diff --git a/src/Makefile.test.include b/src/Makefile.test.include index 5ce1bbb896..8178fb7429 100644 --- a/src/Makefile.test.include +++ b/src/Makefile.test.include @@ -54,6 +54,7 @@ BITCOIN_TESTS =\ test/coins_tests.cpp \ test/compress_tests.cpp \ test/crypto_tests.cpp \ + test/cuckoocache_tests.cpp \ test/DoS_tests.cpp \ test/getarg_tests.cpp \ test/hash_tests.cpp \ diff --git a/src/test/cuckoocache_tests.cpp b/src/test/cuckoocache_tests.cpp new file mode 100644 index 0000000000..1bc50d5ea9 --- /dev/null +++ b/src/test/cuckoocache_tests.cpp @@ -0,0 +1,394 @@ +// 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 +#include "cuckoocache.h" +#include "test/test_bitcoin.h" +#include "random.h" +#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, ie, 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 insecure_rand(true); + +BOOST_AUTO_TEST_SUITE(cuckoocache_tests); + + +/** insecure_GetRandHash fills in a uint256 from insecure_rand + */ +void insecure_GetRandHash(uint256& t) +{ + uint32_t* ptr = (uint32_t*)t.begin(); + for (uint8_t j = 0; j < 8; ++j) + *(ptr++) = insecure_rand.rand32(); +} + +/** Definition copied from /src/script/sigcache.cpp + */ +class uint256Hasher +{ +public: + template + uint32_t operator()(const uint256& key) const + { + static_assert(hash_select <8, "SignatureCacheHasher only has 8 hashes available."); + uint32_t u; + std::memcpy(&u, key.begin() + 4 * hash_select, 4); + return u; + } +}; + + +/* 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) +{ + insecure_rand = 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) +{ + insecure_rand = 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) + *(ptr++) = insecure_rand.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) + 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 = 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; + insecure_rand = 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) + *(ptr++) = insecure_rand.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) + 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 = 32; + test_cache_erase>(megabytes); +} + +template +void test_cache_erase_parallel(size_t megabytes) +{ + double load = 1; + insecure_rand = 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) + *(ptr++) = insecure_rand.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) + 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) + 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 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 = 32; + test_cache_erase_parallel>(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. + insecure_rand = 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) + *(ptr++) = insecure_rand.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 = 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 (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 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();