titaniumtown/physarum
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remove gpu compute stuff (didn't work at all, might revisit later)

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This commit is contained in:
Simon Gardling 2021-03-29 12:27:49 +00:00
parent d36bec61f4
commit e07aa5ca0a
3 changed files with 2 additions and 195 deletions
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2
Cargo.toml
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@ -12,8 +12,6 @@ itertools = "0.10"
rand = "0.8.3" rand = "0.8.3"
rand_distr = "0.4" rand_distr = "0.4"
rayon = "1.5" rayon = "1.5"
arrayfire = {git = "https://github.com/arrayfire/arrayfire-rust.git"}
#arrayfire = "3.8.0"
[dev-dependencies] [dev-dependencies]
criterion = "0.3" criterion = "0.3"

41
src/main.rs
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@ -2,17 +2,8 @@ use chrono::{DateTime, Utc};
use indicatif::{ProgressBar, ProgressStyle}; use indicatif::{ProgressBar, ProgressStyle};
use physarum::model; use physarum::model;
use rand::Rng; use rand::Rng;
use arrayfire as af;
fn main() { fn main() {
let gpu_compute: bool = false;
if gpu_compute {
backend_man();
// af::set_backend(af::Backend::CPU);
af::set_device(0);
af::info();
}
// let n_iterations = 16384; // let n_iterations = 16384;
let n_iterations = 1024; let n_iterations = 1024;
// let n_iterations = 100; // let n_iterations = 100;
@ -38,11 +29,7 @@ fn main() {
let mut model = model::Model::new(width, height, n_particles, n_populations, diffusivity); let mut model = model::Model::new(width, height, n_particles, n_populations, diffusivity);
model.print_configurations(); model.print_configurations();
if gpu_compute {
model.run_cl(n_iterations);
} else {
model.run(n_iterations); model.run(n_iterations);
}
println!("Rendering all saved image data...."); println!("Rendering all saved image data....");
@ -50,31 +37,3 @@ fn main() {
model.flush_image_data(); model.flush_image_data();
println!("Done!"); println!("Done!");
} }
fn backend_man() {
let available = af::get_available_backends();
if available.contains(&af::Backend::CUDA) {
println!("Evaluating CUDA Backend...");
af::set_backend(af::Backend::CUDA);
println!("There are {} CUDA compute devices", af::device_count());
return;
}
/*
if available.contains(&af::Backend::OPENCL) {
println!("Evaluating OpenCL Backend...");
af::set_backend(af::Backend::OPENCL);
println!("There are {} OpenCL compute devices", af::device_count());
return;
}
*/
if available.contains(&af::Backend::CPU) {
println!("Evaluating CPU Backend...");
af::set_backend(af::Backend::CPU);
println!("There are {} CPU compute devices", af::device_count());
return;
}
}

