8,649 research outputs found
Scalable and fast heterogeneous molecular simulation with predictive parallelization schemes
Multiscale and inhomogeneous molecular systems are challenging topics in the
field of molecular simulation. In particular, modeling biological systems in
the context of multiscale simulations and exploring material properties are
driving a permanent development of new simulation methods and optimization
algorithms. In computational terms, those methods require parallelization
schemes that make a productive use of computational resources for each
simulation and from its genesis. Here, we introduce the heterogeneous domain
decomposition approach which is a combination of an heterogeneity sensitive
spatial domain decomposition with an \textit{a priori} rearrangement of
subdomain-walls. Within this approach, the theoretical modeling and
scaling-laws for the force computation time are proposed and studied as a
function of the number of particles and the spatial resolution ratio. We also
show the new approach capabilities, by comparing it to both static domain
decomposition algorithms and dynamic load balancing schemes. Specifically, two
representative molecular systems have been simulated and compared to the
heterogeneous domain decomposition proposed in this work. These two systems
comprise an adaptive resolution simulation of a biomolecule solvated in water
and a phase separated binary Lennard-Jones fluid.Comment: 14 pages, 12 figure
Optimizing Distributed Tensor Contractions using Node-Aware Processor Grids
We propose an algorithm that aims at minimizing the inter-node communication
volume for distributed and memory-efficient tensor contraction schemes on
modern multi-core compute nodes. The key idea is to define processor grids that
optimize intra-/inter-node communication volume in the employed contraction
algorithms. We present an implementation of the proposed node-aware
communication algorithm into the Cyclops Tensor Framework (CTF). We demonstrate
that this implementation achieves a significantly improved performance for
matrix-matrix-multiplication and tensor-contractions on up to several hundreds
modern compute nodes compared to conventional implementations without using
node-aware processor grids. Our implementation shows good performance when
compared with existing state-of-the-art parallel matrix multiplication
libraries (COSMA and ScaLAPACK). In addition to the discussion of the
performance for matrix-matrix-multiplication, we also investigate the
performance of our node-aware communication algorithm for tensor contractions
as they occur in quantum chemical coupled-cluster methods. To this end we
employ a modified version of CTF in combination with a coupled-cluster code
(Cc4s). Our findings show that the node-aware communication algorithm is also
able to improve the performance of coupled-cluster theory calculations for
real-world problems running on tens to hundreds of compute nodes.Comment: 15 pages, 4 figure
Heterogeneous hierarchical workflow composition
Workflow systems promise scientists an automated end-to-end path from hypothesis to discovery. However, expecting any single workflow system to deliver such a wide range of capabilities is impractical. A more practical solution is to compose the end-to-end workflow from more than one system. With this goal in mind, the integration of task-based and in situ workflows is explored, where the result is a hierarchical heterogeneous workflow composed of subworkflows, with different levels of the hierarchy using different programming, execution, and data models. Materials science use cases demonstrate the advantages of such heterogeneous hierarchical workflow composition.This work is a collaboration between Argonne National Laboratory and the Barcelona Supercomputing Center within the Joint Laboratory for Extreme-Scale Computing. This research is supported by the
U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, under contract number DE-AC02-
06CH11357, program manager Laura Biven, and by the Spanish
Government (SEV2015-0493), by the Spanish Ministry of Science and Innovation (contract TIN2015-65316-P), by Generalitat de Catalunya (contract 2014-SGR-1051).Peer ReviewedPostprint (author's final draft
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