Use batched par iter for hecs and bevy

src/bevy/heavy_compute.rs

@@ -1,5 +1,6 @@
 use bevy_ecs::prelude::*;
 use cgmath::*;
+use rayon::prelude::*;
 
 #[derive(Copy, Clone)]
 struct Position(Vector3<f32>);
@@ -29,12 +30,18 @@ impl Benchmark {
     }
 
     pub fn run(&mut self) {
-        for (mut pos, mut mat) in self.0.query::<(&mut Position, &mut Matrix4<f32>)>().iter() {
-            for _ in 0..100 {
-                *mat = mat.invert().unwrap();
-            }
-
-            pos.0 = mat.transform_vector(pos.0);
-        }
+        self.0
+            .query::<(&mut Position, &mut Matrix4<f32>)>()
+            .iter_batched(64)
+            .par_bridge()
+            .for_each(|batch| {
+                for (mut pos, mut mat) in batch {
+                    for _ in 0..100 {
+                        *mat = mat.invert().unwrap();
+                    }
+
+                    pos.0 = mat.transform_vector(pos.0);
+                }
+            });
     }
 }

src/hecs/heavy_compute.rs

@@ -1,5 +1,6 @@
 use cgmath::*;
 use hecs::*;
+use rayon::prelude::*;
 
 #[derive(Copy, Clone)]
 struct Position(Vector3<f32>);
@@ -29,12 +30,18 @@ impl Benchmark {
     }
 
     pub fn run(&mut self) {
-        for (_, (mut pos, mat)) in self.0.query::<(&mut Position, &mut Matrix4<f32>)>().iter() {
-            for _ in 0..100 {
-                *mat = mat.invert().unwrap();
-            }
-
-            pos.0 = mat.transform_vector(pos.0);
-        }
+        self.0
+            .query::<(&mut Position, &mut Matrix4<f32>)>()
+            .iter_batched(64)
+            .par_bridge()
+            .for_each(|batch| {
+                for (_, (mut pos, mat)) in batch {
+                    for _ in 0..100 {
+                        *mat = mat.invert().unwrap();
+                    }
+
+                    pos.0 = mat.transform_vector(pos.0);
+                }
+            });
     }
 }

target/criterion/add_remove_component/specs/change/estimates.json

@@ -0,0 +1 @@
+{"mean":{"confidence_interval":{"confidence_level":0.95,"lower_bound":0.006564487351130271,"upper_bound":0.02767288517482775},"point_estimate":0.017190409920002203,"standard_error":0.005381268140474378},"median":{"confidence_interval":{"confidence_level":0.95,"lower_bound":0.0003467654342424896,"upper_bound":0.020996798836576087},"point_estimate":0.008700507276720337,"standard_error":0.005369756076431017}}