implemented proper linear loop for dot product

sparse_vector/Cargo.toml

@@ -11,3 +11,6 @@ futures = "0.3.28"
 jemalloc-ctl = "0.5.0"
 jemallocator = "0.5.0"
 bytesize = "1.2.0"
+
+[features]
+binary_search = []

sparse_vector/src/main.rs

@@ -1,7 +1,7 @@
-use std::time::Instant;
 use bytesize::ByteSize;
+use jemalloc_ctl::{epoch, stats};
 use rand::Rng;
-use jemalloc_ctl::{stats, epoch};
+use std::time::Instant;
 
 #[global_allocator]
 static ALLOC: jemallocator::Jemalloc = jemallocator::Jemalloc;
@@ -13,13 +13,35 @@ pub struct SparseVec {
 }
 
 impl SparseVec {
-
     pub fn dot(&self, other: &SparseVec) -> f64 {
         let mut sum = 0.0;
 
+        #[cfg(not(feature="binary_search"))]
+        {
+            let mut x = 0;
+            let mut y = 0;
+
+            while x < self.indices.len() && y < other.indices.len() {
+                if self.indices[x] == other.indices[y] {
+                    sum += self.values[x] * other.values[y];
+
+                    x += 1;
+                    y += 1;
+                } else if self.indices[x] > other.indices[y] {
+                    y += 1;
+                } else {
+                    x += 1;
+                }
+            }
+        }
+
+        #[cfg(feature="binary_search")]
+        {
             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];
+                // binary 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];
+            }
         }
 
         sum
@@ -36,14 +58,12 @@ impl SparseVec {
         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);
+            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 {
-            values,
-            indices
-        }
+        Self { values, indices }
     }
 }
 
@@ -76,11 +96,10 @@ macro_rules! time {
         let start = Instant::now();
         $block;
         println!("{} took {}s", $name, start.elapsed().as_secs_f64());
-    }}
+    }};
 }
 
 fn main() {
-
     /// Theoretical size of the vector in elements
     /// This would mean the we would require 10 GBs of memory to store a single vector
     const VECTOR_SIZE: usize = 10_000_000_000;
@@ -92,7 +111,10 @@ fn main() {
     let non_zero_elements = (VECTOR_SIZE as f64 * NULL_NON_NULL_RATIO) as usize;
     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!(
+        "Estimated size on heap: {}",
+        ByteSize::b((non_zero_elements * heap_element_size) as u64)
+    );
     println!("Size on stack: {} B", std::mem::size_of::<SparseVec>());
 
     let vec: SparseVec;
@@ -104,7 +126,10 @@ fn main() {
 
     // 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));
+    println!(
+        "Heap allocated bytes (total): {}",
+        ByteSize::b(stats::allocated::read().unwrap() as u64)
+    );
 
     time!("Sparse vector dot product", {
         vec.dot(&vec);