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Improving Performance of M-to-N Processing and Data Redistribution in In Transit Analysis and Visualization
In an in transit setting, a parallel data producer, such as a numerical simulation, runs on one set of ranks M, while a data consumer, such as a parallel visualization application, runs on a different set of ranks N. One of the central challenges in this in transit setting is to determine the mapping of data from the set of M producer ranks to the set of N consumer ranks. This is a challenging problem for several reasons, such as the producer and consumer codes potentially having different scaling characteristics and different data models. The resulting mapping from M to N ranks can have a significant impact on aggregate application performance. In this work, we present an approach for performing this M-to-N mapping in a way that has broad applicability across a diversity of data producer and consumer applications. We evaluate its design and performance with
a study that runs at high concurrency on a modern HPC platform. By leveraging design characteristics, which facilitate an “intelligent” mapping from M-to-N, we observe significant performance gains are possible in terms of several different metrics, including time-to-solution and amount of data moved
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
Cold gas in cluster cores: Global stability analysis and non-linear simulations of thermal instability
We perform global linear stability analysis and idealized numerical
simulations in global thermal balance to understand the condensation of cold
gas from hot/virial atmospheres (coronae), in particular the intracluster
medium (ICM). We pay particular attention to geometry (e.g., spherical versus
plane-parallel) and the nature of the gravitational potential. Global linear
analysis gives a similar value for the fastest growing thermal instability
modes in spherical and Cartesian geometries. Simulations and observations
suggest that cooling in halos critically depends on the ratio of the cooling
time to the free-fall time (). Extended cold gas condenses out
of the ICM only if this ratio is smaller than a threshold value close to 10.
Previous works highlighted the difference between the nature of cold gas
condensation in spherical and plane-parallel atmospheres; namely, cold gas
condensation appeared easier in spherical atmospheres. This apparent difference
due to geometry arises because the previous plane-parallel simulations focussed
on {\em in situ} condensation of multiphase gas but spherical simulations
studied condensation {\em anywhere} in the box. Unlike previous claims, our
nonlinear simulations show that there are only minor differences in cold gas
condensation, either in situ or anywhere, for different geometries. The amount
of cold gas condensing depends on the shape of the gravitational potential
well; gas has more time to condense if gravitational acceleration decreases
toward the center. In our idealized simulations with heating balancing cooling
in each layer, there can be significant mass/energy/momentum transfer across
layers that can trigger condensation and drive far beyond the
critical value close to 10. Triggered condensation is very prominent in
plane-parallel simulations, in which a large amount of cold gas condenses out.Comment: 17 pages, 16 figures, 2 tables, version accepted in MNRAS. Links to
python codes for global stability analysis:
https://drive.google.com/folderview?id=0B2HaDXI2USsZWUdESVVsN2RGeVU&usp=sharin
ArrayBridge: Interweaving declarative array processing with high-performance computing
Scientists are increasingly turning to datacenter-scale computers to produce
and analyze massive arrays. Despite decades of database research that extols
the virtues of declarative query processing, scientists still write, debug and
parallelize imperative HPC kernels even for the most mundane queries. This
impedance mismatch has been partly attributed to the cumbersome data loading
process; in response, the database community has proposed in situ mechanisms to
access data in scientific file formats. Scientists, however, desire more than a
passive access method that reads arrays from files.
This paper describes ArrayBridge, a bi-directional array view mechanism for
scientific file formats, that aims to make declarative array manipulations
interoperable with imperative file-centric analyses. Our prototype
implementation of ArrayBridge uses HDF5 as the underlying array storage library
and seamlessly integrates into the SciDB open-source array database system. In
addition to fast querying over external array objects, ArrayBridge produces
arrays in the HDF5 file format just as easily as it can read from it.
ArrayBridge also supports time travel queries from imperative kernels through
the unmodified HDF5 API, and automatically deduplicates between array versions
for space efficiency. Our extensive performance evaluation in NERSC, a
large-scale scientific computing facility, shows that ArrayBridge exhibits
statistically indistinguishable performance and I/O scalability to the native
SciDB storage engine.Comment: 12 pages, 13 figure
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