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// Copyright 2017 The Rust Project Developers. See the COPYRIGHT // file at the top-level directory of this distribution and at // http://rust-lang.org/COPYRIGHT. // // Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or // http://www.apache.org/licenses/LICENSE-2.0> or the MIT license // <LICENSE-MIT or http://opensource.org/licenses/MIT>, at your // option. This file may not be copied, modified, or distributed // except according to those terms. // // Based on jitterentropy-library, http://www.chronox.de/jent.html. // Copyright Stephan Mueller <smueller@chronox.de>, 2014 - 2017. // // With permission from Stephan Mueller to relicense the Rust translation under // the MIT license. //! Non-physical true random number generator based on timing jitter. use Rng; use core::{fmt, mem, ptr}; #[cfg(feature="std")] use std::sync::atomic::{AtomicUsize, ATOMIC_USIZE_INIT, Ordering}; const MEMORY_BLOCKS: usize = 64; const MEMORY_BLOCKSIZE: usize = 32; const MEMORY_SIZE: usize = MEMORY_BLOCKS * MEMORY_BLOCKSIZE; /// A true random number generator based on jitter in the CPU execution time, /// and jitter in memory access time. /// /// This is a true random number generator, as opposed to pseudo-random /// generators. Random numbers generated by `JitterRng` can be seen as fresh /// entropy. A consequence is that is orders of magnitude slower than `OsRng` /// and PRNGs (about 10^3 .. 10^6 slower). /// /// There are very few situations where using this RNG is appropriate. Only very /// few applications require true entropy. A normal PRNG can be statistically /// indistinguishable, and a cryptographic PRNG should also be as impossible to /// predict. /// /// Use of `JitterRng` is recommended for initializing cryptographic PRNGs when /// `OsRng` is not available. /// /// This implementation is based on /// [Jitterentropy](http://www.chronox.de/jent.html) version 2.1.0. // // Note: the C implementation relies on being compiled without optimizations. // This implementation goes through lengths to make the compiler not optimise // out what is technically dead code, but that does influence timing jitter. pub struct JitterRng { data: u64, // Actual random number // Number of rounds to run the entropy collector per 64 bits rounds: u32, // Timer and previous time stamp, used by `measure_jitter` timer: fn() -> u64, prev_time: u64, // Deltas used for the stuck test last_delta: i64, last_delta2: i64, // Memory for the Memory Access noise source mem_prev_index: usize, mem: [u8; MEMORY_SIZE], // Make `next_u32` not waste 32 bits data_remaining: Option<u32>, } // Custom Debug implementation that does not expose the internal state impl fmt::Debug for JitterRng { fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result { write!(f, "JitterRng {{}}") } } /// An error that can occur when `test_timer` fails. #[derive(Debug, Clone, PartialEq, Eq)] pub enum TimerError { /// No timer available. NoTimer, /// Timer too coarse to use as an entropy source. CoarseTimer, /// Timer is not monotonically increasing. NotMonotonic, /// Variations of deltas of time too small. TinyVariantions, /// Too many stuck results (indicating no added entropy). TooManyStuck, #[doc(hidden)] __Nonexhaustive, } impl TimerError { fn description(&self) -> &'static str { match *self { TimerError::NoTimer => "no timer available", TimerError::CoarseTimer => "coarse timer", TimerError::NotMonotonic => "timer not monotonic", TimerError::TinyVariantions => "time delta variations too small", TimerError::TooManyStuck => "too many stuck results", TimerError::__Nonexhaustive => unreachable!(), } } } impl fmt::Display for TimerError { fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result { write!