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abstract: 'The solution of eigenproblems is often a key computational bottleneck that limits the tractable system size of numerical algorithms, among them electronic structure theory in chemistry and in condensed matter physics. Large eigenproblems can easily exceed the capacity of a single compute node, thus must be solved on distributed-memory parallel computers. We here present GPU-oriented optimizations of the ELPA two-stage tridiagonalization eigensolver (ELPA2). On top of its existing cuBLAS-based GPU offloading, we add a CUDA kernel to speed up the back-transformation of eigenvectors, which can be the computationally most expensive part of the two-stage tridiagonalization algorithm. We demonstrate the performance of this GPU-accelerated eigensolver by benchmark calculations on two hybrid CPU-GPU architectures, namely a compute cluster based on Intel Xeon Gold CPUs and NVIDIA Volta GPUs, and the Summit supercomputer based on IBM POWER9 CPUs and NVIDIA Volta GPUs. Consistent with previous benchmarks on CPU-only architectures, the GPU-accelerated two-stage solver exhibits a parallel performance superior to the one-stage solver.'
address:
- 'Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC 27708, USA'
- 'Molecular Sciences Software Institute, Blacksburg, VA 24060, USA'
- 'Max Planck Computing and Data Facility, Garching D-85747, Germany'
- 'Institute of Mathematics of 