It's a heterogeneous multiGPU level-3 BLAS library. This is an alternative to cuBLAS-XT, a commercial licensed multiGPU tiled BLAS. However, BLASX deliveries at least 25% more performance and 200% less communication volume. For detailed information, please refer to our ICS paper @ http://arxiv.org/abs/1510.05041 or http://dl.acm.org/citation.cfm?id=2926256

For installation, please change the make.inc. Basically, the following varaiables need to be updated:

• 1. LIBCPUBLAS: please change it to the location of CPU BLAS. OpenBLAS is highly recommended. If you're using Linux, the library extension should be '.so'. I give an OSX verison in the repository make.inc, which follows the dylib extension.
• 2. LIBGPUBLAS: please change it to the location of CUDA on your machine

Once you have configured these two variables, then you're good to go. Simply Make and the library is built in the lib folder.

To use BLASX:

• 1. export LD_LIBRARY_PATH to the location of BLASX lib.
• 2. you need to link cuBLAS when use BLASX. There is an example of linkage in the testing folder.

• 1. open a command line, export BLAS_VERSION=/path/to/BLASX
• 2. init MATLAB from command line, say typing matlab
• 3. that's it, enjoy!!

Please cite our paper@

@inproceedings{wang2016blasx, title={BLASX: A High Performance Level-3 BLAS Library for Heterogeneous Multi-GPU Computing}, author={Wang, Linnan and Wu, Wei and Xu, Zenglin and Xiao, Jianxiong and Yang, Yi}, booktitle={Proceedings of the 2016 International Conference on Supercomputing}, pages={20}, year={2016}, organization={ACM} }

Linnan

---ZGEMM routine courtesy to Jan Winkelmann and Paul Springer