(f, "{}", self.description()) } } #[cfg(feature="std")] impl ::std::error::Error for TimerError { fn description(&self) -> &str { self.description() } } // Initialise to zero; must be positive #[cfg(feature="std")] static JITTER_ROUNDS: AtomicUsize = ATOMIC_USIZE_INIT; impl JitterRng { /// Create a new `JitterRng`. /// Makes use of `std::time` for a timer. /// /// During initialization CPU execution timing jitter is measured a few /// hundred times. If this does not pass basic quality tests, an error is /// returned. The test result is cached to make subsequent calls faster. #[cfg(feature="std")] pub fn new() -> Result<JitterRng, TimerError> { let mut ec = JitterRng::new_with_timer(platform::get_nstime); let mut rounds = JITTER_ROUNDS.load(Ordering::Relaxed) as u32; if rounds == 0 { // No result yet: run test. // This allows the timer test to run multiple times; we don't care. rounds = ec.test_timer()?; JITTER_ROUNDS.store(rounds as usize, Ordering::Relaxed); } ec.set_rounds(rounds); Ok(ec) } /// Create a new `JitterRng`. /// A custom timer can be supplied, making it possible to use `JitterRng` in /// `no_std` environments. /// /// The timer must have nanosecond precision. /// /// This method is more low-level than `new()`. It is the responsibility of /// the caller to run `test_timer` before using any numbers generated with /// `JitterRng`, and optionally call `set_rounds()`. pub fn new_with_timer(timer: fn() -> u64) -> JitterRng { let mut ec = JitterRng { data: 0, rounds: 64, timer: timer, prev_time: 0, last_delta: 0, last_delta2: 0, mem_prev_index: 0, mem: [0; MEMORY_SIZE], data_remaining: None, }; // Fill `data`, `prev_time`, `last_delta` and `last_delta2` with // non-zero values. ec.prev_time = timer(); ec.gen_entropy(); // Do a single read from `self.mem` to make sure the Memory Access noise // source is not optimised out. // Note: this read is important, it effects optimisations for the entire // module! black_box(ec.mem[0]); ec } /// Configures how many rounds are used to generate each 64-bit value. /// This must be greater than zero, and has a big impact on performance /// and output quality. /// /// `new_with_timer` conservatively uses 64 rounds, but often less rounds /// can be used. The `test_timer()` function returns the minimum number of /// rounds required for full strength (platform dependent), so one may use /// `rng.set_rounds(rng.test_timer()?);` or cache the value. pub fn set_rounds(&mut self, rounds: u32) { assert!(rounds > 0); self.rounds = rounds; } // Calculate a random loop count used for the next round of an entropy // collection, based on bits from a fresh value from the timer. // // The timer is folded to produce a number that contains at most `n_bits` // bits. // // Note: A constant should be added to the resulting random loop count to // prevent loops that run 0 times. #[inline(never)] fn random_loop_cnt(&mut self, n_bits: u32) -> u32 { let mut rounds = 0; let mut time = (self.timer)(); // Mix with the current state of the random number balance the random // loop counter a bit more. time ^= self.data; // We fold the time value as much as possible to ensure that as many // bits of the time stamp are included as possible. let folds = (64 + n_bits - 1) / n_bits; let mask = (1 << n_bits) - 1; for _ in 0..folds { rounds ^= time & mask; time = time >> n_bits; } rounds as u32 } // CPU jitter noise source // Noise source based on the CPU execution time jitter // // This function injects the individual bits of the time value into the // entropy pool using an LFSR. // // The code is deliberately inefficient with respect to the bit shifting. // This function not only acts as folding operation, but this function's // execution is used to measure the CPU execution time jitter. Any change to // the loop in this function implies that careful retesting must be done. #[inline(never)] fn lfsr_time(&mut self, time: u64, var_rounds: bool) { fn lfsr(mut data: u64, time: u64) -> u64{ for i in 1..65 { let mut tmp = time << (64 - i); tmp = tmp >> (64 - 1); // Fibonacci LSFR with polynomial of // x^64 + x^61 + x^56 + x^31 + x^28 + x^23 + 1 which is // primitive according to // http://poincare.matf.bg.ac.rs/~ezivkovm/publications/primpol1.pdf // (the shift values are the polynomial values minus one // due to counting bits from 0 to 63). As the current // position is always the LSB, the polynomial only needs // to shift data in from the left without wrap. data ^= tmp; data ^= (data >> 63) & 1; data ^= (data >> 60) & 1; data ^= (data >> 55) & 1; data ^= (data >> 30) & 1; data ^= (data >> 27) & 1; data ^= (data >> 22) & 1; data = data.rotate_left(1); } data } // Note: in the reference implementation only the last round effects // `self.data`, all the other results are ignored. To make sure the // other rounds are not optimised out, we first run all but the last // round on a throw-away value instead of the real `self.data`. let mut lfsr_loop_cnt = 0; if var_rounds { lfsr_loop_cnt = self.random_loop_cnt(4) }; let