30,208 research outputs found
Report of the sensor readout electronics panel
The findings of the Sensor Readout Electronics Panel are summarized in regard to technology assessment and recommended development plans. In addition to two specific readout issues, cryogenic readouts and sub-electron noise, the panel considered three advanced technology areas that impact the ability to achieve large format sensor arrays. These are mega-pixel focal plane packaging issues, focal plane to data processing module interfaces, and event driven readout architectures. Development in each of these five areas was judged to have significant impact in enabling the sensor performance desired for the Astrotech 21 mission set. Other readout issues, such as focal plane signal processing or other high volume data acquisition applications important for Eos-type mapping, were determined not to be relevant for astrophysics science goals
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
On Deciding Local Theory Extensions via E-matching
Satisfiability Modulo Theories (SMT) solvers incorporate decision procedures
for theories of data types that commonly occur in software. This makes them
important tools for automating verification problems. A limitation frequently
encountered is that verification problems are often not fully expressible in
the theories supported natively by the solvers. Many solvers allow the
specification of application-specific theories as quantified axioms, but their
handling is incomplete outside of narrow special cases.
In this work, we show how SMT solvers can be used to obtain complete decision
procedures for local theory extensions, an important class of theories that are
decidable using finite instantiation of axioms. We present an algorithm that
uses E-matching to generate instances incrementally during the search,
significantly reducing the number of generated instances compared to eager
instantiation strategies. We have used two SMT solvers to implement this
algorithm and conducted an extensive experimental evaluation on benchmarks
derived from verification conditions for heap-manipulating programs. We believe
that our results are of interest to both the users of SMT solvers as well as
their developers
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