added single threaded sparse vector impl

sparse_vector/Cargo.toml

@@ -7,4 +7,7 @@ edition = "2021"
 
 [dependencies]
 rand = "0.8.5"
-futures = "0.3.28"
+futures = "0.3.28"
+jemalloc-ctl = "0.5.0"
+jemallocator = "0.5.0"
+bytesize = "1.2.0"

sparse_vector/rust-toolchain.toml

@@ -0,0 +1,2 @@
+[toolchain]
+channel = "nightly"

sparse_vector/src/main.rs

@@ -1,116 +1,98 @@
-use std::ops::{Add, Mul};
+use std::ops::{Add, Mul, Sub};
 use std::thread;
+use std::time::Instant;
+use bytesize::ByteSize;
 use futures::executor::block_on;
 use rand::Rng;
 use futures::future::{join_all};
+use jemalloc_ctl::{stats, epoch};
+
+#[global_allocator]
+static ALLOC: jemallocator::Jemalloc = jemallocator::Jemalloc;
 
 /// Only stores more efficiently when at least 50% of all elements are zeros
 pub struct SparseVec {
-    column: Vec<(usize, f32)>
+    values: Vec<f64>,
+    indices: Vec<usize>,
 }
 
 impl SparseVec {
 
-    pub fn dot(&self, other: &SparseVec) -> f32 {
+    pub fn dot(&self, other: &SparseVec) -> f64 {
+        let mut sum = 0.0;
 
-        let future = async move {
-            let divisions = 128;
+        for index in 0..other.indices.len() {
+            // exponential search for an element in the second vector to have the same index
+            sum += binary_search(self.indices[index], &other.indices, &other.values) * self.values[index];
+        }
 
-            let k = self.column.len() / divisions;
-
-            let mut futures = Vec::new();
-
-            for i in 0..divisions {
-                let off = i * k;
-                futures.push(dot_threaded(&self.column[off..(off + k)], &other.column[..]));
-            }
-
-            join_all(futures).await
-        };
-
-        let result = block_on(future);
-
-        block_on(async move {
-            let divisions = 16;
-
-            let k = result.len() / divisions;
-
-            let mut futures = Vec::new();
-
-            for i in 0..divisions {
-                let off = i * k;
-                futures.push(sum_async(&result[off..(off + k)]));
-            }
-
-            join_all(futures).await
-        }).iter().fold(0.0, |acc, x| acc + x)
+        sum
     }
 
-    pub fn new(elements: usize, null_prop: f32) -> Self {
-        let non_zero_elements = (elements as f32 * (1.0 - null_prop)) as usize;
+    pub fn new(elements: usize, non_null: f64) -> Self {
+        let non_zero_elements = (elements as f64 * non_null) as usize;
 
-        let mut column = Vec::with_capacity(non_zero_elements);
+        let heap_element_size = std::mem::size_of::<f64>() + std::mem::size_of::<usize>();
 
+        println!("Estimated size on heap: {}", ByteSize::b((non_zero_elements * heap_element_size) as u64));
+
+        println!("allocating...");
+        let mut values = Vec::with_capacity(non_zero_elements);
+        let mut indices = Vec::with_capacity(non_zero_elements);
+
+        println!("generating some data...");
         let mut rng = rand::thread_rng();
-        let mut last_idx = 0;
 
-        for _ in 0..non_zero_elements {
-            last_idx = rng.gen_range(last_idx..elements);
-            column.push((last_idx, rng.gen_range(0.001..1.0)))
+        for i in 0..non_zero_elements {
+            values.push(0.5);
+
+            let idx = i as f32 / non_zero_elements as f32 * (elements as f32 - 4.0) + rng.gen_range(0.0..3.0);
+            indices.push(idx as usize);
         }
 
         Self {
-            column
+            values,
+            indices
         }
     }
 }
 
-async fn sum_async(arr: &[f32]) -> f32 {
-    arr.iter().fold(0.0, |acc, x| acc + x)
-}
+fn binary_search(target: usize, indices: &[usize], values: &[f64]) -> f64 {
 
-async fn dot_threaded(a: &[(usize, f32)], b: &[(usize, f32)]) -> f32 {
-    let mut sum = 0.0;
-
-    for pair in a.iter() {
-
-        // exponential search for an element in the second vector to have the same index
-        let mut bound = 1;
-        loop {
-            if bound >= b.len() || b[bound].1 >= pair.1 {
-                break;
-            }
-
-            bound *= 2;
+    let mut range = 0..indices.len();
+    loop {
+        let mut median = (range.end - range.start) >> 1;
+        if median == 0 {
+            break;
         }
+        median += range.start;
 
-        let mut range = 0..bound;
-        loop {
-            let mut median = (range.end - range.start) / 2;
-            if median == 0 {
-                break;
-            }
-            median += range.start;
-
-            if b[median].0 == pair.0 {
-                sum += b[median].1 * pair.1;
-                break;
-            }
-
-            if b[median].0 > pair.0 {
-                range.end = median;
-            } else {
-                range.start = median;
-            }
+        if indices[median] == target {
+            return values[median];
+        } else if indices[median] > target {
+            range.end = median;
+        } else {
+            range.start = median;
         }
     }
 
-    sum
+    0.0
 }
 
 fn main() {
+    let now = Instant::now();
     // generate a sparse vector with 10^10 random elements
-    let vec = SparseVec::new(10_000_000_000, 0.99);
+    // but only with 2% of them being non-null
+    let vec = SparseVec::new(10_usize.pow(10), 0.02);
+    println!("Created sparse vector took: {}s", Instant::now().sub(now).as_secs_f32());
 
-    println!("{}", vec.dot(&vec));
+    println!("Sparse vector stack bytes: {} B", std::mem::size_of_val(&vec));
+
+    // many statistics are cached and only updated when the epoch is advanced.
+    epoch::advance().unwrap();
+    println!("Heap allocated bytes (total): {}", ByteSize::b(stats::allocated::read().unwrap() as u64));
+
+    let now = Instant::now();
+    vec.dot(&vec);
+    println!("Dot product took: {}s", Instant::now().sub(now).as_secs_f32());
 }