• About growt (GrowTable) is a header only library implementing a concurrent growing hash table. There are different variants depending on the use case. See explanation below.

Using the supplied cmake build system you can compile an example file (~example/example.cpp~) and a multitude of different tests/benchmarks. These were also used to generate the plots displayed in our scientific publications => [[https://dl.acm.org/doi/10.1145/3309206][TOPC journal]] [[https://arxiv.org/abs/1601.04017][technical report (outdated)]].

** New Version (Changes)

• complex data-types (arbitrary keys and values)

  • these tables work by allocating elements on the heap, but minimizing key-comparisons
  • new emplace/move functionality

• new table dispatcher simplifies choosing the correct table

• Hash table interface To remain fast while storing some per thread data, we use handles. These help us to remain fast when accessing the hash table. Even though this is a change from the traditional interface, we try to remain as close to ~std::unordered_map~ as possible (within the handle). To achieve this we implemented an iterator based interface. Our iterators remain secure even when the table is migrated in the background. This is necessary because in concurrent scenarios one can never be certain, when a table migration occurs.

All our hash tables support the following functionality:

called on global object

• ~HashTable(size_t n)~ - constructor allocating a table large enough to hold ~n~ elements.
• ~HashTable::Handle getHandle()~ - create handles that can be used to access the table (alternatively ~HashTable::Handle(HashTable global_table_obj)~)

called through handles within the handles, we try to keep the interface as close to ~std::unordered_map~ as possible:

• ~std::pair(iterator, bool) insert(uint64_t k, uint64_t d)~ - if no element with key ~k~ was present, ~insert~ stores the data element ~d~ at key ~k~ and returns an iterator (and true). Otherwise it returns an iterator to the already present element and false.
• ~std::pair(iterator, bool) update(uint64_t k, UpdateFunction f, ...)~ - looks for an element with key ~k~. If present, the stored data is changed atomically using the supplied update function (interface/instruction below). The returned iterator points to the found element (or ~end()~), and the bool shows if an element was found.
• ~std::pair(iterator, bool) insertOrUpdate(uint64_t k, uint64_t d, UpdateFunction f)~ - combines the two functions above. If no data was present with key ~k~, then ~d~ is inserted, otherwise the stored data is updated. The bool can be used to find out which happened (true -> insertion, false -> update).
• ~size_t erase(uint64_t k)~ - removes the stored key value pair, returns the number of erased entries (at most one). The memory is reclaimed once the table is migrated.
• ~iterator find(uint64_t k)~ - finds the data element stored at key ~k~ and returns an iterator (~end()~ if unfound).
• ~const_iterator find(uint64_t k) const~ - same as find

Using handles is not necessary for our non-growing tables.

called through iterators

• dereferencing/reading the values will return the values that were in the table when the iterator was created.
• ~refresh~ will update the iterator to the current state of the table. This might make the iterator ~end()~ (cannot be dereferenced) if the element was erased in the meantime.

called through a reference

• reading the values will return the values that were in the table when the iterator was created (that was dereferenced to become this reference).

** About UpdateFunctions Our update interface uses a user implemented update function. Any object given as an update function should have an ~operator()(uint64_t& cur_data, uint64_t key, uint64_t new_data)~ returning a ~uint64_t~. This function will usually be called by our update operations to change a stored elements data field. This operations does not have to be atomic: it is made atomic using CAS operations. When an atomic version of the method exists, then it can be implemented as an additional function called ~.atomic(uint64_t& cur_data, uint64_t key, uint64_t new_data)~, which is only called iff atomics can be called safely (mostly in synchronized growing variants ~usGrow~ and ~psGrow~). Presensce of the atomic variant is detected automatically. An example for a fully implemented ~UpdateFunction~ might look like this:

+BEGIN_SRC cpp

struct Increment { uint64_t operator()(uint64_t& lhs, const uint64_t, const uint64_t rhs) const { return lhs += rhs; }

// an atomic implementation can improve the performance of updates in .sGrow
// this will be detected automatically
uint64_t atomic    (uint64_t& lhs, const uint64_t, const uint64_t rhs) const
{
    __sync_fetch_and_add(&lhs, rhs);
}

};

+END_SRC

• Content This package contains many different concurrent hash table variants that can be accessed through a dispatcher that automates choosing the correct implementation according to a set of parameters.

