README.md

Eigen linear algebra library with BLIS (BLAS) on CPUs: are there any performance benefits?

Introduction

Eigen is a C++ template-based linear algebra library. In order to use it, no compilation is requried: just get the latest version and specify include paths for your project. Eigen uses a number of optimization techniques to make things fast: parallelization with OpenMP, advanced vector extensions (AVX), smart expression templates and many others.

Linear algebra is an important component of many HPC applications (for instance, the process of training deep neural networks may end up multiplying matrices up to 90% of entire training time). There is a de-facto standard for low-level vector-matrix operations called BLAS. Usually, libraries implementing BLAS interface are highly optimized for various types of hardware and need to be compiled and built on target machines.

Eigen historically supported only Intel's MKL library to delegate some of its computations to a highly optimzied ans specialzied for a particular hardware code. However, starting from version 3.3 Eigen can delegate these computations to any library implementing BLAS API (see this page for details)).

There are many libraries providing BLAS interface. From my experience, I found BLIS to be very convinient and fast. I wanted to see if there would be any performance benefits from integrating Eigen and BLAS library in terms of simple test - single general matrix matrix multiplication (xgemm).

Using Eigen with BLAS library

In order to take advantage of a hardware-optimized linear algebra library, Eigen requires that a macro EIGEN_USE_BLAS is defined before including Eigen headers

#define EIGEN_USE_BLAS
#include "Eigen/Dense"

and a program is linked against corresponding library. That's basically all!

Running examples in this project

Get this project and create initial configuration (required to compile BLIS):

git clone https://github.com/sergey-serebryakov/eigen-blis.git
cd ./eigen-blis
cp ./config.mk.template ./config.mk

Previous versions of BLIS require that a CPU configuration is provided (go here to see which configurations are available). Edit the BLIS_CONFNAME variable in config .mk file to specify a desired configuration. It is very important to select correct configuration at this step. Once configuration is specified, it is time to compile BLIS and get Eigen (before this step you may need to adjust your environmental settings (proxy settings) or install additional dependencies (mercurial - to get Eigen)):

cd ./third_party
make eigen
make blis
cd ..

Compile and build the project:

make

As a result, there will be three executable files in ./build directory:

• blis: BLIS-based matrix multiplication
• eigen: Eigen-based matrix multiplication
• eigen_blis: Eigen + BLIS-based matrix multiplication

Before running examples, an environment must be properly set. To configure Eigen, you need to figure out the number of physical cores your CPU has (do not take into accout Hyper-Threading!). Then export OMP_NUM_THREADS variable (15 cores here is for an Intel's Ivy Bridge family server processor (E7-4890 v2)):

export OMP_NUM_THREADS=15

There are multiple ways how BLIS can be configured in multithreading mode. In most of my experiments, I configure only BLIS_JC_NT and BLIS_IC_NT variables (this example is for the same 15-core processor):

export BLIS_JC_NT=3
export BLIS_IC_NT=5

Once it is done, go to buid directory and run binaries. The programm will iterate over matrix dimensions in the range 200-10000 with step 200 and will multiply squared matrices several times to measure the average time and achieved GFLOPs. The experiment is written in such a way that a matrix multiplication expression will be translated by Eigen to exactly one sgemm call (C = A * B + C) (see Eigen documentation how to write efficient matrix expressions). The output is a two column table with first column being dimension and the second column being average GFLOPs.

Experimental results

In this section I provide several results I got for three different CPUs. In all experiments BLIS is version 0.1.8.

10-core E5-2660 v2 @ 2.2 (Ivy Bridge, theorethical peak is 352 GFLOPs for single precision numbers), GCC: 5.2.0, OS: Ubuntu 14.04, BLIS configured with Sandy Bridge kernel.

export OMP_NUM_THREADS=10
export BLIS_JC_NT=2
export BLIS_IC_NT=5

15-core E7-4890 v2 @ 2.8 (Ivy Bridge, theorethical peak is 672 GFLOPs for single precision numbers), GCC: 4.8.5, OS: Red Hat 7.2, BLIS configured with Sandy Bridge kernel.

export OMP_NUM_THREADS=15
export BLIS_JC_NT=3
export BLIS_IC_NT=5

18-core E7-8890 v3 @ 2.5 (Haswell, theorethical peak is 1440 GFLOPs for single precision numbers), GCC: 4.8.2, OS: Red Hat 7.0, BLIS configured with Haswell kernel.

export OMP_NUM_THREADS=18
export BLIS_JC_NT=3
export BLIS_IC_NT=6

TODO