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Today’s high computational demands from engineering fields and complex hardware development make it necessary to develop and optimize new algorithms toward achieving high performance and good scalability on the next generation of computers. The enormous gap between the high-performance capabilities of GPUs and the slow interconnect between them has made the… (More)

The adoption of hybrid CPU–GPU nodes in traditional supercomputing platforms such as the Cray-XK6 opens acceleration opportunities for electronic structure calculations in materials science and chemistry applications, where mediumsized generalized eigenvalue problems must be solved many times. These eigenvalue problems are too small to effectively solve on… (More)

- Ichitaro Yamazaki, Tingxing Dong, Raffaele Solcà, Stanimire Tomov, Jack J. Dongarra, Thomas C. Schulthess
- Concurrency and Computation: Practice and…
- 2014

For software to fully exploit the computing power of emerging heterogeneous computers, not only must the required computational kernels be optimized for the specific hardware architectures but also an effective scheduling scheme is needed to utilize the available heterogeneous computational units and to hide the communication between them. As a case study,… (More)

We present a new quantum cluster algorithm to simulate models of high-Tc superconductors. This algorithm extends current methods with continuous lattice self-energies, thereby removing artificial long-range correlations. This cures the fermionic sign problem in the underlying quantum Monte Carlo solver for large clusters and realistic values of the Coulomb… (More)

The adoption of hybrid GPU-CPU nodes in traditional supercomputing platforms opens acceleration opportunities for electronic structure calculations in materials science and chemistry applications, where medium sized Hermitian generalized eigenvalue problems must be solved many times. The small size of the problems limits the scalability on a distributed… (More)

- William Sawyer, Anton Kozhevnikov, Raffaele Solcà
- PARCO
- 2015

- A. Haidar, S. Tomov, +4 authors J. Dongarra
- 2012

The Matrix Algebra on GPU and Multicore Architectures (MAGMA) project aims to develop the next generation of LAPACK/ScaLAPACK-compliant linear algebra libraries for heterogeneous multicore-GPU architectures. The functionality covered in LAPACK and ScaLAPACK is a fundamental building block for many scientific computing applications, underlining the… (More)

- Azzam Haidar, Stanimire Tomov, Jack J. Dongarra, Raffaele Solcà, Thomas C. Schulthess
- 2012 SC Companion: High Performance Computing…
- 2012

The adoption of hybrid GPU-CPU nodes in traditional supercomputing platforms such as the Cray-XK6 opens acceleration opportunities for electronic structure calculations in materials science and chemistry applications, where medium-sized generalized eigenvalue problems must be solved many times. These eigenvalue problems are too small to effectively solve on… (More)

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… (More)

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