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PRODID:-//Microsoft Corporation//Outlook MIMEDIR//EN
VERSION:1.0
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DTSTART:20151117T231500Z
DTEND:20151118T010000Z
LOCATION:Level 4 - Lobby
DESCRIPTION;ENCODING=QUOTED-PRINTABLE:ABSTRACT: In quantum physics it is often required to determine spectral properties of large, sparse matrices.=0AFor instance, an approximation to the full spectrum or a number of inner eigenvalues can be computed with algorithms based on the evaluation of Chebyshev polynomials.=0AWe identify relevant bottlenecks of this class of algorithms and develop a reformulated version to increase the computational intensity and obtain a potentially higher efficiency, basically by employing kernel fusion and vector blocking.=0AThe optimized algorithm requires a manual implementation of compute kernels.=0AGuided by a performance model, we show the capabilities of our fully heterogeneous implementation on a petascale system. =0ABased on MPI+OpenMP/CUDA, our approach utilizes all parts of a heterogeneous CPU+GPU system with high efficiency.=0AFinally, our scaling study on up to 4096 heterogeneous nodes reveals a performance of half a petaflop/s, which corresponds to 11% of LINPACK performance for an originally bandwidth-bound sparse linear algebra problem.
SUMMARY:Efficient Large-Scale Sparse Eigenvalue Computations on Heterogeneous Hardware
PRIORITY:3
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