25,000 research outputs found
Finding Best Compiler Options for Critical Software Using Parallel Algorithms
The efficiency of a software piece is a key factor for many systems. Real-time programs, critical software, device drivers, kernel OS functions and many other software pieces which are executed thousands or even millions of times per day require a very efficient execution. How this software is built can significantly affect the run time for these programs, since the context is that of compile-once/run-many. In this sense, the optimization flags used during the compilation time are a crucial element for this goal and they could make a big difference in the final execution time. In this paper, we use parallel metaheuristic techniques to automatically decide which optimization flags should be activated during the compilation on a set of benchmarking programs. The using the appropriate flag configuration is a complex combinatorial problem, but our approach is able to adapt the flag tuning to the characteristics of the software, improving the final run times with respect to other spread practicesThis research has been partially funded by the Spanish MINECO and FEDER projects (TIN2014-57341-R (http://moveon.lcc.uma.es), TIN2016-81766-REDT (http://cirti.es), and TIN2017-88213-R (http://6city.lcc.uma.es). It is also funded by Universidad de MĂĄlaga. Campus de Excelencia Internacional AndalucĂa Tech
The CIAO multiparadigm compiler and system: A progress report
Abstract is not available
The CIAO Multi-Dialect Compiler and System: An Experimentation Workbench for Future (C)LP Systems
CIAO is an advanced programming environment supporting Logic and Constraint programming. It offers a simple concurrent kernel on top of which declarative and non-declarative extensions are added via librarles. Librarles are available for supporting the ISOProlog standard, several constraint domains, functional and higher order programming, concurrent and distributed programming, internet programming, and others. The source language allows declaring properties of predicates via assertions, including types and modes. Such properties are checked at compile-time or at run-time. The compiler and system architecture are designed to natively support modular global analysis, with the two objectives of proving properties in assertions and performing program optimizations, including transparently exploiting parallelism in programs. The purpose of this paper is to report on recent progress made in the context of the CIAO system, with special emphasis on the capabilities of the compiler, the techniques used for supporting such capabilities, and the results in the ĂĄreas of program analysis and transformation already obtained with the system
pocl: A Performance-Portable OpenCL Implementation
OpenCL is a standard for parallel programming of heterogeneous systems. The
benefits of a common programming standard are clear; multiple vendors can
provide support for application descriptions written according to the standard,
thus reducing the program porting effort. While the standard brings the obvious
benefits of platform portability, the performance portability aspects are
largely left to the programmer. The situation is made worse due to multiple
proprietary vendor implementations with different characteristics, and, thus,
required optimization strategies.
In this paper, we propose an OpenCL implementation that is both portable and
performance portable. At its core is a kernel compiler that can be used to
exploit the data parallelism of OpenCL programs on multiple platforms with
different parallel hardware styles. The kernel compiler is modularized to
perform target-independent parallel region formation separately from the
target-specific parallel mapping of the regions to enable support for various
styles of fine-grained parallel resources such as subword SIMD extensions, SIMD
datapaths and static multi-issue. Unlike previous similar techniques that work
on the source level, the parallel region formation retains the information of
the data parallelism using the LLVM IR and its metadata infrastructure. This
data can be exploited by the later generic compiler passes for efficient
parallelization.
The proposed open source implementation of OpenCL is also platform portable,
enabling OpenCL on a wide range of architectures, both already commercialized
and on those that are still under research. The paper describes how the
portability of the implementation is achieved. Our results show that most of
the benchmarked applications when compiled using pocl were faster or close to
as fast as the best proprietary OpenCL implementation for the platform at hand.Comment: This article was published in 2015; it is now openly accessible via
arxi
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
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