added tests and examples for xorshift and binomial
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67
examples/binomial.cpp
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67
examples/binomial.cpp
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@ -0,0 +1,67 @@
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/* Plots the distribution histogram of the fast_binomial algorithm
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* (spoiler alert: it's pleasingly (1/2)^N all the way :D)
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*/
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#include <iostream>
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#include <array>
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#include <atomic>
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#include <thread>
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#include <iomanip>
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#include <sys/ioctl.h>
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#include <unistd.h>
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#include "utils/random/fast_binomial.hpp"
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static constexpr unsigned B = 24;
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static thread_local FastBinomial<B> rnd;
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static constexpr unsigned M = 4;
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static constexpr size_t N = 1ULL << 34;
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static constexpr size_t per_thread_iters = N / M;
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std::array<std::atomic<uint64_t>, B> buckets;
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void generate()
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{
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for(size_t i = 0; i < per_thread_iters; ++i)
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buckets[rnd() - 1].fetch_add(1);
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}
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int main(void)
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{
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struct winsize w;
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ioctl(STDOUT_FILENO, TIOCGWINSZ, &w);
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auto bar_len = w.ws_col - 20;
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std::array<std::thread, M> threads;
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for(auto& bucket : buckets)
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bucket.store(0);
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for(auto& t : threads)
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t = std::thread([]() { generate(); });
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for(auto& t : threads)
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t.join();
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auto max = std::accumulate(buckets.begin(), buckets.end(), (uint64_t)0,
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[](auto& acc, auto& x) { return std::max(acc, x.load()); });
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std::cout << std::fixed;
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for(size_t i = 0; i < buckets.size(); ++i)
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{
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auto x = buckets[i].load();
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auto rel = bar_len * x / max;
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std::cout << std::setw(2) << i + 1 << " ";
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for(size_t i = 0; i < rel; ++i)
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std::cout << "=";
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std::cout << " " << 100 * (double)x / N << "%" << std::endl;
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}
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return 0;
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}
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65
examples/xorshift.cpp
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65
examples/xorshift.cpp
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@ -0,0 +1,65 @@
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/* Plots the distribution histogram of the xorshift algorithm
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* (spoiler alert: it's pleasingly uniform all the way :D)
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*/
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#include <iostream>
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#include <array>
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#include <atomic>
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#include <thread>
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#include <sys/ioctl.h>
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#include <unistd.h>
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#include "utils/random/xorshift128plus.hpp"
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static thread_local Xorshift128plus rnd;
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static constexpr unsigned B = 1 << 10;
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static constexpr uint64_t K = (uint64_t)(-1) / B;
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static constexpr unsigned M = 4;
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static constexpr size_t N = 1ULL << 34;
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static constexpr size_t per_thread_iters = N / M;
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std::array<std::atomic<unsigned>, B> buckets;
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void generate()
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{
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for(size_t i = 0; i < per_thread_iters; ++i)
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buckets[rnd() / K].fetch_add(1);
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}
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int main(void)
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{
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struct winsize w;
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ioctl(STDOUT_FILENO, TIOCGWINSZ, &w);
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auto bar_len = w.ws_col - 20;
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std::array<std::thread, M> threads;
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for(auto& bucket : buckets)
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bucket.store(0);
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for(auto& t : threads)
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t = std::thread([]() { generate(); });
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for(auto& t : threads)
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t.join();
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auto max = std::accumulate(buckets.begin(), buckets.end(), 0u,
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[](auto& acc, auto& x) { return std::max(acc, x.load()); });
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std::cout << std::fixed;
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for(auto& bucket : buckets)
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{
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auto x = bucket.load();
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auto rel = bar_len * x / max;
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for(size_t i = 0; i < rel; ++i)
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std::cout << "=";
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std::cout << " " << 100.0 * x / N * B - 100 << "%" << std::endl;
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}
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return 0;
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}
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@ -27,11 +27,9 @@ class FastBinomial
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// ------------------
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// 16 1111 -> 5 =====
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static constexpr uint64_t mask = (1 << N) - 1;
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static constexpr uint64_t mask = (1ULL << N) - 1;
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public:
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FastBinomial() = default;
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unsigned operator()()
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{
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while(true)
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@ -44,7 +42,9 @@ public:
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auto x = random() & mask;
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// if we have all zeros, then we have an invalid case and we
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// need to generate again
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// need to generate again, we have this every (1/2)^N times
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// so therefore we could say it's very unlikely to happen for
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// large N. e.g. N = 32; p = 2.328 * 10^-10
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if(UNLIKELY(!x))
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continue;
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@ -1,5 +1,4 @@
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#ifndef MEMGRAPH_UTILS_RANDOM_XORSHIFT_HPP
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#define MEMGRAPH_UTILS_RANDOM_XORSHIFT_HPP
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#pragma once
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#include <cstdlib>
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#include <random>
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@ -25,7 +24,6 @@ public:
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// the number generated by MT can be full of zeros and xorshift
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// doesn't like this so we use MurmurHash3 64bit finalizer to
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// make it less biased
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s[0] = avalance(dist(gen));
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s[1] = avalance(dist(gen));
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}
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@ -56,5 +54,3 @@ private:
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return s;
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}
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};
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#endif
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