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kevacoin/src/test/cuckoocache_tests.cpp

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// Copyright (c) 2012-2016 The Bitcoin Core developers
// Distributed under the MIT software license, see the accompanying
// file COPYING or http://www.opensource.org/licenses/mit-license.php.
#include <boost/test/unit_test.hpp>
#include "cuckoocache.h"
#include "script/sigcache.h"
#include "test/test_bitcoin.h"
#include "random.h"
#include <thread>
/** Test Suite for CuckooCache
*
* 1) All tests should have a deterministic result (using insecure rand
* with deterministic seeds)
* 2) Some test methods are templated to allow for easier testing
* against new versions / comparing
* 3) Results should be treated as a regression test, i.e., did the behavior
* change significantly from what was expected. This can be OK, depending on
* the nature of the change, but requires updating the tests to reflect the new
* expected behavior. For example improving the hit rate may cause some tests
* using BOOST_CHECK_CLOSE to fail.
*
*/
FastRandomContext local_rand_ctx(true);
BOOST_AUTO_TEST_SUITE(cuckoocache_tests);
/** insecure_GetRandHash fills in a uint256 from local_rand_ctx
*/
void insecure_GetRandHash(uint256& t)
{
uint32_t* ptr = (uint32_t*)t.begin();
for (uint8_t j = 0; j < 8; ++j)
*(ptr++) = local_rand_ctx.rand32();
}
/* Test that no values not inserted into the cache are read out of it.
*
* There are no repeats in the first 200000 insecure_GetRandHash calls
*/
BOOST_AUTO_TEST_CASE(test_cuckoocache_no_fakes)
{
local_rand_ctx = FastRandomContext(true);
CuckooCache::cache<uint256, SignatureCacheHasher> cc{};
size_t megabytes = 4;
cc.setup_bytes(megabytes << 20);
uint256 v;
for (int x = 0; x < 100000; ++x) {
insecure_GetRandHash(v);
cc.insert(v);
}
for (int x = 0; x < 100000; ++x) {
insecure_GetRandHash(v);
BOOST_CHECK(!cc.contains(v, false));
}
};
/** This helper returns the hit rate when megabytes*load worth of entries are
* inserted into a megabytes sized cache
*/
template <typename Cache>
double test_cache(size_t megabytes, double load)
{
local_rand_ctx = FastRandomContext(true);
std::vector<uint256> hashes;
Cache set{};
size_t bytes = megabytes * (1 << 20);
set.setup_bytes(bytes);
uint32_t n_insert = static_cast<uint32_t>(load * (bytes / sizeof(uint256)));
hashes.resize(n_insert);
for (uint32_t i = 0; i < n_insert; ++i) {
uint32_t* ptr = (uint32_t*)hashes[i].begin();
for (uint8_t j = 0; j < 8; ++j)
*(ptr++) = local_rand_ctx.rand32();
}
/** We make a copy of the hashes because future optimizations of the
* cuckoocache may overwrite the inserted element, so the test is
* "future proofed".
*/
std::vector<uint256> hashes_insert_copy = hashes;
/** Do the insert */
for (uint256& h : hashes_insert_copy)
set.insert(h);
/** Count the hits */
uint32_t count = 0;
for (uint256& h : hashes)
count += set.contains(h, false);
double hit_rate = ((double)count) / ((double)n_insert);
return hit_rate;
}
/** The normalized hit rate for a given load.
*
* The semantics are a little confusing, so please see the below
* explanation.
*
* Examples:
*
* 1) at load 0.5, we expect a perfect hit rate, so we multiply by
* 1.0
* 2) at load 2.0, we expect to see half the entries, so a perfect hit rate
* would be 0.5. Therefore, if we see a hit rate of 0.4, 0.4*2.0 = 0.8 is the
* normalized hit rate.
*
* This is basically the right semantics, but has a bit of a glitch depending on
* how you measure around load 1.0 as after load 1.0 your normalized hit rate
* becomes effectively perfect, ignoring freshness.
*/
double normalize_hit_rate(double hits, double load)
{
return hits * std::max(load, 1.0);
}
/** Check the hit rate on loads ranging from 0.1 to 2.0 */
BOOST_AUTO_TEST_CASE(cuckoocache_hit_rate_ok)
{
/** Arbitrarily selected Hit Rate threshold that happens to work for this test
* as a lower bound on performance.
*/
double HitRateThresh = 0.98;
size_t megabytes = 4;
for (double load = 0.1; load < 2; load *= 2) {
double hits = test_cache<CuckooCache::cache<uint256, SignatureCacheHasher>>(megabytes, load);
BOOST_CHECK(normalize_hit_rate(hits, load) > HitRateThresh);
}
}
/** This helper checks that erased elements are preferentially inserted onto and
* that the hit rate of "fresher" keys is reasonable*/
template <typename Cache>
void test_cache_erase(size_t megabytes)
{
double load = 1;
local_rand_ctx = FastRandomContext(true);
std::vector<uint256> hashes;
Cache set{};
size_t bytes = megabytes * (1 << 20);
set.setup_bytes(bytes);
uint32_t n_insert = static_cast<uint32_t>(load * (bytes / sizeof(uint256)));
hashes.resize(n_insert);
for (uint32_t i = 0; i < n_insert; ++i) {
uint32_t* ptr = (uint32_t*)hashes[i].begin();
for (uint8_t j = 0; j < 8; ++j)
*(ptr++) = local_rand_ctx.rand32();
}
/** We make a copy of the hashes because future optimizations of the
* cuckoocache may overwrite the inserted element, so the test is
* "future proofed".
