In this video from the GPU Technology Conference, Ole Schütt, Ph.D. Student at ETH Zürich presents: Accelerated Sparse Matrix Multiplication for Quantum Chemistry with CP2K on Hybrid Supercomputers.
Learn how we achieve great GPU performance with an auto-tuned sparse matrix multiplication library, enabling quantum simulation of millions of electrons. Our tool of choice is CP2K, a leading code in the field of electronic structure and simulation. Exploiting the locality and sparsity this code achieves a linear computational complexity for DFT, allowing for novel science. Massive parallelism over thousands of GPUs leads to excellent time to solution. The major computational kernel is block-sparse matrix matrix multiplication. We will discuss results and development insights, including GPU kernels and latency hiding node-parallel techniques. We propose sparse matrix multiplications as a powerful abstraction to formulate streaming algorithms in general.”