150
src/model.rs
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@ -12,7 +12,6 @@ use std::f32::consts::TAU;
use std::time::{Duration, Instant}; use std::time::{Duration, Instant};
use rayon::iter::{ParallelIterator, IntoParallelIterator}; use rayon::iter::{ParallelIterator, IntoParallelIterator};
use indicatif::{ParallelProgressIterator, ProgressBar, ProgressStyle}; use indicatif::{ParallelProgressIterator, ProgressBar, ProgressStyle};
use arrayfire as af;
use std::path::Path; use std::path::Path;
/// A single Physarum agent. The x and y positions are continuous, hence we use floating point /// A single Physarum agent. The x and y positions are continuous, hence we use floating point
@ -250,155 +249,6 @@ impl Model {
pb.finish(); pb.finish();
} }
// Currently VERY poorly implemented (allocates memory each iteration)
// I need to learn more about gpu compute to tackle this one
pub fn run_cl(&mut self, steps: usize) {
let pb = ProgressBar::new(steps as u64);
pb.set_style(
ProgressStyle::default_bar()
.template(
"{spinner:.green} [{elapsed_precise}] [{bar:40.cyan/blue}] {pos}/{len} ({eta} {percent}%, {per_sec})",
)
.progress_chars("#>-"),
);
// Combine grids
let grids = &mut self.grids;
combine(grids, &self.attraction_table);
let agents_list = &*self.agents.clone();
let agent_num: usize = agents_list.len() as usize;
let dims = af::Dim4::new(&[agent_num as u64, 1, 1, 1]);
let mut sensor_distance_list: Vec<f32> = Vec::new();
let mut sensor_angle_list: Vec<f32> = Vec::new();
let mut rotation_angle_list: Vec<f32> = Vec::new();
let mut step_distance_list: Vec<f32> = Vec::new();
// Need to fix, super slow
for agent in &*self.agents.clone() {
let PopulationConfig {
sensor_distance,
sensor_angle,
rotation_angle,
step_distance,
..
} = &grids.clone()[agent.population_id].config;
sensor_distance_list.push(*sensor_distance);
sensor_angle_list.push(*sensor_angle);
rotation_angle_list.push(*rotation_angle);
step_distance_list.push(*step_distance);
}
let sensor_distance = af::Array::new(&sensor_distance_list, dims);
let sensor_angle = af::Array::new(&sensor_angle_list, dims);
let mut agent_angles_list: Vec<f32> = Vec::new();
let mut agent_x_list: Vec<f32> = Vec::new();
let mut agent_y_list: Vec<f32> = Vec::new();
for i in 0..steps {
let grids = &mut self.grids;
combine(grids, &self.attraction_table);
println!("Starting tick for all agents...");
let agents_tick_time = Instant::now();
agent_angles_list = agents_list.iter().map(|agent| agent.angle).collect();
agent_x_list = agents_list.iter().map(|agent| agent.x).collect();
agent_y_list = agents_list.iter().map(|agent| agent.y).collect();
let agent_x = af::Array::new(&agent_x_list, dims);
let agent_y = af::Array::new(&agent_y_list, dims);
let agent_angles = af::Array::new(&agent_angles_list, dims);
let cos_angles = af::cos(&agent_angles);
let sin_angles = af::sin(&agent_angles);
let cos_angle_dis = af::mul(&cos_angles, &sensor_distance, false);
let sin_angle_dis = af::mul(&sin_angles, &sensor_distance, false);
let xc_array = &af::add(&agent_x, &cos_angle_dis, false);
let yc_array = &af::add(&agent_y, &sin_angle_dis, false);
let xc = Self::to_vec(xc_array);
let yc = Self::to_vec(yc_array);
let agent_add_sens = af::add(&agent_angles, &sensor_angle, false);
let agent_sub_sens = af::sub(&agent_angles, &sensor_angle, false);
let agent_add_sens_mul = af::mul(&agent_add_sens, &sensor_distance, false);
let agent_sub_sens_mul = af::mul(&agent_sub_sens, &sensor_distance, false);
let xl_array = &af::add(&agent_x, &af::sin(&agent_sub_sens_mul), false);
let yl_array = &af::add(&agent_y, &af::sin(&agent_sub_sens_mul), false);
let xr_array = &af::add(&agent_x, &af::sin(&agent_add_sens_mul), false);
let yr_array = &af::add(&agent_y, &af::sin(&agent_add_sens_mul), false);
let xl = Self::to_vec(xl_array);
let yl = Self::to_vec(yl_array);
let xr = Self::to_vec(xr_array);
let yr = Self::to_vec(yr_array);
self.agents.par_iter_mut().for_each(|agent| {
let i: usize = agent.i;
let rotation_angle = rotation_angle_list[i];
let step_distance = rotation_angle_list[i];
let xc = xc[i];
let xl = xl[i];
let xr = xr[i];
let yc = yc[i];
let yl = yl[i];
let yr = yr[i];
let grid = &grids[agent.population_id];
let (width, height) = (grid.width, grid.height);
let trail_c = grid.get_buf(xc, yc);
let trail_l = grid.get_buf(xl, yl);
let trail_r = grid.get_buf(xr, yr);
let mut rng = rand::thread_rng();
let direction = Model::pick_direction(trail_c, trail_l, trail_r, &mut rng);
agent.rotate_and_move(direction, rotation_angle, step_distance, width, height);
});
let agents_tick_elapsed = agents_tick_time.elapsed().as_millis();
let ms_per_agent: f64 = (agents_tick_elapsed as f64) / (self.agents.len() as f64);
println!("Finished tick for all agents. took {}ms\nTime per agent: {}ms\n", agents_tick_time.elapsed().as_millis(), ms_per_agent);
// Deposit
for agent in self.agents.iter() {
self.grids[agent.population_id].deposit(agent.x, agent.y);
}
// Diffuse + Decay
let diffusivity = self.diffusivity;
self.grids.par_iter_mut().for_each(|grid| {
grid.diffuse(diffusivity);
});
self.save_image_data();
self.iteration += 1;
pb.set_position(i as u64);
}
pb.finish();
}
fn to_vec<T:af::HasAfEnum+Default+Clone>(array: &af::Array<T>) -> Vec<T> {
let mut vec = vec!(T::default();array.elements());
array.host(&mut vec);
return vec;
}
fn save_image_data(&mut self) { fn save_image_data(&mut self) {
let grids = self.grids.clone(); let grids = self.grids.clone();
self.img_data_vec.push(ImgData::new(grids, self.palette, self.iteration)); self.img_data_vec.push(ImgData::new(grids, self.palette, self.iteration));