67,123 research outputs found
Parallel netCDF: A Scientific High-Performance I/O Interface
Dataset storage, exchange, and access play a critical role in scientific
applications. For such purposes netCDF serves as a portable and efficient file
format and programming interface, which is popular in numerous scientific
application domains. However, the original interface does not provide an
efficient mechanism for parallel data storage and access. In this work, we
present a new parallel interface for writing and reading netCDF datasets. This
interface is derived with minimum changes from the serial netCDF interface but
defines semantics for parallel access and is tailored for high performance. The
underlying parallel I/O is achieved through MPI-IO, allowing for dramatic
performance gains through the use of collective I/O optimizations. We compare
the implementation strategies with HDF5 and analyze both. Our tests indicate
programming convenience and significant I/O performance improvement with this
parallel netCDF interface.Comment: 10 pages,7 figure
Practical Sparse Matrices in C++ with Hybrid Storage and Template-Based Expression Optimisation
Despite the importance of sparse matrices in numerous fields of science,
software implementations remain difficult to use for non-expert users,
generally requiring the understanding of underlying details of the chosen
sparse matrix storage format. In addition, to achieve good performance, several
formats may need to be used in one program, requiring explicit selection and
conversion between the formats. This can be both tedious and error-prone,
especially for non-expert users. Motivated by these issues, we present a
user-friendly and open-source sparse matrix class for the C++ language, with a
high-level application programming interface deliberately similar to the widely
used MATLAB language. This facilitates prototyping directly in C++ and aids the
conversion of research code into production environments. The class internally
uses two main approaches to achieve efficient execution: (i) a hybrid storage
framework, which automatically and seamlessly switches between three underlying
storage formats (compressed sparse column, Red-Black tree, coordinate list)
depending on which format is best suited and/or available for specific
operations, and (ii) a template-based meta-programming framework to
automatically detect and optimise execution of common expression patterns.
Empirical evaluations on large sparse matrices with various densities of
non-zero elements demonstrate the advantages of the hybrid storage framework
and the expression optimisation mechanism.Comment: extended and revised version of an earlier conference paper
arXiv:1805.0338
Practical Sparse Matrices in C++ with Hybrid Storage and Template-Based Expression Optimisation
Despite the importance of sparse matrices in numerous fields of science,
software implementations remain difficult to use for non-expert users,
generally requiring the understanding of underlying details of the chosen
sparse matrix storage format. In addition, to achieve good performance, several
formats may need to be used in one program, requiring explicit selection and
conversion between the formats. This can be both tedious and error-prone,
especially for non-expert users. Motivated by these issues, we present a
user-friendly and open-source sparse matrix class for the C++ language, with a
high-level application programming interface deliberately similar to the widely
used MATLAB language. This facilitates prototyping directly in C++ and aids the
conversion of research code into production environments. The class internally
uses two main approaches to achieve efficient execution: (i) a hybrid storage
framework, which automatically and seamlessly switches between three underlying
storage formats (compressed sparse column, Red-Black tree, coordinate list)
depending on which format is best suited and/or available for specific
operations, and (ii) a template-based meta-programming framework to
automatically detect and optimise execution of common expression patterns.
Empirical evaluations on large sparse matrices with various densities of
non-zero elements demonstrate the advantages of the hybrid storage framework
and the expression optimisation mechanism.Comment: extended and revised version of an earlier conference paper
arXiv:1805.0338
A Reconfigurable Tile-Based Architecture to Compute FFT and FIR Functions in the Context of Software-Defined Radio
Software-defined radio (SDR) is the term used for flexible radio systems that can deal with multiple standards. For an efficient implementation, such systems require appropriate reconfigurable architectures. This paper targets the efficient implementation of the most computationally intensive kernels of two significantly different standards, viz. Bluetooth and HiperLAN/2, on the same reconfigurable hardware. These kernels are FIR filtering and FFT. The designed architecture is based on a two-dimensional arrangement of 17 tiles. Each tile contains a multiplier, an adder, local memory and multiplexers allowing flexible communication with the neighboring tiles. The tile-base data path is complemented with a global controller and various memories. The design has been implemented in SystemC and simulated extensively to prove equivalence with a reference all-software design. It has also been synthesized and turns out to outperform significantly other reconfigurable designs with respect to speed and area
Adaptive beamforming for large arrays in satellite communications systems with dispersed coverage
Conventional multibeam satellite communications systems ensure coverage of wide areas through multiple fixed beams where all users inside a beam share the same bandwidth. We consider a new and more flexible system where each user is assigned his own beam, and the users can be very geographically dispersed. This is achieved through the use of a large direct radiating array (DRA) coupled with adaptive beamforming so as to reject interferences and to provide a maximal gain to the user of interest. New fast-converging adaptive beamforming algorithms are presented, which allow to obtain good signal to interference and noise ratio (SINR) with a number of snapshots much lower than the number of antennas in the array. These beamformers are evaluated on reference scenarios
Alpha Entanglement Codes: Practical Erasure Codes to Archive Data in Unreliable Environments
Data centres that use consumer-grade disks drives and distributed
peer-to-peer systems are unreliable environments to archive data without enough
redundancy. Most redundancy schemes are not completely effective for providing
high availability, durability and integrity in the long-term. We propose alpha
entanglement codes, a mechanism that creates a virtual layer of highly
interconnected storage devices to propagate redundant information across a
large scale storage system. Our motivation is to design flexible and practical
erasure codes with high fault-tolerance to improve data durability and
availability even in catastrophic scenarios. By flexible and practical, we mean
code settings that can be adapted to future requirements and practical
implementations with reasonable trade-offs between security, resource usage and
performance. The codes have three parameters. Alpha increases storage overhead
linearly but increases the possible paths to recover data exponentially. Two
other parameters increase fault-tolerance even further without the need of
additional storage. As a result, an entangled storage system can provide high
availability, durability and offer additional integrity: it is more difficult
to modify data undetectably. We evaluate how several redundancy schemes perform
in unreliable environments and show that alpha entanglement codes are flexible
and practical codes. Remarkably, they excel at code locality, hence, they
reduce repair costs and become less dependent on storage locations with poor
availability. Our solution outperforms Reed-Solomon codes in many disaster
recovery scenarios.Comment: The publication has 12 pages and 13 figures. This work was partially
supported by Swiss National Science Foundation SNSF Doc.Mobility 162014, 2018
48th Annual IEEE/IFIP International Conference on Dependable Systems and
Networks (DSN
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