized with last insert.
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* 4) An Erase caller must release all memory before allowing a new Writer.
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* 1. Write requires synchronized access (e.g. a lock)
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* 2. Read requires no concurrent Write, synchronized with last insert.
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* 3. Erase requires no concurrent Write, synchronized with last insert.
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* 4. An Erase caller must release all memory before allowing a new Writer.
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*
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*
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* Note on function names:
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@ -177,7 +174,7 @@ private:
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mutable std::vector<bool> epoch_flags;
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/** epoch_heuristic_counter is used to determine when an epoch might be aged
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* & an expensive scan should be done. epoch_heuristic_counter is
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* & an expensive scan should be done. epoch_heuristic_counter is
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* decremented on insert and reset to the new number of inserts which would
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* cause the epoch to reach epoch_size when it reaches zero.
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*/
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@ -194,24 +191,25 @@ private:
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uint32_t epoch_size;
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/** depth_limit determines how many elements insert should try to replace.
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* Should be set to log2(n)*/
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* Should be set to log2(n).
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*/
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uint8_t depth_limit;
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/** hash_function is a const instance of the hash function. It cannot be
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* static or initialized at call time as it may have internal state (such as
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* a nonce).
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* */
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*/
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const Hash hash_function;
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/** compute_hashes is convenience for not having to write out this
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* expression everywhere we use the hash values of an Element.
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*
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* We need to map the 32-bit input hash onto a hash bucket in a range [0, size) in a
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* manner which preserves as much of the hash's uniformity as possible. Ideally
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* manner which preserves as much of the hash's uniformity as possible. Ideally
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* this would be done by bitmasking but the size is usually not a power of two.
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*
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* The naive approach would be to use a mod -- which isn't perfectly uniform but so
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* long as the hash is much larger than size it is not that bad. Unfortunately,
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* long as the hash is much larger than size it is not that bad. Unfortunately,
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* mod/division is fairly slow on ordinary microprocessors (e.g. 90-ish cycles on
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* haswell, ARM doesn't even have an instruction for it.); when the divisor is a
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* constant the compiler will do clever tricks to turn it into a multiply+add+shift,
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@ -223,10 +221,10 @@ private:
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* somewhat complicated and the result is still slower than other options:
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*
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* Instead we treat the 32-bit random number as a Q32 fixed-point number in the range
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* [0,1) and simply multiply it by the size. Then we just shift the result down by
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* 32-bits to get our bucket number. The result has non-uniformity the same as a
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* [0, 1) and simply multiply it by the size. Then we just shift the result down by
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* 32-bits to get our bucket number. The result has non-uniformity the same as a
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* mod, but it is much faster to compute. More about this technique can be found at
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* http://lemire.me/blog/2016/06/27/a-fast-alternative-to-the-modulo-reduction/
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* http://lemire.me/blog/2016/06/27/a-fast-alternative-to-the-modulo-reduction/ .
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*
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* The resulting non-uniformity is also more equally distributed which would be
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* advantageous for something like linear probing, though it shouldn't matter
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* 32*32->64 multiply, which means the operation is reasonably fast even on a
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* typical 32-bit processor.
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*
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* @param e the element whose hashes will be returned
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* @returns std::array<uint32_t, 8> of deterministic hashes derived from e
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* @param e The element whose hashes will be returned
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* @returns Deterministic hashes derived from `e` uniformly mapped onto the range [0, size)
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*/
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inline std::array<uint32_t, 8> compute_hashes(const Element& e) const
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{
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@ -252,14 +250,14 @@ private:
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(uint32_t)(((uint64_t)hash_function.template operator()<7>(e) * (uint64_t)size) >> 32)}};
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}
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/* end
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* @returns a constexpr index that can never be inserted to */
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/** invalid returns a special index that can never be inserted to
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* @returns the special constexpr index that can never be inserted to */
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constexpr uint32_t invalid() const
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{
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return ~(uint32_t)0;
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}
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/** allow_erase marks the element at index n as discardable. Threadsafe
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/** allow_erase marks the element at index `n` as discardable. Threadsafe
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* without any concurrent insert.
