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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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80
src/histogram.rs
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@@ -1,5 +1,4 @@
use std::fmt;
use std::cell::RefCell;
// One histogram
#[derive(Debug,Clone)]
@@ -54,7 +53,7 @@ pub struct Dataset {
bias_fc: Vec<f64>,
// bias value cache
bias: RefCell<Vec<Option<f64>>>,
bias: Vec<f64>,
// histogram weight
pub weights: Vec<f64>,
@@ -64,9 +63,9 @@ impl Dataset {
pub fn new(num_bins: usize, dimens_lengths: Vec<usize>, bin_width: Vec<f64>, hist_min: Vec<f64>, hist_max: Vec<f64>, bias_pos: Vec<f64>, bias_fc: Vec<f64>, kT: f64, histograms: Vec<Histogram>, cyclic: bool) -> Dataset {
let num_windows = histograms.len();
let bias: RefCell<Vec<Option<f64>>> = RefCell::new(vec![None; num_bins*num_windows]);
let bias: Vec<f64> = vec![0.0; num_bins*num_windows];
let weights = vec![1.0; num_windows];
Dataset{
let mut ds = Dataset{
num_windows,
num_bins,
dimens_lengths,
@@ -80,7 +79,15 @@ impl Dataset {
bias_fc,
bias,
weights
};
for window in 0..num_windows {
for bin in 0..num_bins {
let ndx = window * num_bins + bin;
ds.bias[ndx] = ds.calc_bias(bin, window);
}
}
ds
}
pub fn new_weighted(ds: Dataset, weights: Vec<f64>) -> Dataset {
@@ -113,45 +120,40 @@ impl Dataset {
}).collect()
}
pub fn get_bias(&self, bin: usize, window: usize) -> f64 {
let ndx = window * self.num_bins + bin;
self.bias[ndx]
}
// Harmonic bias calculation: bias = 0.5*k(dx)^2
// if cyclic is true, lowest and highest bins are assumed to be
// neighbors. This returns exp(U/kT) instead of U for better performance.
pub fn calc_bias(&self, bin: usize, window: usize) -> f64 {
let ndx = window * self.num_bins + bin;
let mut cache = self.bias.borrow_mut();
match cache[ndx] {
Some(val) => val,
None => {
let dimens = self.dimens_lengths.len();
fn calc_bias(&self, bin: usize, window: usize) -> f64 {
let dimens = self.dimens_lengths.len();
// index of the bias value depends on the window und dimension
let bias_ndx: Vec<usize> = (0..dimens)
.map(|dimen| { window * dimens + dimen }).collect();
// index of the bias value depends on the window und dimension
let bias_ndx: Vec<usize> = (0..dimens)
.map(|dimen| { window * dimens + dimen }).collect();
// find the N coords, force constants and bias coords
let coord = self.get_coords_for_bin(bin);
let bias_fc: Vec<f64> = bias_ndx.iter().map(|ndx| { self.bias_fc[*ndx] }).collect();
let bias_pos: Vec<f64> = bias_ndx.iter().map(|ndx| { self.bias_pos[*ndx] }).collect();
// find the N coords, force constants and bias coords
let coord = self.get_coords_for_bin(bin);
let bias_fc: Vec<f64> = bias_ndx.iter().map(|ndx| { self.bias_fc[*ndx] }).collect();
let bias_pos: Vec<f64> = bias_ndx.iter().map(|ndx| { self.bias_pos[*ndx] }).collect();
let mut bias_sum = 0.0;
for i in 0..dimens {
let mut dist = (coord[i] - bias_pos[i]).abs();
if self.cyclic { // periodic conditions
let hist_len = self.hist_max[i] - self.hist_min[i];
if dist > 0.5 * hist_len {
dist -= hist_len;
}
}
// store exp(U/kT) for better performance
bias_sum += 0.5 * bias_fc[i] * dist * dist
let mut bias_sum = 0.0;
for i in 0..dimens {
let mut dist = (coord[i] - bias_pos[i]).abs();
if self.cyclic { // periodic conditions
let hist_len = self.hist_max[i] - self.hist_min[i];
if dist > 0.5 * hist_len {
dist -= hist_len;
}
let bias_sum = (-bias_sum/self.kT).exp();
cache[ndx] = Some(bias_sum);
bias_sum
}
// store exp(U/kT) for better performance
bias_sum += 0.5 * bias_fc[i] * dist * dist
}
let bias_sum = (-bias_sum/self.kT).exp();
bias_sum
}
}
impl fmt::Display for Dataset {
@@ -247,11 +249,11 @@ mod tests {
let ds = Dataset::new(
5, // num bins
vec![1],
vec![1.0], // bin width
vec![0.0], // hist min
vec![9.0], // hist max
vec![7.5], // x0
vec![10.0], // fc
vec![1.0, 1.0], // bin width
vec![0.0, 0.0], // hist min
vec![5.0, 5.0], // hist max
vec![7.5, 7.5], // x0
vec![10.0, 10.0], // fc
300.0*k_B, // kT
vec![build_hist(), build_hist()], // hists
false // cyclic