diff options
Diffstat (limited to 'src/libextra/test.rs')
| -rw-r--r-- | src/libextra/test.rs | 249 |
1 files changed, 123 insertions, 126 deletions
diff --git a/src/libextra/test.rs b/src/libextra/test.rs index 72e70943ce1..50ca96e6e21 100644 --- a/src/libextra/test.rs +++ b/src/libextra/test.rs @@ -19,15 +19,21 @@ use core::prelude::*; use getopts; use sort; +use stats::Stats; use term; +use time::precise_time_ns; use core::comm::{stream, SharedChan}; use core::either; use core::io; +use core::num; use core::option; +use core::rand::RngUtil; +use core::rand; use core::result; use core::task; use core::to_str::ToStr; +use core::u64; use core::uint; use core::vec; @@ -139,7 +145,7 @@ type OptRes = Either<TestOpts, ~str>; // Parses command line arguments into test options pub fn parse_opts(args: &[~str]) -> OptRes { - let args_ = vec::tail(args); + let args_ = args.tail(); let opts = ~[getopts::optflag("ignored"), getopts::optflag("test"), getopts::optflag("bench"), @@ -431,7 +437,7 @@ fn run_tests(opts: &TestOpts, callback(TeFiltered(filtered_descs)); let (filtered_tests, filtered_benchs) = - do vec::partition(filtered_tests) |e| { + do filtered_tests.partition |e| { match e.testfn { StaticTestFn(_) | DynTestFn(_) => true, StaticBenchFn(_) | DynBenchFn(_) => false @@ -609,152 +615,143 @@ fn calc_result(desc: &TestDesc, task_succeeded: bool) -> TestResult { } } -pub mod bench { - use core::prelude::*; - - use core::num; - use core::rand::RngUtil; - use core::rand; - use core::u64; - use core::vec; - use stats::Stats; - use test::{BenchHarness, BenchSamples}; - use time::precise_time_ns; - - impl BenchHarness { - /// Callback for benchmark functions to run in their body. - pub fn iter(&mut self, inner:&fn()) { - self.ns_start = precise_time_ns(); - let k = self.iterations; - for u64::range(0, k) |_| { - inner(); - } - self.ns_end = precise_time_ns(); +impl BenchHarness { + /// Callback for benchmark functions to run in their body. + pub fn iter(&mut self, inner:&fn()) { + self.ns_start = precise_time_ns(); + let k = self.iterations; + for u64::range(0, k) |_| { + inner(); } + self.ns_end = precise_time_ns(); + } - pub fn ns_elapsed(&mut self) -> u64 { - if self.ns_start == 0 || self.ns_end == 0 { - 0 - } else { - self.ns_end - self.ns_start - } + pub fn ns_elapsed(&mut self) -> u64 { + if self.ns_start == 0 || self.ns_end == 0 { + 0 + } else { + self.ns_end - self.ns_start } + } - pub fn ns_per_iter(&mut self) -> u64 { - if self.iterations == 0 { - 0 - } else { - self.ns_elapsed() / self.iterations - } + pub fn ns_per_iter(&mut self) -> u64 { + if self.iterations == 0 { + 0 + } else { + self.ns_elapsed() / self.iterations } + } - pub fn bench_n(&mut self, n: u64, f: &fn(&mut BenchHarness)) { - self.iterations = n; - debug!("running benchmark for %u iterations", - n as uint); - f(self); - } + pub fn bench_n(&mut self, n: u64, f: &fn(&mut BenchHarness)) { + self.iterations = n; + debug!("running benchmark for %u iterations", + n as uint); + f(self); + } - // This is the Go benchmark algorithm. It produces a single - // datapoint and always tries to run for 1s. - pub fn go_bench(&mut self, f: &fn(&mut BenchHarness)) { - - // Rounds a number down to the nearest power of 10. - fn round_down_10(n: u64) -> u64 { - let mut n = n; - let mut res = 1; - while n > 10 { - n = n / 10; - res *= 10; - } - res - } + // This is the Go benchmark algorithm. It produces a single + // datapoint and always tries to run for 1s. + pub fn go_bench(&mut self, f: &fn(&mut BenchHarness)) { - // Rounds x up to a number of the form [1eX, 2eX, 5eX]. - fn round_up(n: u64) -> u64 { - let base = round_down_10(n); - if n < (2 * base) { - 2 * base - } else if n < (5 * base) { - 5 * base - } else { - 10 * base - } + // Rounds a number down to the nearest power of 10. + fn round_down_10(n: u64) -> u64 { + let mut n = n; + let mut res = 1; + while n > 10 { + n = n / 10; + res *= 10; } + res + } - // Initial bench run to get ballpark figure. - let mut n = 1_u64; - self.bench_n(n, f); - - while n < 1_000_000_000 && - self.ns_elapsed() < 1_000_000_000 { - let last = n; - - // Try to estimate iter count for 1s falling back to 1bn - // iterations if first run took < 1ns. - if self.ns_per_iter() == 0 { - n = 1_000_000_000; - } else { - n = 1_000_000_000 / self.ns_per_iter(); - } - - n = u64::max(u64::min(n+n/2, 100*last), last+1); - n = round_up(n); - self.bench_n(n, f); + // Rounds x up to a number of the form [1eX, 2eX, 5eX]. + fn round_up(n: u64) -> u64 { + let base = round_down_10(n); + if n < (2 * base) { + 2 * base + } else if n < (5 * base) { + 5 * base + } else { + 10 * base } } - // This is a more statistics-driven benchmark algorithm. - // It stops as quickly as 50ms, so long as the statistical - // properties are satisfactory. If those properties are - // not met, it may run as long as the Go algorithm. - pub fn auto_bench(&mut self, f: &fn(&mut BenchHarness)) -> ~[f64] { - - let mut rng = rand::rng(); - let mut magnitude = 10; - let mut prev_madp = 0.0; + // Initial bench run to get ballpark figure. + let mut n = 1_u64; + self.bench_n(n, |x| f(x)); - loop { - let n_samples = rng.gen_uint_range(50, 60); - let n_iter = rng.gen_uint_range(magnitude, - magnitude * 2); + while n < 1_000_000_000 && + self.ns_elapsed() < 1_000_000_000 { + let last = n; - let samples = do vec::from_fn(n_samples) |_| { - self.bench_n(n_iter as u64, f); - self.ns_per_iter() as f64 - }; + // Try to estimate iter count for 1s falling back to 1bn + // iterations if first run took < 1ns. + if self.ns_per_iter() == 0 { + n = 1_000_000_000; + } else { + n = 1_000_000_000 / self.ns_per_iter(); + } - // Eliminate outliers - let med = samples.median(); - let mad = samples.median_abs_dev(); - let samples = do vec::filter(samples) |f| { - num::abs(*f - med) <= 3.0 * mad - }; + n = u64::max(u64::min(n+n/2, 100*last), last+1); + n = round_up(n); + self.bench_n(n, |x| f(x)); + } + } - debug!("%u samples, median %f, MAD=%f, %u survived filter", - n_samples, med as float, mad as float, - samples.len()); - - if samples.len() != 0 { - // If we have _any_ cluster of signal... - let curr_madp = samples.median_abs_dev_pct(); - if self.ns_elapsed() > 1_000_000 && - (curr_madp < 1.0 || - num::abs(curr_madp - prev_madp) < 0.1) { - return samples; - } - prev_madp = curr_madp; - - if n_iter > 20_000_000 || - self.ns_elapsed() > 20_000_000 { - return samples; - } + // This is a more statistics-driven benchmark algorithm. + // It stops as quickly as 50ms, so long as the statistical + // properties are satisfactory. If those properties are + // not met, it may run as long as the Go algorithm. + pub fn auto_bench(&mut self, f: &fn(&mut BenchHarness)) -> ~[f64] { + + let mut rng = rand::rng(); + let mut magnitude = 10; + let mut prev_madp = 0.0; + + loop { + let n_samples = rng.gen_uint_range(50, 60); + let n_iter = rng.gen_uint_range(magnitude, + magnitude * 2); + + let samples = do vec::from_fn(n_samples) |_| { + self.bench_n(n_iter as u64, |x| f(x)); + self.ns_per_iter() as f64 + }; + + // Eliminate outliers + let med = samples.median(); + let mad = samples.median_abs_dev(); + let samples = do vec::filter(samples) |f| { + num::abs(*f - med) <= 3.0 * mad + }; + + debug!("%u samples, median %f, MAD=%f, %u survived filter", + n_samples, med as float, mad as float, + samples.len()); + + if samples.len() != 0 { + // If we have _any_ cluster of signal... + let curr_madp = samples.median_abs_dev_pct(); + if self.ns_elapsed() > 1_000_000 && + (curr_madp < 1.0 || + num::abs(curr_madp - prev_madp) < 0.1) { + return samples; } + prev_madp = curr_madp; - magnitude *= 2; + if n_iter > 20_000_000 || + self.ns_elapsed() > 20_000_000 { + return samples; + } } + + magnitude *= 2; } } +} + +pub mod bench { + use test::{BenchHarness, BenchSamples}; pub fn benchmark(f: &fn(&mut BenchHarness)) -> BenchSamples { |