LIBXSMM Brings Deep-learning “Lessons Learned” to Many HPC Applications

Floating point performance on 16 nodes of the Knights Landing, Knights Mill and Skylake processor generation. Shown is a large set of EDGE’s possible configurations. All results are reported in terms of non-zero operations, contributing to EDGE’s solution. Dark gray bars represent the performance of single seismic forward simulations, while light gray bars show the performance of fused simulations.

The story “LIBXSMM Brings Deep-learning “Lessons Learned” to Many HPC Applications” by Rob Farber features EDGE as one of the first HPC applications, exploiting the Intel Xeon Phi processor for machine learning (Knights Mill). An important takeaway of the article is that only a comprehensive approach leads to optimal performance. Key factors for seismic wave propagation simulations in EDGE are: a) Extensive verification, including floating point precision as a modeling parameter, b) fused simulation technology for the exploitation of inter-simulation parallelism, and c) Just In Time (GIT) generated small sparse-matrix tensor kernels through the library LIBXSMM. The full story is available from the deep learning section of Medium. Further details are given in the slides of the IXPUG Middle East Conference 2018.