yavl

Introduction

yavl is a small vectorization library written for doing experiments and benchmarks to compare the efficiency of manual vectorized code and compiler auto-vectorized code.

Usage in a production project is not recommended.

Features

Or topics that these experiments are about, either done or to be done:

• Vector3/4 calculations
• Matrix3x3/4x4 calculations
• Pseudorandom number generation(PCG32)
• String manipulation
• ISPC version of previous topics

Results and conclusion will be updated if more work is done.

Results

Benchmark results

All the following results(measured in nanoseconds) are aquired on my Linux PC with a AMD CPU.

Vectors:

Op(1000 iterations)	vec3	vec3_unvectorized	vec4	vec4_unvectorized
addition	503	1665	511	1299
subtraction	505	1691	520	1316
division	722	1768	736	1357
shuffle	517	1388	525	2711
dot	1035	1776	1051	1606
sqrt	1024	3160	1043	4621
rsqrt	643	4650	653	7850

Matrix:

Op(1000 iterations)	mat3	mat3_unvectorized	mat4	mat4_unvectorized
multiplication(vec)	7482	2656	1723	2031
multiplication(mat)	4377	3164	3086	2500

Random number generation:

Op(1000 iterations)	pcg32	pcg32_unvectorized
generate 8 numbers	5283	9566

Assembly code comparison:

TODO...

Conclusions

1. Manual intrinsic vecterization do help in scenarios where we need to do homogenerous operations to a data pack, especially when we apply specialized hardware implemented functions like sqrt&rsqrt(with possibly one or two Newton-Raphson iterations to help improve precision).

2. Write a function applying each step to a data pack is better than apply function to each element of a data pack, just like the idea of SoA(Structure of Arrays) from data oriented programming. This could help compiler to better auto-vectorized your code even not using intrinsics manually.

3. Your intrinsic implementation can be slower than plain ordinary one.

   • Check the results for matrices. Vectorized matrix3x3 is way slower than unvecotrized implementation. I use a __m256 and a __m128 for three columns(to save some memory space) when AVX available. Turns out to be a performance penalty.

   • Each intrinsic function comes with a different cost. If you're not familiar enough with them(just like me), your implementation could be slower than the compiler auto-vectorized one. Check the matrices muliplication result.