67,706 research outputs found
Lily: A parser generator for LL(1) languages
This paper discusses the design and implementation of Lily, a language for generating LL(1) language parsers, originally designed by Dr. Thomas J. Sager of the University of Missouri--Rolla. A method for the automatic generation of parser tables is described which creates small, highly optimized tables, suitable for conversion to minimal perfect hash functions.
An implementation of Lily is discussed with attention to design goals, implementation of parser table generation, and table optimization techniques. Proposals are made detailing possibilities for further augmentation of the system. Examples of Lily programs are given as well as a manual for the system
Texturizing PPCG: Supporting Texture Memory in a Polyhedral Compiler
In this paper, we discuss techniques to transform
sequential programs to texture/surface memory optimized CUDA
programs. We achieve this by using PPCG, an automatic paral-
lelizing compiler based on the Polyhedral model. We implemented
a static analysis in PPCG which validates the semantics of the
texturized transformed program. Depending on the results of
the analysis, our algorithm chooses to use texture and/or surface
memory, and alters the Abstract Syntax Tree accordingly. We
also modified the code-generation phase of PPCG to take care
of various subtleties. We evaluated the texturization algorithm
on the PolyBench (4.2.1 beta) benchmark and observed up to
1.6x speedup with a geometric mean of 1.103X. The title and
at many places, the paper uses term Texture memory. But, the
optimizations are for Texture and Surface memory
Automated Design of a Correction Dipole Magnet for LHC
A correction dipole magnet, with a horizontal dipole nested inside a vertical dipole has been designed and optimized linking together different electromagnetic software and CAD/CAM systems. The necessary interfaces have recently been established in the program ROXIE which has been developed at CERN for the automatic generation and optimization of superconducting coil geometries. The program provides, in addition to a mathematical optimization chest, interfaces to commercial electromagnetic and structural software packages, CAD/CAM and databases. The results from electromagnetic calculations with different programs have been compared. Some modelling considerations to reduce the computation time are also given
A Survey on Compiler Autotuning using Machine Learning
Since the mid-1990s, researchers have been trying to use machine-learning
based approaches to solve a number of different compiler optimization problems.
These techniques primarily enhance the quality of the obtained results and,
more importantly, make it feasible to tackle two main compiler optimization
problems: optimization selection (choosing which optimizations to apply) and
phase-ordering (choosing the order of applying optimizations). The compiler
optimization space continues to grow due to the advancement of applications,
increasing number of compiler optimizations, and new target architectures.
Generic optimization passes in compilers cannot fully leverage newly introduced
optimizations and, therefore, cannot keep up with the pace of increasing
options. This survey summarizes and classifies the recent advances in using
machine learning for the compiler optimization field, particularly on the two
major problems of (1) selecting the best optimizations and (2) the
phase-ordering of optimizations. The survey highlights the approaches taken so
far, the obtained results, the fine-grain classification among different
approaches and finally, the influential papers of the field.Comment: version 5.0 (updated on September 2018)- Preprint Version For our
Accepted Journal @ ACM CSUR 2018 (42 pages) - This survey will be updated
quarterly here (Send me your new published papers to be added in the
subsequent version) History: Received November 2016; Revised August 2017;
Revised February 2018; Accepted March 2018
Fine-Grain Iterative Compilation for WCET Estimation
Compiler optimizations, although reducing the execution times of programs, raise issues in static WCET estimation techniques and tools. Flow facts, such as loop bounds, may not be automatically found by static WCET analysis tools after aggressive code optimizations. In this paper, we explore the use of iterative compilation (WCET-directed program optimization to explore the optimization space), with the objective to (i) allow flow facts to be automatically found and (ii) select optimizations that result in the lowest WCET estimates. We also explore to which extent code outlining helps, by allowing the selection of different optimization options for different code snippets of the application
A Survey on Software Testing Techniques using Genetic Algorithm
The overall aim of the software industry is to ensure delivery of high
quality software to the end user. To ensure high quality software, it is
required to test software. Testing ensures that software meets user
specifications and requirements. However, the field of software testing has a
number of underlying issues like effective generation of test cases,
prioritisation of test cases etc which need to be tackled. These issues demand
on effort, time and cost of the testing. Different techniques and methodologies
have been proposed for taking care of these issues. Use of evolutionary
algorithms for automatic test generation has been an area of interest for many
researchers. Genetic Algorithm (GA) is one such form of evolutionary
algorithms. In this research paper, we present a survey of GA approach for
addressing the various issues encountered during software testing.Comment: 13 Page
Description and Optimization of Abstract Machines in a Dialect of Prolog
In order to achieve competitive performance, abstract machines for Prolog and
related languages end up being large and intricate, and incorporate
sophisticated optimizations, both at the design and at the implementation
levels. At the same time, efficiency considerations make it necessary to use
low-level languages in their implementation. This makes them laborious to code,
optimize, and, especially, maintain and extend. Writing the abstract machine
(and ancillary code) in a higher-level language can help tame this inherent
complexity. We show how the semantics of most basic components of an efficient
virtual machine for Prolog can be described using (a variant of) Prolog. These
descriptions are then compiled to C and assembled to build a complete bytecode
emulator. Thanks to the high level of the language used and its closeness to
Prolog, the abstract machine description can be manipulated using standard
Prolog compilation and optimization techniques with relative ease. We also show
how, by applying program transformations selectively, we obtain abstract
machine implementations whose performance can match and even exceed that of
state-of-the-art, highly-tuned, hand-crafted emulators.Comment: 56 pages, 46 figures, 5 tables, To appear in Theory and Practice of
Logic Programming (TPLP
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