mut throw_away: u64 = 0; for _ in 0..lfsr_loop_cnt { throw_away = lfsr(throw_away, time); } black_box(throw_away); self.data = lfsr(self.data, time); } // Memory Access noise source // This is a noise source based on variations in memory access times // // This function performs memory accesses which will add to the timing // variations due to an unknown amount of CPU wait states that need to be // added when accessing memory. The memory size should be larger than the L1 // caches as outlined in the documentation and the associated testing. // // The L1 cache has a very high bandwidth, albeit its access rate is usually // slower than accessing CPU registers. Therefore, L1 accesses only add // minimal variations as the CPU has hardly to wait. Starting with L2, // significant variations are added because L2 typically does not belong to // the CPU any more and therefore a wider range of CPU wait states is // necessary for accesses. L3 and real memory accesses have even a wider // range of wait states. However, to reliably access either L3 or memory, // the `self.mem` memory must be quite large which is usually not desirable. #[inline(never)] fn memaccess(&mut self, var_rounds: bool) { let mut acc_loop_cnt = 128; if var_rounds { acc_loop_cnt += self.random_loop_cnt(4) }; let mut index = self.mem_prev_index; for _ in 0..acc_loop_cnt { // Addition of memblocksize - 1 to index with wrap around logic to // ensure that every memory location is hit evenly. // The modulus also allows the compiler to remove the indexing // bounds check. index = (index + MEMORY_BLOCKSIZE - 1) % MEMORY_SIZE; // memory access: just add 1 to one byte // memory access implies read from and write to memory location let tmp = self.mem[index]; self.mem[index] = tmp.wrapping_add(1); } self.mem_prev_index = index; } // Stuck test by checking the: // - 1st derivation of the jitter measurement (time delta) // - 2nd derivation of the jitter measurement (delta of time deltas) // - 3rd derivation of the jitter measurement (delta of delta of time // deltas) // // All values must always be non-zero. // This test is a heuristic to see whether the last measurement holds // entropy. fn stuck(&mut self, current_delta: i64) -> bool { let delta2 = self.last_delta - current_delta; let delta3 = delta2 - self.last_delta2; self.last_delta = current_delta; self.last_delta2 = delta2; current_delta == 0 || delta2 == 0 || delta3 == 0 } // This is the heart of the entropy generation: calculate time deltas and // use the CPU jitter in the time deltas. The jitter is injected into the // entropy pool. // // Ensure that `self.prev_time` is primed before using the output of this // function. This can be done by calling this function and not using its // result. fn measure_jitter(&mut self) -> Option<()> { // Invoke one noise source before time measurement to add variations self.memaccess(true); // Get time stamp and calculate time delta to previous // invocation to measure the timing variations let time = (self.timer)(); // Note: wrapping_sub combined with a cast to `i64` generates a correct // delta, even in the unlikely case this is a timer that is not strictly // monotonic. let current_delta = time.wrapping_sub(self.prev_time) as i64; self.prev_time = time; // Call the next noise source which also injects the data self.lfsr_time(current_delta as u64, true); // Check whether we have a stuck measurement (i.e. does the last // measurement holds entropy?). if self.stuck(current_delta) { return None }; // Rotate the data buffer by a prime number (any odd number would // do) to ensure that every bit position of the input time stamp // has an even chance of being merged with a bit position in the // entropy pool. We do not use one here as the adjacent bits in // successive time deltas may have some form of dependency. The // chosen value of 7 implies that the low 7 bits of the next // time delta value is concatenated with the current time delta. self.data = self.data.rotate_left(7); Some(()) } // Shuffle the pool a bit by mixing some value with a bijective function // (XOR) into the pool. // // The function generates a mixer value that depends on the bits set and // the location of the set bits in the random number generated by the // entropy source. Therefore, based on the generated random number, this // mixer value can have 2^64 different values. That