875 research outputs found
Exploring Scientific Application Performance Using Large Scale Object Storage
One of the major performance and scalability bottlenecks in large scientific
applications is parallel reading and writing to supercomputer I/O systems. The
usage of parallel file systems and consistency requirements of POSIX, that all
the traditional HPC parallel I/O interfaces adhere to, pose limitations to the
scalability of scientific applications. Object storage is a widely used storage
technology in cloud computing and is more frequently proposed for HPC workload
to address and improve the current scalability and performance of I/O in
scientific applications. While object storage is a promising technology, it is
still unclear how scientific applications will use object storage and what the
main performance benefits will be. This work addresses these questions, by
emulating an object storage used by a traditional scientific application and
evaluating potential performance benefits. We show that scientific applications
can benefit from the usage of object storage on large scales.Comment: Preprint submitted to WOPSSS workshop at ISC 201
h5fortran: object-oriented polymorphic Fortran interface for HDF5 file IO
h5fortran provides object-oriented and functional interface to the HDF5 library for Fortran. h5fortran prioritizes ease-of-use, robust self-tests and Fortran 2008 standard syntax for broad compiler, operating system and computing platform support from Raspberry Pi to HPC.https://engrxiv.org/u85s4First author draf
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
An overview of the planned CCAT software system
CCAT will be a 25m diameter sub-millimeter telescope capable of operating in
the 0.2 to 2.1mm wavelength range. It will be located at an altitude of 5600m
on Cerro Chajnantor in northern Chile near the ALMA site. The anticipated first
generation instruments include large format (60,000 pixel) kinetic inductance
detector (KID) cameras, a large format heterodyne array and a direct detection
multi-object spectrometer. The paper describes the architecture of the CCAT
software and the development strategy.Comment: 17 pages, 6 figures, to appear in Software and Cyberinfrastructure
for Astronomy III, Chiozzi & Radziwill (eds), Proc. SPIE 9152, paper ID
9152-10
Towards Exascale Scientific Metadata Management
Advances in technology and computing hardware are enabling scientists from
all areas of science to produce massive amounts of data using large-scale
simulations or observational facilities. In this era of data deluge, effective
coordination between the data production and the analysis phases hinges on the
availability of metadata that describe the scientific datasets. Existing
workflow engines have been capturing a limited form of metadata to provide
provenance information about the identity and lineage of the data. However,
much of the data produced by simulations, experiments, and analyses still need
to be annotated manually in an ad hoc manner by domain scientists. Systematic
and transparent acquisition of rich metadata becomes a crucial prerequisite to
sustain and accelerate the pace of scientific innovation. Yet, ubiquitous and
domain-agnostic metadata management infrastructure that can meet the demands of
extreme-scale science is notable by its absence.
To address this gap in scientific data management research and practice, we
present our vision for an integrated approach that (1) automatically captures
and manipulates information-rich metadata while the data is being produced or
analyzed and (2) stores metadata within each dataset to permeate
metadata-oblivious processes and to query metadata through established and
standardized data access interfaces. We motivate the need for the proposed
integrated approach using applications from plasma physics, climate modeling
and neuroscience, and then discuss research challenges and possible solutions
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