*/
std::vector<uint256> hashes_insert_copy = hashes;
/** Insert the first half */
for (uint32_t i = 0; i < (n_insert / 2); ++i)
set.insert(hashes_insert_copy[i]);
/** Erase the first quarter */
for (uint32_t i = 0; i < (n_insert / 4); ++i)
set.contains(hashes[i], true);
/** Insert the second half */
for (uint32_t i = (n_insert / 2); i < n_insert; ++i)
set.insert(hashes_insert_copy[i]);
/** elements that we marked erased but that are still there */
size_t count_erased_but_contained = 0;
/** elements that we did not erase but are older */
size_t count_stale = 0;
/** elements that were most recently inserted */
size_t count_fresh = 0;
for (uint32_t i = 0; i < (n_insert / 4); ++i)
count_erased_but_contained += set.contains(hashes[i], false);
for (uint32_t i = (n_insert / 4); i < (n_insert / 2); ++i)
count_stale += set.contains(hashes[i], false);
for (uint32_t i = (n_insert / 2); i < n_insert; ++i)
count_fresh += set.contains(hashes[i], false);
double hit_rate_erased_but_contained = double(count_erased_but_contained) / (double(n_insert) / 4.0);
double hit_rate_stale = double(count_stale) / (double(n_insert) / 4.0);
double hit_rate_fresh = double(count_fresh) / (double(n_insert) / 2.0);
// Check that our hit_rate_fresh is perfect
BOOST_CHECK_EQUAL(hit_rate_fresh, 1.0);
// Check that we have a more than 2x better hit rate on stale elements than
// erased elements.
BOOST_CHECK(hit_rate_stale > 2 * hit_rate_erased_but_contained);
}
BOOST_AUTO_TEST_CASE(cuckoocache_erase_ok)
{
size_t megabytes = 4;
test_cache_erase<CuckooCache::cache<uint256, SignatureCacheHasher>>(megabytes);
}
template <typename Cache>
void test_cache_erase_parallel(size_t megabytes)
{
double load = 1;
local_rand_ctx = FastRandomContext(true);
std::vector<uint256> hashes;
Cache set{};
size_t bytes = megabytes * (1 << 20);
set.setup_bytes(bytes);
uint32_t n_insert = static_cast<uint32_t>(load * (bytes / sizeof(uint256)));
hashes.resize(n_insert);
for (uint32_t i = 0; i < n_insert; ++i) {
uint32_t* ptr = (uint32_t*)hashes[i].begin();
for (uint8_t j = 0; j < 8; ++j)
*(ptr++) = local_rand_ctx.rand32();
}
/** We make a copy of the hashes because future optimizations of the
* cuckoocache may overwrite the inserted element, so the test is
* "future proofed".
*/
std::vector<uint256> hashes_insert_copy = hashes;
boost::shared_mutex mtx;
{
/** Grab lock to make sure we release inserts */
boost::unique_lock<boost::shared_mutex> l(mtx);
/** Insert the first half */
for (uint32_t i = 0; i < (n_insert / 2); ++i)
set.insert(hashes_insert_copy[i]);
}
/** Spin up 3 threads to run contains with erase.
*/
std::vector<std::thread> threads;
/** Erase the first quarter */
for (uint32_t x = 0; x < 3; ++x)
/** Each thread is emplaced with x copy-by-value
*/
threads.emplace_back([&, x] {
boost::shared_lock<boost::shared_mutex> l(mtx);
size_t ntodo = (n_insert/4)/3;
size_t start = ntodo*x;
size_t end = ntodo*(x+1);
for (uint32_t i = start; i < end; ++i)
set.contains(hashes[i], true);
});
/** Wait for all threads to finish
*/
for (std::thread& t : threads)
t.join();
/** Grab lock to make sure we observe erases */
boost::unique_lock<boost::shared_mutex> l(mtx);
/** Insert the second half */
for (uint32_t i = (n_insert / 2); i < n_insert; ++i)
set.insert(hashes_insert_copy[i]);
/** elements that we marked erased but that are still there */
size_t count_erased_but_contained = 0;
/** elements that we did not erase but are older */
size_t count_stale = 0;
/** elements that were most recently inserted */
size_t count_fresh = 0;
for (uint32_t i = 0; i < (n_insert / 4); ++i)
count_erased_but_contained += set.contains(hashes[i], false);
for (uint32_t i = (n_insert / 4); i < (n_insert / 2); ++i)
count_stale += set.contains(hashes[i], false);
for (uint32_t i = (n_insert / 2); i < n_insert; ++i)
count_fresh += set.contains(hashes[i], false);
double hit_rate_erased_but_contained = double(count_erased_but_contained) / (double(n_insert) / 4.0);
double hit_rate_stale = double(count_stale) / (double(n_insert) / 4.0);
double hit_rate_fresh = double(count_fresh) / (double(n_insert) / 2.0);
// Check that our hit_rate_fresh is perfect
BOOST_CHECK_EQUAL(hit_rate_fresh, 1.0);
// Check that we have a more than 2x better hit rate on stale elements than
// erased elements.