mixer value is // initialized with the first two SHA-1 constants. After obtaining the // mixer value, it is XORed into the random number. // // The mixer value is not assumed to contain any entropy. But due to the // XOR operation, it can also not destroy any entropy present in the // entropy pool. #[inline(never)] fn stir_pool(&mut self) { // This constant is derived from the first two 32 bit initialization // vectors of SHA-1 as defined in FIPS 180-4 section 5.3.1 // The order does not really matter as we do not rely on the specific // numbers. We just pick the SHA-1 constants as they have a good mix of // bit set and unset. const CONSTANT: u64 = 0x67452301efcdab89; // The start value of the mixer variable is derived from the third // and fourth 32 bit initialization vector of SHA-1 as defined in // FIPS 180-4 section 5.3.1 let mut mixer = 0x98badcfe10325476; // This is a constant time function to prevent leaking timing // information about the random number. // The normal code is: // ``` // for i in 0..64 { // if ((self.data >> i) & 1) == 1 { mixer ^= CONSTANT; } // } // ``` // This is a bit fragile, as LLVM really wants to use branches here, and // we rely on it to not recognise the opportunity. for i in 0..64 { let apply = (self.data >> i) & 1; let mask = !apply.wrapping_sub(1); mixer ^= CONSTANT & mask; mixer = mixer.rotate_left(1); } self.data ^= mixer; } fn gen_entropy(&mut self) -> u64 { // Prime `self.prev_time`, and run the noice sources to make sure the // first loop round collects the expected entropy. let _ = self.measure_jitter(); for _ in 0..self.rounds { // If a stuck measurement is received, repeat measurement // Note: we do not guard against an infinite loop, that would mean // the timer suddenly became broken. while self.measure_jitter().is_none() {} } self.stir_pool(); self.data } /// Basic quality tests on the timer, by measuring CPU timing jitter a few /// hundred times. /// /// If succesful, this will return the estimated number of rounds necessary /// to collect 64 bits of entropy. Otherwise a `TimerError` with the cause /// of the failure will be returned. pub fn test_timer(&mut self) -> Result<u32, TimerError> { // We could add a check for system capabilities such as `clock_getres` // or check for `CONFIG_X86_TSC`, but it does not make much sense as the // following sanity checks verify that we have a high-resolution timer. #[cfg(all(target_arch = "wasm32", not(target_os = "emscripten")))] return Err(TimerError::NoTimer); let mut delta_sum = 0; let mut old_delta = 0; let mut time_backwards = 0; let mut count_mod = 0; let mut count_stuck = 0; // TESTLOOPCOUNT needs some loops to identify edge systems. // 100 is definitely too little. const TESTLOOPCOUNT: u64 = 300; const CLEARCACHE: u64 = 100; for i in 0..(CLEARCACHE + TESTLOOPCOUNT) { // Measure time delta of core entropy collection logic let time = (self.timer)(); self.memaccess(true); self.lfsr_time(time, true); let time2 = (self.timer)(); // Test whether timer works if time == 0 || time2 == 0 { return Err(TimerError::NoTimer); } let delta = time2.wrapping_sub(time) as i64; // Test whether timer is fine grained enough to provide delta even // when called shortly after each other -- this implies that we also // have a high resolution timer if delta == 0 { return Err(TimerError::CoarseTimer); } // Up to here we did not modify any variable that will be // evaluated later, but we already performed some work. Thus we // already have had an impact on the caches, branch prediction, // etc. with the goal to clear it to get the worst case // measurements. if i < CLEARCACHE { continue; } if self.stuck(delta) { count_stuck += 1; } // Test whether we have an increasing timer. if !(time2 > time) { time_backwards += 1; } // Count the number of times the counter increases in steps of 100ns // or greater. if (delta % 100) == 0 { count_mod += 1; } // Ensure that we have a varying delta timer which is necessary for // the calculation of entropy -- perform this check only after the // first loop is executed as we need to prime the old_delta value delta_sum += (delta - old_delta).abs() as u64; old_delta = delta; } // We allow the time to run backwards for up to three times. // This can happen if the clock is being adjusted by NTP operations. // If such an operation just happens to interfere with our test, it // should not fail. The value of 3 should cover the NTP case being // performed during our test run. if time_backwards > 3 { return Err(TimerError::NotMonotonic); } // Test that the available amount of entropy per round does not get to // low. We expect 1 bit of entropy per round as a reasonable minimum // (although less is possible, it means the collector loop has to run // much more often). // `assert!