Semantic-Driven Parallelization of Loops Operating on User-Defined Containers

We describe ROSE, a C++ infrastructure for source-to-source translation, that provides an interface for programmers to easily write their own translators for optimizing user-defined high-level abstractions

de Supinski, Semantic-driven parallelization of loops operating on user-defined containers

1 Introduction In object-oriented languages such as C++, abstractions are a key aspect of library design, sharing aspects of language design, which aims to provide the application developer with an efficient and convenient interface. For example, the C++ Standard Template Library (STL), parts of which are standardized within the C++ standard libraries, includes a collection of template classes that can be used as containers for user-defined constructs. Some STL containers, such as vectors, provide random access to their elements using an integer index, while other containers such as lists and sets provide other means to access their elements. Nevertheless, all STL containers provide sequential element accesses and thus all of them can be used in the code fragment in Figure 1. This design strategy permits all containers to be used interchangeably in algorithms that process a sequence of elements

Traversal, Case Analysis, and Lowering for C++ Program Analysis

To work effectively, programmers need tools to support their typical development activities, such as the creation, analysis, and transformation of source code. Analysis and transformation tools can be difficult to write for modern programming languages and, without a reusable framework, each tool must separately implement nontrivial algorithms like name lookup and type checking. This thesis describes an extension to one such framework, named Pivot, that focuses on programs written in C++. This extension, named Filter, assists the tool builder in traversal, case analysis, and lowering of the data structure representing C++ programs. Comparisons described in the thesis show a 2-4x code reduction when solving basic problems (e.g., searching for uses of a given declaration) using the extension and a performance overhead that drops below 2x for larger problems (e.g., checking C++ layout compatibility)

A Language for Specifying Compiler Optimizations for Generic Software

Compiler optimization is important to software performance, and modern processor architectures make optimization even more critical. However, many modern software applications use libraries providing high levels of abstraction. Such libraries often hinder effective optimization—the libraries are difficult to analyze using current compiler technology. For example, high-level libraries often use dynamic memory allocation and indirectly expressed control structures, such as iterator-based loops. Programs using these libraries often cannot achieve an optimal level of performance. On the other hand, software libraries have also been recognized as potentially aiding in program optimization. One proposed implementation of library-based optimization is to allow the library author, or a library user, to define custom analyses and optimizations. Only limited systems have been created to take advantage of this potential, however. One problem in creating a framework for defining new optimizations and analyses is how users are to specify them: implementing them by hand inside a compiler is difficult and prone to errors. Thus, a domain-specific language for library-based compiler optimizations would be beneficial. Many optimization specification languages have appeared in the literature, but they tend to be either limited in power or unnecessarily difficult to use. Therefore, I have designed, implemented, and evaluated the Pavilion language for specifying program analyses and optimizations, designed for library authors and users. These analyses and optimizations can be based on the implementation of a particular library, its use in a specific program, or on the properties of a broad range of types, expressed through concepts. The new system is intended to provide a high level of expressiveness, even though the intended users are unlikely to be compiler experts

Hybrid analysis of memory references and its application to automatic parallelization

Executing sequential code in parallel on a multithreaded machine has been an elusive goal of the academic and industrial research communities for many years. It has recently become more important due to the widespread introduction of multicores in PCs. Automatic multithreading has not been achieved because classic, static compiler analysis was not powerful enough and program behavior was found to be, in many cases, input dependent. Speculative thread level parallelization was a welcome avenue for advancing parallelization coverage but its performance was not always optimal due to the sometimes unnecessary overhead of checking every dynamic memory reference. In this dissertation we introduce a novel analysis technique, Hybrid Analysis, which unifies static and dynamic memory reference techniques into a seamless compiler framework which extracts almost maximum available parallelism from scientific codes and incurs close to the minimum necessary run time overhead. We present how to extract maximum information from the quantities that could not be sufficiently analyzed through static compiler methods, and how to generate sufficient conditions which, when evaluated dynamically, can validate optimizations. Our techniques have been fully implemented in the Polaris compiler and resulted in whole program speedups on a large number of industry standard benchmark applications