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* @param n the index to allow erasure of
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*/
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collection_flags.bit_set(n);
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}
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/** please_keep marks the element at index n as an entry that should be kept.
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/** please_keep marks the element at index `n` as an entry that should be kept.
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* Threadsafe without any concurrent insert.
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* @param n the index to prioritize keeping
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*/
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*
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* @param new_size the desired number of elements to store
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* @returns the maximum number of elements storable
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**/
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*/
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uint32_t setup(uint32_t new_size)
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{
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// depth_limit must be at least one otherwise errors can occur.
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* negligible compared to the size of the elements.
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*
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* @param bytes the approximate number of bytes to use for this data
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* structure.
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* structure
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* @returns the maximum number of elements storable (see setup()
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* documentation for more detail)
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*/
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* It drops the last tried element if it runs out of depth before
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* encountering an open slot.
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*
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* Thus
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* Thus:
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*
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* ```
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* insert(x);
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* return contains(x, false);
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* ```
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*
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* is not guaranteed to return true.
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*
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* @post one of the following: All previously inserted elements and e are
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* now in the table, one previously inserted element is evicted from the
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* table, the entry attempted to be inserted is evicted.
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*
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*/
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inline void insert(Element e)
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{
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/** Swap with the element at the location that was
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* not the last one looked at. Example:
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*
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* 1) On first iteration, last_loc == invalid(), find returns last, so
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* 1. On first iteration, last_loc == invalid(), find returns last, so
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* last_loc defaults to locs[0].
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* 2) On further iterations, where last_loc == locs[k], last_loc will
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* 2. On further iterations, where last_loc == locs[k], last_loc will
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* go to locs[k+1 % 8], i.e., next of the 8 indices wrapping around
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* to 0 if needed.
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*
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}
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}
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/* contains iterates through the hash locations for a given element
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/** contains iterates through the hash locations for a given element
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* and checks to see if it is present.
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*
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* contains does not check garbage collected state (in other words,
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* garbage is only collected when the space is needed), so:
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*
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* ```
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* insert(x);
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* if (contains(x, true))
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* return contains(x, false);
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* else
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* return true;
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* ```
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*
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* executed on a single thread will always return true!
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*
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* contains returns a bool set true if the element was found.
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*
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* @param e the element to check
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* @param erase
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* @param erase whether to attempt setting the garbage collect flag
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*
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* @post if erase is true and the element is found, then the garbage collect
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* flag is set
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@ -10,11 +10,11 @@
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/** Test Suite for CuckooCache
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*
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* 1) All tests should have a deterministic result (using insecure rand
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* 1. All tests should have a deterministic result (using insecure rand
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* with deterministic seeds)
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* 2) Some test methods are templated to allow for easier testing
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* 2. Some test methods are templated to allow for easier testing
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* against new versions / comparing
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* 3) Results should be treated as a regression test, i.e., did the behavior
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* 3. Results should be treated as a regression test, i.e., did the behavior
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* change significantly from what was expected. This can be OK, depending on
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* the nature of the change, but requires updating the tests to reflect the new
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* expected behavior. For example improving the hit rate may cause some tests
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@ -99,9 +99,9 @@ static double test_cache(size_t megabytes, double load)
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*
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* Examples:
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*
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* 1) at load 0.5, we expect a perfect hit rate, so we multiply by
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* 1. at load 0.5, we expect a perfect hit rate, so we multiply by
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* 1.0
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* 2) at load 2.0, we expect to see half the entries, so a perfect hit rate
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* 2. at load 2.0, we expect to see half the entries, so a perfect hit rate
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* would be 0.5. Therefore, if we see a hit rate of 0.4, 0.4*2.0 = 0.8 is the
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* normalized hit rate.
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*
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