Annual Report 2010 - Fachgruppe Informatik an der RWTH Aachen ...

Research
Cloud Computing
The cloud computing model provides flexible support for “pay as you go” systems. In
addition to no upfront investment in large clusters or supercomputers, such systems incur no
maintenance costs. Furthermore, they can be expanded and reduced on-demand in real-time.
Total cost can be close to zero when resources are not in use. The cloud user can pay costs
directly proportional to need rather than allocating resources according to average or peak
load. Our research explores whether cloud computing services are suitable for highperformance
computing (HPC) workloads. To this end, in addition to traditional metrics such
as GFLOPS and efficiency, we introduce and study concepts like average expected
performance and execution time, expected cost to completion, flops per dollars.
Eigencomputations in Physics
Density Functional Theory (DFT) [1] is a powerful method of investigation that has become
the “standard model” of material science. DFT is one of the most effective frameworks for
studying complex quantum mechanical systems. DFT-based methods are growing as the
standard tools for simulating new materials. At the core of DFT lie a large number of
generalized eigenproblems, to be solved at each iteration of an iterative process. Our goal is to
take advantage of physical knowledge to speed up the computation of both each individual
eigenproblem, and of the whole sequence.
Parallel Eigensolvers
The computation of eigenvalues and eigenvectors of symmetric tridiagonal matrices is most
common in applications, and it is one the steps in the solution of Hermitian and symmetric
eigenproblems. While several accurate and efficient methods for the triadiagonal
eigenproblem exist, the state of the art libraries only target uniprocessors or large distributed
memory systems. We are developing new eigensolvers specifically designed for todays
architectures, ranging from multi-core and many-core, to GPUs, to hybrid systems. Our target
is to establish the reference parallel library for eigencomputations.
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