@conference {886,
	title = {Efficient Implementation Of Quantum Materials Simulations On Distributed CPU-GPU Systems},
	booktitle = {The International Conference for High Performance Computing, Networking, Storage and Analysis (SC15)},
	year = {2015},
	month = {2015-11},
	publisher = {ACM},
	organization = {ACM},
	address = {Austin, TX},
	abstract = {We present a scalable implementation of the Linearized Augmented Plane Wave method for distributed memory systems, which relies on an efficient distributed, block-cyclic setup of the Hamiltonian and overlap matrices and allows us to turn around highly accurate 1000+ atom all-electron quantum materials simulations on clusters with a few hundred nodes. The implementation runs efficiently on standard multicore CPU nodes, as well as hybrid CPU-GPU nodes. The key for the latter is a novel algorithm to solve the generalized eigenvalue problem for dense, complex Hermitian matrices on distributed hybrid CPU-GPU systems. Performance tests for Li-intercalated CoO2 supercells containing 1501 atoms demonstrate that high-accuracy, transferable quantum simulations can now be used in throughput materials search problems. While our application can benefit and get scalable performance through CPU-only libraries like ScaLAPACK or ELPA2, our new hybrid solver enables the efficient use of GPUs and shows that a hybrid CPU-GPU architecture scales to a desired performance using substantially fewer cluster nodes, and notably, is considerably more energy efficient than the traditional multicore CPU only systems for such complex applications.},
	author = {Raffaele Solc{\`a} and Anton Kozhevnikov and Azzam Haidar and Stanimire Tomov and Thomas C. Schulthess and Jack Dongarra}
}