(delta_average >= log2(1))` // `assert!(delta_sum / TESTLOOPCOUNT >= 1)` // `assert!(delta_sum >= TESTLOOPCOUNT)` if delta_sum < TESTLOOPCOUNT { return Err(TimerError::TinyVariantions); } // Ensure that we have variations in the time stamp below 100 for at // least 10% of all checks -- on some platforms, the counter increments // in multiples of 100, but not always if count_mod > (TESTLOOPCOUNT * 9 / 10) { return Err(TimerError::CoarseTimer); } // If we have more than 90% stuck results, then this Jitter RNG is // likely to not work well. if count_stuck > (TESTLOOPCOUNT * 9 / 10) { return Err(TimerError::TooManyStuck); } // Estimate the number of `measure_jitter` rounds necessary for 64 bits // of entropy. // // We don't try very hard to come up with a good estimate of the // available bits of entropy per round here for two reasons: // 1. Simple estimates of the available bits (like Shannon entropy) are // too optimistic. // 2) Unless we want to waste a lot of time during intialization, there // only a small number of samples are available. // // Therefore we use a very simple and conservative estimate: // `let bits_of_entropy = log2(delta_average) / 2`. // // The number of rounds `measure_jitter` should run to collect 64 bits // of entropy is `64 / bits_of_entropy`. // // To have smaller rounding errors, intermediate values are multiplied // by `FACTOR`. To compensate for `log2` and division rounding down, // add 1. let delta_average = delta_sum / TESTLOOPCOUNT; // println!("delta_average: {}", delta_average); const FACTOR: u32 = 3; fn log2(x: u64) -> u32 { 64 - x.leading_zeros() } // pow(δ, FACTOR) must be representable; if you have overflow reduce FACTOR Ok(64 * 2 * FACTOR / (log2(delta_average.pow(FACTOR)) + 1)) } /// Statistical test: return the timer delta of one normal run of the /// `JitterEntropy` entropy collector. /// /// Setting `var_rounds` to `true` will execute the memory access and the /// CPU jitter noice sources a variable amount of times (just like a real /// `JitterEntropy` round). /// /// Setting `var_rounds` to `false` will execute the noice sources the /// minimal number of times. This can be used to measure the minimum amount /// of entropy one round of entropy collector can collect in the worst case. /// /// # Example /// /// Use `timer_stats` to run the [NIST SP 800-90B Entropy Estimation Suite] /// (https://github.com/usnistgov/SP800-90B_EntropyAssessment). /// /// This is the recommended way to test the quality of `JitterRng`. It /// should be run before using the RNG on untested hardware, after changes /// that could effect how the code is optimised, and after major compiler /// compiler changes, like a new LLVM version. /// /// First generate two files `jitter_rng_var.bin` and `jitter_rng_var.min`. /// /// Execute `python noniid_main.py -v jitter_rng_var.bin 8`, and validate it /// with `restart.py -v jitter_rng_var.bin 8 <min-entropy>`. /// This number is the expected amount of entropy that is at least available /// for each round of the entropy collector. This number should be greater /// than the amount estimated with `64 / test_timer()`. /// /// Execute `python noniid_main.py -v -u 4 jitter_rng_var.bin 4`, and /// validate it with `restart.py -v -u 4 jitter_rng_var.bin 4 <min-entropy>`. /// This number is the expected amount of entropy that is available in the /// last 4 bits of the timer delta after running noice sources. Note that /// a value of 3.70 is the minimum estimated entropy for true randomness. /// /// Execute `python noniid_main.py -v -u 4 jitter_rng_var.bin 4`, and /// validate it with `restart.py -v -u 4 jitter_rng_var.bin 4 <min-entropy>`. /// This number is the expected amount of entropy that is available to the /// entropy collecter if both noice sources only run their minimal number of /// times. This measures the absolute worst-case, and gives a lower bound /// for the available entropy. /// /// ```rust,no_run /// use rand::JitterRng; /// /// # use std::error::Error; /// # use