BOOST_CHECK(hit_rate_stale > 2 * hit_rate_erased_but_contained);
}
BOOST_AUTO_TEST_CASE(cuckoocache_erase_parallel_ok)
{
size_t megabytes = 4;
test_cache_erase_parallel<CuckooCache::cache<uint256, SignatureCacheHasher>>(megabytes);
}
template <typename Cache>
void test_cache_generations()
{
// This test checks that for a simulation of network activity, the fresh hit
// rate is never below 99%, and the number of times that it is worse than
// 99.9% are less than 1% of the time.
double min_hit_rate = 0.99;
double tight_hit_rate = 0.999;
double max_rate_less_than_tight_hit_rate = 0.01;
// A cache that meets this specification is therefore shown to have a hit
// rate of at least tight_hit_rate * (1 - max_rate_less_than_tight_hit_rate) +
// min_hit_rate*max_rate_less_than_tight_hit_rate = 0.999*99%+0.99*1% == 99.89%
// hit rate with low variance.
// We use deterministic values, but this test has also passed on many
// iterations with non-deterministic values, so it isn't "overfit" to the
// specific entropy in FastRandomContext(true) and implementation of the
// cache.
local_rand_ctx = FastRandomContext(true);
// block_activity models a chunk of network activity. n_insert elements are
// adde to the cache. The first and last n/4 are stored for removal later
// and the middle n/2 are not stored. This models a network which uses half
// the signatures of recently (since the last block) added transactions
// immediately and never uses the other half.
struct block_activity {
std::vector<uint256> reads;
block_activity(uint32_t n_insert, Cache& c) : reads()
{
std::vector<uint256> inserts;
inserts.resize(n_insert);
reads.reserve(n_insert / 2);
for (uint32_t i = 0; i < n_insert; ++i) {
uint32_t* ptr = (uint32_t*)inserts[i].begin();
for (uint8_t j = 0; j < 8; ++j)
*(ptr++) = local_rand_ctx.rand32();
}
for (uint32_t i = 0; i < n_insert / 4; ++i)
reads.push_back(inserts[i]);
for (uint32_t i = n_insert - (n_insert / 4); i < n_insert; ++i)
reads.push_back(inserts[i]);
for (auto h : inserts)
c.insert(h);
}
};
const uint32_t BLOCK_SIZE = 1000;
// We expect window size 60 to perform reasonably given that each epoch
// stores 45% of the cache size (~472k).
const uint32_t WINDOW_SIZE = 60;
const uint32_t POP_AMOUNT = (BLOCK_SIZE / WINDOW_SIZE) / 2;
const double load = 10;
const size_t megabytes = 4;
const size_t bytes = megabytes * (1 << 20);
const uint32_t n_insert = static_cast<uint32_t>(load * (bytes / sizeof(uint256)));
std::vector<block_activity> hashes;
Cache set{};
set.setup_bytes(bytes);
hashes.reserve(n_insert / BLOCK_SIZE);
std::deque<block_activity> last_few;
uint32_t out_of_tight_tolerance = 0;
uint32_t total = n_insert / BLOCK_SIZE;
// we use the deque last_few to model a sliding window of blocks. at each
// step, each of the last WINDOW_SIZE block_activities checks the cache for
// POP_AMOUNT of the hashes that they inserted, and marks these erased.
for (uint32_t i = 0; i < total; ++i) {
if (last_few.size() == WINDOW_SIZE)
last_few.pop_front();
last_few.emplace_back(BLOCK_SIZE, set);
uint32_t count = 0;
for (auto& act : last_few)
for (uint32_t k = 0; k < POP_AMOUNT; ++k) {
count += set.contains(act.reads.back(), true);
act.reads.pop_back();
}
// We use last_few.size() rather than WINDOW_SIZE for the correct
// behavior on the first WINDOW_SIZE iterations where the deque is not
// full yet.
double hit = (double(count)) / (last_few.size() * POP_AMOUNT);
// Loose Check that hit rate is above min_hit_rate
BOOST_CHECK(hit > min_hit_rate);
// Tighter check, count number of times we are less than tight_hit_rate
// (and implicitly, greater than min_hit_rate)
out_of_tight_tolerance += hit < tight_hit_rate;
}
// Check that being out of tolerance happens less than
// max_rate_less_than_tight_hit_rate of the time
BOOST_CHECK(double(out_of_tight_tolerance) / double(total) < max_rate_less_than_tight_hit_rate);
}
BOOST_AUTO_TEST_CASE(cuckoocache_generations)
{
test_cache_generations<CuckooCache::cache<uint256, SignatureCacheHasher>>();
}
BOOST_AUTO_TEST_SUITE_END();