Created
February 17, 2024 03:03
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simulates hashing into least loaded of 2 32-cell bins
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#!/usr/bin/env python3 | |
import numpy | |
class BinHasher32EntriesTwoChoices: | |
def __init__(self, size_exp: int) -> None: | |
# each bin is represented by a word-size bitvector | |
self.size_exp: int = size_exp | |
self.array = numpy.zeros([2 ** (self.size_exp - 5)], dtype='uint32') | |
self.overflows: int = 0 | |
self.bin_mask: numpy.uint64 = numpy.uint64(2 ** (self.size_exp - 5) - 1) << numpy.uint64(64 - self.size_exp) | |
""" | |
Returns True if insert was successful, False on overflow. | |
""" | |
def insert(self, num: int) -> bool: | |
# first calculate secondary bin | |
# randomly permute num using Murmur3 64-bit finalizer (inline for speed) | |
sec_num = num | |
sec_num ^= sec_num >> numpy.uint64(33) | |
sec_num *= 0xff51afd7ed558ccd | |
sec_num ^= sec_num >> numpy.uint64(33) | |
sec_num *= 0xc4ceb9fe1a85ec53 | |
sec_num ^= sec_num >> numpy.uint64(33) | |
# find bin using leftmost {log2(bins)} bits of permuted value | |
pri_bin_index: int = numpy.uint64(num & self.bin_mask) >> numpy.uint64(64 - self.size_exp) | |
sec_bin_index: int = numpy.uint64(sec_num & self.bin_mask) >> numpy.uint64(64 - self.size_exp) | |
pri_bin_word: numpy.uint32 = self.array[pri_bin_index] | |
sec_bin_word: numpy.uint32 = self.array[sec_bin_index] | |
pri_bin_count = pri_bin_word.bit_count() | |
sec_bin_count = sec_bin_word.bit_count() | |
# if both bins are full then increment overflow counter and exit | |
if pri_bin_count == 32 and sec_bin_count == 32: | |
self.overflows += 1 | |
return False | |
# prefer primary bin on ties | |
# set rightmost unset bit in least loaded bin | |
if pri_bin_count <= sec_bin_count: | |
pri_bin_word = pri_bin_word | (pri_bin_word + 1) | |
self.array[pri_bin_index] = pri_bin_word | |
else: | |
sec_bin_word = sec_bin_word | (sec_bin_word + 1) | |
self.array[sec_bin_index] = sec_bin_word | |
return True | |
if __name__ == "__main__": | |
for size_exp in range(8, 27): | |
print(f"Table size: {2 ** size_exp}") | |
table = BinHasher32EntriesTwoChoices(size_exp) | |
first_overflow = False | |
for i in range(1, (2 ** table.size_exp) + 1): | |
num: int = numpy.random.randint(1, high=numpy.iinfo(numpy.uint64).max, dtype=numpy.uint64) | |
if not table.insert(num) and not first_overflow: | |
first_overflow = True | |
print(f"Overflowed at {i} elements (fraction: {i / (2 ** table.size_exp)})") | |
print(f"Overflows for {2 ** table.size_exp} elements: {table.overflows} (fraction: {table.overflows / (2 ** table.size_exp)})") |
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