diff --git a/src/test/cuckoocache_tests.cpp b/src/test/cuckoocache_tests.cpp index c259d7825..84ddacb08 100644 --- a/src/test/cuckoocache_tests.cpp +++ b/src/test/cuckoocache_tests.cpp @@ -1,373 +1,397 @@ // 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 "cuckoocache.h" #include "random.h" #include "script/sigcache.h" #include "test/test_bitcoin.h" #include #include -#include /** 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) + 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 cc{}; cc.setup_bytes(32 << 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 double test_cache(size_t megabytes, double load) { local_rand_ctx = FastRandomContext(true); std::vector hashes; Cache set{}; size_t bytes = megabytes * (1 << 20); set.setup_bytes(bytes); uint32_t n_insert = static_cast(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) + 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 hashes_insert_copy = hashes; /** Do the insert */ - for (uint256 &h : hashes_insert_copy) + for (uint256 &h : hashes_insert_copy) { set.insert(h); + } /** Count the hits */ uint32_t count = 0; - for (uint256 &h : hashes) + for (uint256 &h : hashes) { count += set.contains(h, false); - double hit_rate = ((double)count) / ((double)n_insert); + } + 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 = 32; for (double load = 0.1; load < 2; load *= 2) { double hits = test_cache>( 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 void test_cache_erase(size_t megabytes) { double load = 1; local_rand_ctx = FastRandomContext(true); std::vector hashes; Cache set{}; size_t bytes = megabytes * (1 << 20); set.setup_bytes(bytes); uint32_t n_insert = static_cast(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) + 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 hashes_insert_copy = hashes; /** Insert the first half */ - for (uint32_t i = 0; i < (n_insert / 2); ++i) + 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) + 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) + 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) + 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) + } + 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) + } + 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 = 32; test_cache_erase>( megabytes); } template void test_cache_erase_parallel(size_t megabytes) { double load = 1; local_rand_ctx = FastRandomContext(true); std::vector hashes; Cache set{}; size_t bytes = megabytes * (1 << 20); set.setup_bytes(bytes); uint32_t n_insert = static_cast(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) + 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 hashes_insert_copy = hashes; boost::shared_mutex mtx; { /** Grab lock to make sure we release inserts */ boost::unique_lock l(mtx); /** Insert the first half */ - for (uint32_t i = 0; i < (n_insert / 2); ++i) + 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 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 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) + for (uint32_t i = start; i < end; ++i) { set.contains(hashes[i], true); + } }); /** Wait for all threads to finish */ - for (std::thread &t : threads) + for (std::thread &t : threads) { t.join(); + } /** Grab lock to make sure we observe erases */ boost::unique_lock l(mtx); /** Insert the second half */ - for (uint32_t i = (n_insert / 2); i < n_insert; ++i) + 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) + 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) + } + 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) + } + 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 = 32; test_cache_erase_parallel< CuckooCache::cache>(megabytes); } template 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 reads; block_activity(uint32_t n_insert, Cache &c) : reads() { std::vector 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) + for (uint8_t j = 0; j < 8; ++j) { *(ptr++) = local_rand_ctx.rand32(); + } } - for (uint32_t i = 0; i < n_insert / 4; ++i) + 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) + } + for (uint32_t i = n_insert - (n_insert / 4); i < n_insert; ++i) { reads.push_back(inserts[i]); - for (auto h : inserts) + } + for (auto h : inserts) { c.insert(h); + } } }; const uint32_t BLOCK_SIZE = 10000; // 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 = 32; const size_t bytes = megabytes * (1 << 20); const uint32_t n_insert = static_cast(load * (bytes / sizeof(uint256))); std::vector hashes; Cache set{}; set.setup_bytes(bytes); hashes.reserve(n_insert / BLOCK_SIZE); std::deque 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 (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); + 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 implicityly, 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>(); } BOOST_AUTO_TEST_SUITE_END();