329 research outputs found
Locality-aware parallel block-sparse matrix-matrix multiplication using the Chunks and Tasks programming model
We present a method for parallel block-sparse matrix-matrix multiplication on
distributed memory clusters. By using a quadtree matrix representation, data
locality is exploited without prior information about the matrix sparsity
pattern. A distributed quadtree matrix representation is straightforward to
implement due to our recent development of the Chunks and Tasks programming
model [Parallel Comput. 40, 328 (2014)]. The quadtree representation combined
with the Chunks and Tasks model leads to favorable weak and strong scaling of
the communication cost with the number of processes, as shown both
theoretically and in numerical experiments.
Matrices are represented by sparse quadtrees of chunk objects. The leaves in
the hierarchy are block-sparse submatrices. Sparsity is dynamically detected by
the matrix library and may occur at any level in the hierarchy and/or within
the submatrix leaves. In case graphics processing units (GPUs) are available,
both CPUs and GPUs are used for leaf-level multiplication work, thus making use
of the full computing capacity of each node.
The performance is evaluated for matrices with different sparsity structures,
including examples from electronic structure calculations. Compared to methods
that do not exploit data locality, our locality-aware approach reduces
communication significantly, achieving essentially constant communication per
node in weak scaling tests.Comment: 35 pages, 14 figure
Canonical density matrix perturbation theory
Density matrix perturbation theory [Niklasson and Challacombe, Phys. Rev.
Lett. 92, 193001 (2004)] is generalized to canonical (NVT) free energy
ensembles in tight-binding, Hartree-Fock or Kohn-Sham density functional
theory. The canonical density matrix perturbation theory can be used to
calculate temperature dependent response properties from the coupled perturbed
self-consistent field equations as in density functional perturbation theory.
The method is well suited to take advantage of sparse matrix algebra to achieve
linear scaling complexity in the computational cost as a function of system
size for sufficiently large non-metallic materials and metals at high
temperatures.Comment: 21 pages, 3 figure
Relativistic diffusion processes and random walk models
The nonrelativistic standard model for a continuous, one-parameter diffusion
process in position space is the Wiener process. As well-known, the Gaussian
transition probability density function (PDF) of this process is in conflict
with special relativity, as it permits particles to propagate faster than the
speed of light. A frequently considered alternative is provided by the
telegraph equation, whose solutions avoid superluminal propagation speeds but
suffer from singular (non-continuous) diffusion fronts on the light cone, which
are unlikely to exist for massive particles. It is therefore advisable to
explore other alternatives as well. In this paper, a generalized Wiener process
is proposed that is continuous, avoids superluminal propagation, and reduces to
the standard Wiener process in the non-relativistic limit. The corresponding
relativistic diffusion propagator is obtained directly from the nonrelativistic
Wiener propagator, by rewriting the latter in terms of an integral over
actions. The resulting relativistic process is non-Markovian, in accordance
with the known fact that nontrivial continuous, relativistic Markov processes
in position space cannot exist. Hence, the proposed process defines a
consistent relativistic diffusion model for massive particles and provides a
viable alternative to the solutions of the telegraph equation.Comment: v3: final, shortened version to appear in Phys. Rev.
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