The Parallel Hashmap

Overview

This repository aims to provide an set of excellent hash map implementations, with the following characteristics:

header only: nothing to build, just copy the parallel_hashmap directory to your project and you are good to go.
compiler with C++11 support required, C++14 and C++17 APIs are provided
Very efficient, significantly faster than your compiler's unordered map/set or Boost's, or than sparsepp
Memory friendly: low memory usage, although a little higher than sparsepp
supports heterogeneous lookup
easy to forward declare: just include phmap_fwd_decl.h in your header files to forward declare Parallel Hashmap containers. This header is only 111 lines including comments.
Tested on Windows (vs2015 & vs2017), linux (g++ 5, 6, 7, 8, clang++ 3.9, 4.0, 5.0) and MacOS (g++ and clang++) - click on travis and appveyor icons above for detailed test status.

Fast and memory friendly

Click here For a full writeup explaining the design and benefits of the Parallel Hashmap.

The hashmaps provided here are built upon those open sourced by Google in the Abseil library. They use closed hashing, where values are stored directly into a memory array, avoiding memory indirections. By using parallel SSE2 instructions, these hashmaps are able to look up items by checking 16 slots in parallel, allowing the implementation to remain fast even when the table is filled up to 87.5% capacity.

IMPORTANT: This repository borrows code from the abseil-cpp repository, with modifications, and may behave differently from the original. This repository is an independent work, with no guarantees implied or provided by the authors. Please visit abseil-cpp for the official Abseil libraries.

Installation

Copy the parallel_hashmap directory to your project. Update your include path. That's all.

A cmake configuration files (CMakeLists.txt) is provided for building the tests and examples. Command for building and running the tests is: mkdir build && cd build && cmake -DPHMAP_BUILD_TESTS=ON .. && cmake --build . && make test

Example

#include <iostream>
#include <string>
#include <parallel_hashmap/phmap.h>

using phmap::flat_hash_map;
 
int main()
{
    // Create an unordered_map of three strings (that map to strings)
    flat_hash_map<std::string, std::string> email = 
    {
        { "tom",  "tom@gmail.com"},
        { "jeff", "jk@gmail.com"},
        { "jim",  "jimg@microsoft.com"}
    };
 
    // Iterate and print keys and values 
    for (const auto& n : email) 
        std::cout << n.first << "'s email is: " << n.second << "\n";
 
    // Add a new entry
    email["bill"] = "bg@whatever.com";
 
    // and print it
    std::cout << "bill's email is: " << email["bill"] << "\n";
 
    return 0;
}

Various hash maps and their pros and cons

The header parallel_hashmap/phmap.h provides the implementation for the following eight hash tables:

phmap::flat_hash_set
phmap::flat_hash_map
phmap::node_hash_set
phmap::node_hash_map
phmap::parallel_flat_hash_set
phmap::parallel_flat_hash_map
phmap::parallel_node_hash_set
phmap::parallel_node_hash_map

The full types with template parameters can be found in the parallel_hashmap/phmap_fwd_decl.h header, which is useful for forward declaring the Parallel Hashmaps when necessaary.

Key points:

The flat hash maps may move the keys and values in memory. So if you keep a pointer to something inside a flat hash map, this pointer may become invalid when the map is mutated. The node hash maps don't, and should be used instead if this is a problem.
The flat hash maps will use less memory, and usually be faster than the node hash maps, so use them if you can. A possible exception is when the values inserted in the hash map are large (say more than 100 bytes [needs testing]).
The parallel hash maps are preferred when you have a few hash maps that will store a very large number of values. The non-parallel hash maps are preferred if you have a large number of hash maps, each storing a relatively small number of values.
The benefits of the parallel hash maps are:
a. reduced peak memory usage (when resizing), and
b. multithreading support (and inherent internal parallelism)

Memory usage

type	memory usage
flat tables
node tables	!node_mem_usage](https://github.com/greg7mdp/parallel-hashmap/blob/master/html/img/node_mem_usage.gif?raw=true)

The load factor varies between 0.4375 (just after the resize) and 0.875 (just before the resize).

In addition, when the table resizes, the peak memory usage is an additional 50% of the new size for the non-parallel hashmaps, and an additional 3% of the new size for the parallel hashmaps (single threaded usage).

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