Performance Overview

Performance Comparison with Mainstream Implementations (lower is better)

Test Environment: AMD 5800H / Windows 10 / GCC 11.3

Operation	emhash7	emhash8	emhash6	emhash5	phmap	absl	std::unordered_map
Insert (10M)	66.0	95.6	63.6	64.7	59.5	53.2	295
Find Hit	16.9	18.3	15.1	16.6	36.4	22.6	33.0
Find Miss	18.1	18.1	17.0	18.8	19.7	11.7	52.0
Erase	20.4	46.3	24.0	22.3	56.2	41.5	293
Iterate	0.74	0.00	0.75	4.75	3.46	3.49	62.6

Note: emhash8's iteration time of 0.00 ms indicates extremely fast iteration due to its contiguous memory layout (actual time is < 0.005 ms).

Excellent Performance at High Load Factors

Even at an extreme load factor of 0.999, emhash maintains outstanding performance:

emhash7::HashMap<int64_t, int> myhash(1 << 20, 0.999f);
// Insert 1M+ elements without rehash
// Stable insert/erase performance without degradation

How it works

// Compile: g++ -O3 -march=native -I../include -I../thirdparty -std=c++17 -DEMH_HIGH_LOAD=123456 comprehensive_bench.cpp

#include "emhash/hash_table7.hpp"

static void RunHighLoadFactor()
{
    std::random_device rd;
    const auto rand_key = rd();
    WyRand srngi(rand_key), srnge(rand_key);

    const auto max_lf   = 0.999f; //<= 0.999f
    const auto vsize    = 1u << (20 + rand_key % 6); //must be power of 2
    emhash7::HashMap<int64_t, int> myhash(vsize, max_lf);

    auto nowus = getus();
    for (size_t i = 0; i < size_t(vsize * max_lf); i++)
        myhash.emplace(srngi(), i);
    assert(myhash.bucket_count() == vsize); //no rehash

    const auto insert_time = getus() - nowus; nowus = getus();
    //erase & insert at a fixed load factor
    for (size_t i = 0; i < vsize; i++) {
        myhash.erase(srnge()); //erase an old key
        myhash[srngi()] = 1;   //insert a new key
    }
    const auto erase_time = getus() - nowus;
    printf("vsize = %d, load factor = %.4f, insert/erase = %ld/%ld ms\n",
        vsize, myhash.load_factor(), insert_time / 1000, erase_time / 1000);
    assert(myhash.load_factor() >= max_lf - 0.001);
}

Full benchmark code with multi-version comparison: bench/comprehensive_bench.cpp

Real benchmark results (1M buckets, LF=0.999, compiled with -DEMH_HIGH_LOAD=123456)

Test Environment: AMD 5800H / Windows 10 / GCC 11.3

hashmap	Insert(ms)	Erase+Insert(ms)	LF
emhash7::HashMap	44	118	99.9
emhash6::HashMap	38	202	99.9
emhash5::HashMap	42	90	99.9
emhash8::HashMap	49	110	99.9

hashmap	Fhit(ms)	Fmiss(ms)	Iter(ms)	LF
emhash7::HashMap	18	21	2	99.9
emhash6::HashMap	15	18	2	99.9
emhash5::HashMap	17	17	1	99.9

Other hash maps (absl, phmap, ska, tsl, robin_hood) cannot run at 0.999 LF — they either cap max_load_factor at ~0.875 or suffer catastrophic clustering.

Performance Charts

Integer Key Performance

String Key Performance

Structure Layout