olca-mkl

This is an experimental project that links the Intel Math Kernel (MKL) as calculation library for openLCA. The chart below shows a performance comparison with UMFPACK for fully solving A * x = b for an ecoinvent 3.9 matrix including JNI overhead for communicating between JVM and native memory:

Building

The build scripts are written in Dart so that you need to have a current version of Dart (3.x) installed as well as the archive library. To install the library, run:

dart pub add archive

On macOS with Apple M1/M2, one will need to set the Rust toolchain to stable-x86_64-apple-darwin:

rustup install stable-x86_64-apple-darwin
rustup default stable-x86_64-apple-darwin

Then to build:

cd olca-mkl
dart build.dart

This will download the MKL Python package and its dependencies from pypi.org and extract the native libraries into the bin folder. It then compiles the bindings for the Java Native Interface (JNI) which are written in Rust against these libraries. (On Windows, it also generates a lib-file with the exported symbols for the linker first). This should then generate a (lib)olcamkl.* library in the bin folder.

The Java part has an MKL class with the native method-bindings and methods for loading the libraries from a folder. The method MKL.loadFrom(DIR) will load the libraries from the folder {DIR}/olca-mkl-x64_v{VERSION}. The current version is 1 (the next version would be 2, then 3 etc.) and it is not the version of the MKL but the version of the openLCA JNI bindings for the MKL. The method MKL.loadFromDefault() will try to load the libraries from the openLCA default workspace ~/openLCA-data-1.4/olca-mkl-x64_v{VERSION}. With the methods MKL.isLibraryDir and MKL.isDefaultLibraryDir you can also test if a directory contains the MKL library folder with the required libraries (useful for the integration in openLCA later).

Note that there is only support for x64 CPUs, e.g. on macOS M1/2, you need to run a x64 JVM using the compatibility layer.