std::fs::File; /// # use std::io::Write; /// # /// # fn try_main() -> Result<(), Box<Error>> { /// fn get_nstime() -> u64 { /// use std::time::{SystemTime, UNIX_EPOCH}; /// /// let dur = SystemTime::now().duration_since(UNIX_EPOCH).unwrap(); /// // The correct way to calculate the current time is /// // `dur.as_secs() * 1_000_000_000 + dur.subsec_nanos() as u64` /// // But this is faster, and the difference in terms of entropy is /// // negligible (log2(10^9) == 29.9). /// dur.as_secs() << 30 | dur.subsec_nanos() as u64 /// } /// /// // Do not initialize with `JitterRng::new`, but with `new_with_timer`. /// // 'new' always runst `test_timer`, and can therefore fail to /// // initialize. We want to be able to get the statistics even when the /// // timer test fails. /// let mut rng = JitterRng::new_with_timer(get_nstime); /// /// // 1_000_000 results are required for the NIST SP 800-90B Entropy /// // Estimation Suite /// // FIXME: this number is smaller here, otherwise the Doc-test is too slow /// const ROUNDS: usize = 10_000; /// let mut deltas_variable: Vec<u8> = Vec::with_capacity(ROUNDS); /// let mut deltas_minimal: Vec<u8> = Vec::with_capacity(ROUNDS); /// /// for _ in 0..ROUNDS { /// deltas_variable.push(rng.timer_stats(true) as u8); /// deltas_minimal.push(rng.timer_stats(false) as u8); /// } /// /// // Write out after the statistics collection loop, to not disturb the /// // test results. /// File::create("jitter_rng_var.bin")?.write(&deltas_variable)?; /// File::create("jitter_rng_min.bin")?.write(&deltas_minimal)?; /// # /// # Ok(()) /// # } /// # /// # fn main() { /// # try_main().unwrap(); /// # } /// ``` #[cfg(feature="std")] pub fn timer_stats(&mut self, var_rounds: bool) -> i64 { let time = platform::get_nstime(); self.memaccess(var_rounds); self.lfsr_time(time, var_rounds); let time2 = platform::get_nstime(); time2.wrapping_sub(time) as i64 } } #[cfg(feature="std")] mod platform { #[cfg(not(any(target_os = "macos", target_os = "ios", target_os = "windows", all(target_arch = "wasm32", not(target_os = "emscripten")))))] pub fn get_nstime() -> u64 { use std::time::{SystemTime, UNIX_EPOCH}; let dur = SystemTime::now().duration_since(UNIX_EPOCH).unwrap(); // The correct way to calculate the current time is // `dur.as_secs() * 1_000_000_000 + dur.subsec_nanos() as u64` // But this is faster, and the difference in terms of entropy is negligible // (log2(10^9) == 29.9). dur.as_secs() << 30 | dur.subsec_nanos() as u64 } #[cfg(any(target_os = "macos", target_os = "ios"))] pub fn get_nstime() -> u64 { extern crate libc; // On Mac OS and iOS std::time::SystemTime only has 1000ns resolution. // We use `mach_absolute_time` instead. This provides a CPU dependent unit, // to get real nanoseconds the result should by multiplied by numer/denom // from `mach_timebase_info`. // But we are not interested in the exact nanoseconds, just entropy. So we // use the raw result. unsafe { libc::mach_absolute_time() } } #[cfg(target_os = "windows")] pub fn get_nstime() -> u64 { extern crate winapi; unsafe { let mut t = super::mem::zeroed(); winapi::um::profileapi::QueryPerformanceCounter(&mut t); *t.QuadPart() as u64 } } #[cfg(all(target_arch = "wasm32", not(target_os = "emscripten")))] pub fn get_nstime() -> u64 { unreachable!() } } // A function that is opaque to the optimizer to assist in avoiding dead-code // elimination. Taken from `bencher`. fn black_box<T>(dummy: T) -> T { unsafe { let ret = ptr::read_volatile(&dummy); mem::forget(dummy); ret } } impl Rng for JitterRng { fn next_u32(&mut self) -> u32 { // We want to use both parts of the generated entropy if let Some(high) = self.data_remaining.take() { high } else { let data = self.next_u64(); self.data_remaining = Some((data >> 32) as u32); data as u32 } } fn next_u64(&mut self) -> u64 { self.gen_entropy() } fn fill_bytes(&mut self, dest: &mut [u8]) { let mut left = dest; while left.len() >= 8 { let (l, r) = {left}.split_at_mut(8); left = r; let chunk: [u8; 8] = unsafe { mem::transmute(self.next_u64().to_le()) }; l.copy_from_slice(&chunk); } let n = left.len(); if n > 0 { let chunk: [u8; 8] = unsafe { mem::transmute(self.next_u64().to_le()) }; left.copy_from_slice(&chunk[..n]); } } } // There are no tests included because (1) this is an "external" RNG, so output // is not reproducible and (2) `test_timer` *will* fail on some platforms.