Assignment: basic linear algebra operations

Download this crate for the second homework assignment. It contains some tests, benchmarks, and function prototypes. Since we are still covering basic Rust concepts, we still focus on small and simple functions.

1 Dot product

1.1 Implement dot

The dot product is used extensively in machine learning and is defined as:

Implement the dot product in src/dot.rs. The following function signature is given:

pub fn dot(u: &[f32], v: &[f32]) -> f32;

There are several ways to implement the dot product in Rust. For this assignment, try to stick to the C implementation in the first slide deck as closely as possible.

You can test your implementation with cargo test dot_test. There is also a benchmark that will compute the dot product of two random vectors of 500 components. You can run the benchmark with rustup run nightly cargo bench dot_bench.

2 Element-wise product

The element-wise product (also called Hadamard product for matrices) computes a new vector consisting of the pairwise multiplications of two vectors. That is, the elementwise product is computed as:

2.1 Implement the element-wise product

Implement the element-wise product in src/mul.rs as a function with the signature

pub fn mul(u: &[f32], v: &[f32]) -> Vec<f32>

which:

There is a simple test for the implementation that you can run with cargo test mul_test.

2.2 Implement an in-place version of the element-wise product

Implement the element-wise product in src/mul.rs as a function with the signature

pub fn mul_inplace(u: &mut [f32], v: &[f32])

This function should not return a new vector, but instead update , so that it contains the element-wise product after calling this function.

There is a simple test for the implementation that you can run with cargo test mul_inplace_test.

2.3 Compare the running times of the implementation

A benchmark is provided for mul and mul_inplace. Run cargo bench to compare the running times for the implementations. Which implementation is faster? Try to explain the result.

Submission

The tar.gz or zip file should be uploaded through this page. The deadline is May 3 at 13:00.