756 research outputs found
DOLFIN: Automated Finite Element Computing
We describe here a library aimed at automating the solution of partial differential equations using the finite element method. By employing novel techniques for automated code generation, the library combines a high level of expressiveness with efficient computation. Finite element variational forms may be expressed in near mathematical notation, from which low-level code is automatically generated, compiled and seamlessly integrated with efficient implementations of
computational meshes and high-performance linear algebra. Easy-to-use object-oriented interfaces to the library are provided in the form of a C++ library and a Python module. This paper discusses the mathematical abstractions and methods used in the design of the library and its implementation. A number of examples are presented to demonstrate the use of the library in application code
Towards Ontology-Based Program Analysis
Program analysis is fundamental for program optimizations, debugging,
and many other tasks. But developing program analyses has been a
challenging and error-prone process for general users. Declarative
program analysis has shown the promise to dramatically improve the
productivity in the development of program analyses. Current
declarative program analysis is however subject to some major
limitations in supporting cooperations among analysis tools, guiding
program optimizations, and often requires much effort for repeated
program preprocessing.
In this work, we advocate the integration of ontology into declarative
program analysis. As a way to standardize the definitions of concepts
in a domain and the representation of the knowledge in the domain,
ontology offers a promising way to address the limitations of current
declarative program analysis. We develop a prototype framework named
PATO for conducting program analysis upon ontology-based program
representation. Experiments on six program analyses confirm the
potential of ontology for complementing existing declarative program
analysis. It supports multiple analyses without separate program
preprocessing, promotes cooperative Liveness analysis between two
compilers, and effectively guides a data placement optimization for
Graphic Processing Units (GPU)
Automated Code Generation for Lattice Quantum Chromodynamics and beyond
We present here our ongoing work on a Domain Specific Language which aims to
simplify Monte-Carlo simulations and measurements in the domain of Lattice
Quantum Chromodynamics. The tool-chain, called Qiral, is used to produce
high-performance OpenMP C code from LaTeX sources. We discuss conceptual issues
and details of implementation and optimization. The comparison of the
performance of the generated code to the well-established simulation software
is also made
CU2CL: A CUDA-to-OpenCL Translator for Multi- and Many-core Architectures
The use of graphics processing units (GPUs) in
high-performance parallel computing continues to become more
prevalent, often as part of a heterogeneous system. For years,
CUDA has been the de facto programming environment for
nearly all general-purpose GPU (GPGPU) applications. In spite
of this, the framework is available only on NVIDIA GPUs,
traditionally requiring reimplementation in other frameworks
in order to utilize additional multi- or many-core devices.
On the other hand, OpenCL provides an open and vendorneutral
programming environment and runtime system. With
implementations available for CPUs, GPUs, and other types of
accelerators, OpenCL therefore holds the promise of a āwrite
once, run anywhereā ecosystem for heterogeneous computing.
Given the many similarities between CUDA and OpenCL,
manually porting a CUDA application to OpenCL is typically
straightforward, albeit tedious and error-prone. In response
to this issue, we created CU2CL, an automated CUDA-to-
OpenCL source-to-source translator that possesses a novel design
and clever reuse of the Clang compiler framework. Currently,
the CU2CL translator covers the primary constructs found in
CUDA runtime API, and we have successfully translated many
applications from the CUDA SDK and Rodinia benchmark suite.
The performance of our automatically translated applications via
CU2CL is on par with their manually ported countparts
The exploitation of parallelism on shared memory multiprocessors
PhD ThesisWith the arrival of many general purpose shared memory multiple processor
(multiprocessor) computers into the commercial arena during the mid-1980's, a
rift has opened between the raw processing power offered by the emerging
hardware and the relative inability of its operating software to effectively deliver
this power to potential users. This rift stems from the fact that, currently, no
computational model with the capability to elegantly express parallel activity is
mature enough to be universally accepted, and used as the basis for programming
languages to exploit the parallelism that multiprocessors offer. To add to this,
there is a lack of software tools to assist programmers in the processes of designing
and debugging parallel programs.
Although much research has been done in the field of programming languages,
no undisputed candidate for the most appropriate language for programming
shared memory multiprocessors has yet been found. This thesis examines why this
state of affairs has arisen and proposes programming language constructs,
together with a programming methodology and environment, to close the ever
widening hardware to software gap.
The novel programming constructs described in this thesis are intended for use
in imperative languages even though they make use of the synchronisation
inherent in the dataflow model by using the semantics of single assignment when
operating on shared data, so giving rise to the term shared values. As there are
several distinct parallel programming paradigms, matching flavours of shared
value are developed to permit the concise expression of these paradigms.The Science and Engineering Research Council
The C++0x "Concepts" Effort
C++0x is the working title for the revision of the ISO standard of the C++
programming language that was originally planned for release in 2009 but that
was delayed to 2011. The largest language extension in C++0x was "concepts",
that is, a collection of features for constraining template parameters. In
September of 2008, the C++ standards committee voted the concepts extension
into C++0x, but then in July of 2009, the committee voted the concepts
extension back out of C++0x.
This article is my account of the technical challenges and debates within the
"concepts" effort in the years 2003 to 2009. To provide some background, the
article also describes the design space for constrained parametric
polymorphism, or what is colloquially know as constrained generics. While this
article is meant to be generally accessible, the writing is aimed toward
readers with background in functional programming and programming language
theory. This article grew out of a lecture at the Spring School on Generic and
Indexed Programming at the University of Oxford, March 2010
Efficient integration of software components for scientific simulations
Abstract unavailable please refer to PD
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