17,968 research outputs found
Domain-Specific Acceleration and Auto-Parallelization of Legacy Scientific Code in FORTRAN 77 using Source-to-Source Compilation
Massively parallel accelerators such as GPGPUs, manycores and FPGAs represent
a powerful and affordable tool for scientists who look to speed up simulations
of complex systems. However, porting code to such devices requires a detailed
understanding of heterogeneous programming tools and effective strategies for
parallelization. In this paper we present a source to source compilation
approach with whole-program analysis to automatically transform single-threaded
FORTRAN 77 legacy code into OpenCL-accelerated programs with parallelized
kernels.
The main contributions of our work are: (1) whole-source refactoring to allow
any subroutine in the code to be offloaded to an accelerator. (2) Minimization
of the data transfer between the host and the accelerator by eliminating
redundant transfers. (3) Pragmatic auto-parallelization of the code to be
offloaded to the accelerator by identification of parallelizable maps and
reductions.
We have validated the code transformation performance of the compiler on the
NIST FORTRAN 78 test suite and several real-world codes: the Large Eddy
Simulator for Urban Flows, a high-resolution turbulent flow model; the shallow
water component of the ocean model Gmodel; the Linear Baroclinic Model, an
atmospheric climate model and Flexpart-WRF, a particle dispersion simulator.
The automatic parallelization component has been tested on as 2-D Shallow
Water model (2DSW) and on the Large Eddy Simulator for Urban Flows (UFLES) and
produces a complete OpenCL-enabled code base. The fully OpenCL-accelerated
versions of the 2DSW and the UFLES are resp. 9x and 20x faster on GPU than the
original code on CPU, in both cases this is the same performance as manually
ported code.Comment: 12 pages, 5 figures, submitted to "Computers and Fluids" as full
paper from ParCFD conference entr
Coordinate noun phrase disambiguation in a generative parsing model
In this paper we present methods for improving the disambiguation of noun phrase (NP) coordination within the framework of a lexicalised history-based parsing model. As
well as reducing noise in the data, we look at modelling two main sources of information for disambiguation: symmetry in conjunct structure, and the dependency between conjunct lexical heads. Our changes to the baseline model result in an increase in NP coordination dependency f-score from 69.9% to
73.8%, which represents a relative reduction in f-score error of 13%
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Structure identification in relational data
This paper presents several investigations into the prospects for identifying meaningful structures in empirical data, namely, structures permitting effective organization of the data to meet requirements of future queries. We propose a general framework whereby the notion of identifiability is given a precise formal definition similar to that of learnability. Using this framework, we then explore if a tractable procedure exists for deciding whether a given relation is decomposable into a constraint network or a CNF theory with desirable topology and, if the answer is positive, identifying the desired decomposition. Finally, we address the problem of expressing a given relation as a Horn theory and, if this is impossible, finding the best k-Horn approximation to the given relation. We show that both problems can be solved in time polynomial in the length of the data
On the Effect of Semantically Enriched Context Models on Software Modularization
Many of the existing approaches for program comprehension rely on the
linguistic information found in source code, such as identifier names and
comments. Semantic clustering is one such technique for modularization of the
system that relies on the informal semantics of the program, encoded in the
vocabulary used in the source code. Treating the source code as a collection of
tokens loses the semantic information embedded within the identifiers. We try
to overcome this problem by introducing context models for source code
identifiers to obtain a semantic kernel, which can be used for both deriving
the topics that run through the system as well as their clustering. In the
first model, we abstract an identifier to its type representation and build on
this notion of context to construct contextual vector representation of the
source code. The second notion of context is defined based on the flow of data
between identifiers to represent a module as a dependency graph where the nodes
correspond to identifiers and the edges represent the data dependencies between
pairs of identifiers. We have applied our approach to 10 medium-sized open
source Java projects, and show that by introducing contexts for identifiers,
the quality of the modularization of the software systems is improved. Both of
the context models give results that are superior to the plain vector
representation of documents. In some cases, the authoritativeness of
decompositions is improved by 67%. Furthermore, a more detailed evaluation of
our approach on JEdit, an open source editor, demonstrates that inferred topics
through performing topic analysis on the contextual representations are more
meaningful compared to the plain representation of the documents. The proposed
approach in introducing a context model for source code identifiers paves the
way for building tools that support developers in program comprehension tasks
such as application and domain concept location, software modularization and
topic analysis
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