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Multithreading

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Daniel Bauer
2018-11-18 12:04:27 +01:00
parent 56b6cb247a
commit 6ed0e4c49c
6 changed files with 165 additions and 54 deletions
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24
src/lib.rs
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@@ -4,6 +4,7 @@
extern crate error_chain;
extern crate rand;
extern crate rgsl;
extern crate rayon;
pub mod io;
pub mod histogram;
@@ -13,6 +14,7 @@ use histogram::Dataset;
use std::f64;
use std::fmt;
use std::io::prelude::*;
use rayon::prelude::*;
// init error chain
pub mod errors { error_chain!{} }
@@ -57,10 +59,10 @@ fn is_converged(old_F: &[f64], new_F: &[f64], tolerance: f64) -> bool {
// estimate the probability of a bin of the histogram set based on given bias offsets (F)
// This evaluates the first WHAM equation for each bin.
fn calc_bin_probability(bin: usize, dataset: &Dataset, F: &[f64]) -> f64 {
let mut denom_sum: f64 = 0.0;
let mut denom_sum: f64 = 0.0;
let bin_count: f64 = dataset.get_weighted_bin_count(bin);
for (window, h) in dataset.histograms.iter().enumerate() {
let bias = dataset.calc_bias(bin, window);
let bias = dataset.get_bias(bin, window);
denom_sum += (dataset.weights[window] * h.num_points as f64) * bias * F[window];
}
bin_count / denom_sum
@@ -71,7 +73,7 @@ fn calc_bin_probability(bin: usize, dataset: &Dataset, F: &[f64]) -> f64 {
fn calc_window_F(window: usize, dataset: &Dataset, P: &[f64]) -> f64 {
let f: f64 = (0..dataset.num_bins).zip(P.iter()) // zip bins and P
.map(|bin_and_prob: (usize, &f64)| {
let bias = dataset.calc_bias(bin_and_prob.0, window);
let bias = dataset.get_bias(bin_and_prob.0, window);
bin_and_prob.1 * bias
}).sum();
1.0/f
@@ -80,18 +82,18 @@ fn calc_window_F(window: usize, dataset: &Dataset, P: &[f64]) -> f64 {
// One full WHAM iteration includes calculation of new probabilities P and
// new bias offsets F based on previous bias offsets F_prev. This updates
// the values in vectors F and P
fn perform_wham_iteration(dataset: &Dataset, F_prev: &[f64], F: &mut [f64], P: &mut [f64]) {
fn perform_wham_iteration(dataset: &Dataset, F_prev: &[f64], F: &mut Vec<f64>, P: &mut Vec<f64>) {
// evaluate first WHAM equation for each bin to
// estimage probabilities based on previous offsets (F_prev)
for bin in 0..dataset.num_bins {
P[bin] = calc_bin_probability(bin, dataset, F_prev);
}
// estimage probabilities based on previous offsets (F_prev))
(0..dataset.num_bins).into_par_iter()
.map(|bin| { calc_bin_probability(bin, dataset, F_prev) })
.collect_into_vec(P);
// evaluate second WHAM equation for each window to
// estimate new bias offsets from propabilities
for window in 0..dataset.num_windows {
F[window] = calc_window_F(window, dataset, P);
}
(0..dataset.num_windows).into_par_iter()
.map(|window| {calc_window_F(window, dataset, P)} )
.collect_into_vec(F);
}
pub fn perform_wham(cfg: &Config, dataset: &Dataset) -> Result<(Vec<f64>, Vec<f64>, Vec<f64>)> {