implemented proper linear loop for dot product
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@ -10,4 +10,7 @@ rand = "0.8.5"
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futures = "0.3.28"
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jemalloc-ctl = "0.5.0"
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jemallocator = "0.5.0"
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bytesize = "1.2.0"
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bytesize = "1.2.0"
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[features]
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binary_search = []
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@ -1,7 +1,7 @@
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use std::time::Instant;
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use bytesize::ByteSize;
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use jemalloc_ctl::{epoch, stats};
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use rand::Rng;
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use jemalloc_ctl::{stats, epoch};
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use std::time::Instant;
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#[global_allocator]
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static ALLOC: jemallocator::Jemalloc = jemallocator::Jemalloc;
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@ -13,13 +13,35 @@ pub struct SparseVec {
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}
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impl SparseVec {
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pub fn dot(&self, other: &SparseVec) -> f64 {
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let mut sum = 0.0;
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for index in 0..other.indices.len() {
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// exponential search for an element in the second vector to have the same index
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sum += binary_search(self.indices[index], &other.indices, &other.values) * self.values[index];
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#[cfg(not(feature="binary_search"))]
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{
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let mut x = 0;
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let mut y = 0;
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while x < self.indices.len() && y < other.indices.len() {
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if self.indices[x] == other.indices[y] {
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sum += self.values[x] * other.values[y];
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x += 1;
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y += 1;
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} else if self.indices[x] > other.indices[y] {
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y += 1;
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} else {
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x += 1;
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}
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}
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}
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#[cfg(feature="binary_search")]
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{
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for index in 0..other.indices.len() {
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// binary search for an element in the second vector to have the same index
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sum += binary_search(self.indices[index], &other.indices, &other.values)
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* self.values[index];
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}
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}
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sum
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@ -36,14 +58,12 @@ impl SparseVec {
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for i in 0..non_zero_elements {
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values.push(0.5);
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let idx = i as f32 / non_zero_elements as f32 * (elements as f32 - 4.0) + rng.gen_range(0.0..3.0);
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let idx = i as f32 / non_zero_elements as f32 * (elements as f32 - 4.0)
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+ rng.gen_range(0.0..3.0);
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indices.push(idx as usize);
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}
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Self {
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values,
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indices
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}
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Self { values, indices }
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}
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}
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@ -76,11 +96,10 @@ macro_rules! time {
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let start = Instant::now();
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$block;
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println!("{} took {}s", $name, start.elapsed().as_secs_f64());
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}}
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}};
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}
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fn main() {
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/// Theoretical size of the vector in elements
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/// This would mean the we would require 10 GBs of memory to store a single vector
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const VECTOR_SIZE: usize = 10_000_000_000;
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@ -92,7 +111,10 @@ fn main() {
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let non_zero_elements = (VECTOR_SIZE as f64 * NULL_NON_NULL_RATIO) as usize;
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let heap_element_size = std::mem::size_of::<f64>() + std::mem::size_of::<usize>();
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println!("Estimated size on heap: {}", ByteSize::b((non_zero_elements * heap_element_size) as u64));
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println!(
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"Estimated size on heap: {}",
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ByteSize::b((non_zero_elements * heap_element_size) as u64)
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);
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println!("Size on stack: {} B", std::mem::size_of::<SparseVec>());
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let vec: SparseVec;
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@ -104,9 +126,12 @@ fn main() {
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// many statistics are cached and only updated when the epoch is advanced.
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epoch::advance().unwrap();
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println!("Heap allocated bytes (total): {}", ByteSize::b(stats::allocated::read().unwrap() as u64));
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println!(
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"Heap allocated bytes (total): {}",
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ByteSize::b(stats::allocated::read().unwrap() as u64)
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);
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time!("Sparse vector dot product", {
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vec.dot(&vec);
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});
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}
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}
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