6,384 research outputs found
Application-tailored Linear Algebra Algorithms: A search-based Approach
In this paper, we tackle the problem of automatically generating algorithms
for linear algebra operations by taking advantage of problem-specific
knowledge. In most situations, users possess much more information about the
problem at hand than what current libraries and computing environments accept;
evidence shows that if properly exploited, such information leads to
uncommon/unexpected speedups. We introduce a knowledge-aware linear algebra
compiler that allows users to input matrix equations together with properties
about the operands and the problem itself; for instance, they can specify that
the equation is part of a sequence, and how successive instances are related to
one another. The compiler exploits all this information to guide the generation
of algorithms, to limit the size of the search space, and to avoid redundant
computations. We applied the compiler to equations arising as part of
sensitivity and genome studies; the algorithms produced exhibit, respectively,
100- and 1000-fold speedups
Lanczos eigensolution method for high-performance computers
The theory, computational analysis, and applications are presented of a Lanczos algorithm on high performance computers. The computationally intensive steps of the algorithm are identified as: the matrix factorization, the forward/backward equation solution, and the matrix vector multiples. These computational steps are optimized to exploit the vector and parallel capabilities of high performance computers. The savings in computational time from applying optimization techniques such as: variable band and sparse data storage and access, loop unrolling, use of local memory, and compiler directives are presented. Two large scale structural analysis applications are described: the buckling of a composite blade stiffened panel with a cutout, and the vibration analysis of a high speed civil transport. The sequential computational time for the panel problem executed on a CONVEX computer of 181.6 seconds was decreased to 14.1 seconds with the optimized vector algorithm. The best computational time of 23 seconds for the transport problem with 17,000 degs of freedom was on the the Cray-YMP using an average of 3.63 processors
Proceedings of the 3rd Workshop on Domain-Specific Language Design and Implementation (DSLDI 2015)
The goal of the DSLDI workshop is to bring together researchers and
practitioners interested in sharing ideas on how DSLs should be designed,
implemented, supported by tools, and applied in realistic application contexts.
We are both interested in discovering how already known domains such as graph
processing or machine learning can be best supported by DSLs, but also in
exploring new domains that could be targeted by DSLs. More generally, we are
interested in building a community that can drive forward the development of
modern DSLs. These informal post-proceedings contain the submitted talk
abstracts to the 3rd DSLDI workshop (DSLDI'15), and a summary of the panel
discussion on Language Composition
Formal Reasoning Using an Iterative Approach with an Integrated Web IDE
This paper summarizes our experience in communicating the elements of
reasoning about correctness, and the central role of formal specifications in
reasoning about modular, component-based software using a language and an
integrated Web IDE designed for the purpose. Our experience in using such an
IDE, supported by a 'push-button' verifying compiler in a classroom setting,
reveals the highly iterative process learners use to arrive at suitably
specified, automatically provable code. We explain how the IDE facilitates
reasoning at each step of this process by providing human readable verification
conditions (VCs) and feedback from an integrated prover that clearly indicates
unprovable VCs to help identify obstacles to completing proofs. The paper
discusses the IDE's usage in verified software development using several
examples drawn from actual classroom lectures and student assignments to
illustrate principles of design-by-contract and the iterative process of
creating and subsequently refining assertions, such as loop invariants in
object-based code.Comment: In Proceedings F-IDE 2015, arXiv:1508.0338
Knowledge-Based Automatic Generation of Linear Algebra Algorithms and Code
This dissertation focuses on the design and the implementation of
domain-specific compilers for linear algebra matrix equations. The development
of efficient libraries for such equations, which lie at the heart of most
software for scientific computing, is a complex process that requires expertise
in a variety of areas, including the application domain, algorithms, numerical
analysis and high-performance computing. Moreover, the process involves the
collaboration of several people for a considerable amount of time. With our
compilers, we aim to relieve the developers from both designing algorithms and
writing code, and to generate routines that match or even surpass the
performance of those written by human experts.Comment: Dissertatio
CSM Testbed Development and Large-Scale Structural Applications
A research activity called Computational Structural Mechanics (CSM) conducted at the NASA Langley Research Center is described. This activity is developing advanced structural analysis and computational methods that exploit high-performance computers. Methods are developed in the framework of the CSM Testbed software system and applied to representative complex structural analysis problems from the aerospace industry. An overview of the CSM Testbed methods development environment is presented and some new numerical methods developed on a CRAY-2 are described. Selected application studies performed on the NAS CRAY-2 are also summarized
Clafer: Lightweight Modeling of Structure, Behaviour, and Variability
Embedded software is growing fast in size and complexity, leading to intimate
mixture of complex architectures and complex control. Consequently, software
specification requires modeling both structures and behaviour of systems.
Unfortunately, existing languages do not integrate these aspects well, usually
prioritizing one of them. It is common to develop a separate language for each
of these facets. In this paper, we contribute Clafer: a small language that
attempts to tackle this challenge. It combines rich structural modeling with
state of the art behavioural formalisms. We are not aware of any other modeling
language that seamlessly combines these facets common to system and software
modeling. We show how Clafer, in a single unified syntax and semantics, allows
capturing feature models (variability), component models, discrete control
models (automata) and variability encompassing all these aspects. The language
is built on top of first order logic with quantifiers over basic entities (for
modeling structures) combined with linear temporal logic (for modeling
behaviour). On top of this semantic foundation we build a simple but expressive
syntax, enriched with carefully selected syntactic expansions that cover
hierarchical modeling, associations, automata, scenarios, and Dwyer's property
patterns. We evaluate Clafer using a power window case study, and comparing it
against other notations that substantially overlap with its scope (SysML, AADL,
Temporal OCL and Live Sequence Charts), discussing benefits and perils of using
a single notation for the purpose
A Factor Graph Approach to Automated Design of Bayesian Signal Processing Algorithms
The benefits of automating design cycles for Bayesian inference-based
algorithms are becoming increasingly recognized by the machine learning
community. As a result, interest in probabilistic programming frameworks has
much increased over the past few years. This paper explores a specific
probabilistic programming paradigm, namely message passing in Forney-style
factor graphs (FFGs), in the context of automated design of efficient Bayesian
signal processing algorithms. To this end, we developed "ForneyLab"
(https://github.com/biaslab/ForneyLab.jl) as a Julia toolbox for message
passing-based inference in FFGs. We show by example how ForneyLab enables
automatic derivation of Bayesian signal processing algorithms, including
algorithms for parameter estimation and model comparison. Crucially, due to the
modular makeup of the FFG framework, both the model specification and inference
methods are readily extensible in ForneyLab. In order to test this framework,
we compared variational message passing as implemented by ForneyLab with
automatic differentiation variational inference (ADVI) and Monte Carlo methods
as implemented by state-of-the-art tools "Edward" and "Stan". In terms of
performance, extensibility and stability issues, ForneyLab appears to enjoy an
edge relative to its competitors for automated inference in state-space models.Comment: Accepted for publication in the International Journal of Approximate
Reasonin
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