All our data structures and connected classes are declared in the ~growt~ namespace.

** Non-growing hash tables

• ~folklore~ is a simple linear probing hash table using atomic operations, to change cell contents.

** Growing hash tables Our growing variants use the above non-growing tables. They grow by migrating the entire hash table once it gets too full for the current size. Migration is done in the background without the user knowing about it. During the migration hash table accesses may be delayed until the table is migrated (usually the waiting thread will help with the migration).

Threads can only access our growing hash tables by creating a thread specific handle. These handles cannot be shared between threads.

• ~uaGrow~ is a growing table, where threads that access the table are responsible for eventual migrations. These will be performed automatically and asynchronously. Migrated cells are marked to ensure atomicity (this reduces the available key space by one bit. Keys >=2^63 cannot be inserted).
• ~usGrow~ similar to ~uaGrow~ but growing steps are somewhat synchronized (ensures automatically that no updates run during growing phases) eliminating the need for marking.
• ~paGrow~ where growing is done by a dedicated pool of growing threads. Similar to ~uaGrow~ marking is used to ensure atomicity of the hash table migration.
• ~psGrow~ combining the thread pool of ~paGrow~ with the synchronized growing approach of ~usGrow~.

** Our tests and Benchmarks All generated tests (~make~ recipes) have the same name structure.

~__~ => e.g. ~ins_full_uaGrow~

*** test_abbrv

• ~ins~ - insertion and find test (seperate)
• ~mix~ - mixed inserts and finds
• ~agg~ - aggregation using insertOrUpdate on a skewed key sequence
• ~con~ - updates and finds on a skewed key sequence
• ~del~ - alternating inserts and deletions (approx. constant table size)

*** full list of hash tables Some of the following tables have to be activated through cmake options.

• ~sequential~ - our sequential table (use only one thread!)
• ~folklore~ - our non growing tables
• ~uaGrow, usGrow, paGrow, psGrow~ - our main growing tables with different growing strategies
• ~junction_linear, junction_grampa, junction_leap, folly, cuckoo, tbb_hm, tbb_um~ - third party tables

Note: in the paper we have some additional third party hash tables. These depend on some additional wrappers and are not reproduced here. Wrappers for their libraries can be found in a branch called legacy_wrappers.

• Usage in your own projects

** Including our project To make it easy, you can include the header ~data-structures/table_config.hpp~, which includes all necessary files and offers an interface to choose the right hash table for your workload. Additional hash table modifications can be found in ~data-structures/hash_table_mods.hpp~

+BEGIN_SRC cpp

include "data-structures/table_config.hpp"

using table_type = typename growt::table_config<key_type, mapped_type, hash_function, allocator_type, // any number of hash mods hmod::growable, hmod::deletions>::table_type;

+END_SRC

** About our utility functions The utility functions are now placed in their own submodule [[https://github.com/TooBiased/utils_tm][github repository]]

• Our Usage of Third Party Code This package can be used all on its own (see example.cpp and ...test.cpp). However third party codes are used for additional functionality/tests. Most of the third party libraries are either searched on your machine (TBB, pthread), or they are placed in submodules (downloaded through git).

** We use the following libraries: *** for utility:

• TBB - to implement a fixed memory pool allocator
• xxHash - usable hash function

*** as third party hash tables (for benchmarks):

• TBB - ~tbb::concurrent_hash_map and tbb::concurrent_unordered_map~

• LibCuckoo - ~cuckoohash_map~

• Junction - ~junction::ConcurrentMap_Linear ..._Grampa ..._Leapfrog~

• Folly - ~folly::AtomicHashMap~

• Build Notes Tested using current versions of g++.

** Easy build without third party code

+BEGIN_SRC bash

git clone https://github.com/TooBiased/growt.git cd growt mkdir build cd build cmake .. make

+END_SRC

** Building with third party libraries Third party libraries are either installed using your package manager or they are downloaded into the ~misc/submodules~ folder.

+BEGIN_SRC bash

git clone https://github.com/TooBiased/growt.git cd growt git submodule init mkdir build cd build cmake -D GROWT_BUILD_ALL_THIRD_PARTIES=ON .. make

+END_SRC

note that folly needs quite a lot of extern libraries (zstd, glog, ...) those have to be installed, to compile any test using folly (checkout their github [[https://github.com/facebook/folly]]).