Loop Optimizations in C and C++ Compilers: An Overview

The evolution of computer hardware in the past decades has truly been remarkable. From scalar instruction execution through superscalar and vector to parallel, processors are able to reach astonishing speeds – if programmed accordingly. Now, writing programs that take all the hardware details into consideration for the sake of efficiency is extremely difficult and error-prone. Therefore we increasingly rely on compilers to do the heavy-lifting for us. A significant part of optimizations done by compilers are loop optimiza- tions. Loops are inherently expensive parts of a program in terms of run time, and it is important that they exploit superscalar and vector instructions. In this paper, we give an overview of the scientific literature on loop optimization technology, and summarize the status of current implementations in the most widely used C and C++ compilers in the industry

Loop optimizations in C and C++ compilers: an overview

The evolution of computer hardware in the past decades has truly been remarkable. From scalar instruction execution through superscalar and vector to parallel, processors are able to reach astonishing speeds – if programmed accordingly. Now, writing programs that take all the hardware details into consideration for the sake of efficiency is extremely difficult and error-prone. Therefore we increasingly rely on compilers to do the heavy-lifting for us. A significant part of optimizations done by compilers are loop optimiza- tions. Loops are inherently expensive parts of a program in terms of run time, and it is important that they exploit superscalar and vector instructions. In this paper, we give an overview of the scientific literature on loop optimization technology, and summarize the status of current implementations in the most widely used C and C++ compilers in the industry

Survey of new vector computers: The CRAY 1S from CRAY research; the CYBER 205 from CDC and the parallel computer from ICL - architecture and programming

Problems which can arise with vector and parallel computers are discussed in a user oriented context. Emphasis is placed on the algorithms used and the programming techniques adopted. Three recently developed supercomputers are examined and typical application examples are given in CRAY FORTRAN, CYBER 205 FORTRAN and DAP (distributed array processor) FORTRAN. The systems performance is compared. The addition of parts of two N x N arrays is considered. The influence of the architecture on the algorithms and programming language is demonstrated. Numerical analysis of magnetohydrodynamic differential equations by an explicit difference method is illustrated, showing very good results for all three systems. The prognosis for supercomputer development is assessed

Data-Driven Refactorings for Haskell

Agile software development allows for software to evolve slowly over time. Decisions made during the early stages of a program's lifecycle often come with a cost in the form of technical debt. Technical debt is the concept that reworking a program that is implemented in a naive or "easy" way, is often more difficult than changing the behaviour of a more robust solution. Refactoring is one of the primary ways to reduce technical debt. Refactoring is the process of changing the internal structure of a program without changing its external behaviour. The goal of performing refactorings is to increase code quality, maintainability, and extensibility of the source program. Performing refactorings manually is time consuming and error-prone. This makes automated refactoring tools very useful. Haskell is a strongly typed, pure functional programming language. Haskell's rich type system allows for complex and powerful data models and abstractions. These abstractions and data models are an important part of Haskell programs. This thesis argues that these parts of a program accrue technical debt, and that refactoring is an important technique to reduce this type of technical debt. Refactorings exist that tackle issues with a program's data model, however these refactorings are specific to the object-oriented programming paradigm. This thesis reports on work done to design and automate refactorings that help Haskell programmers develop and evolve these abstractions. This work also discussed the current design and implementation of HaRe (the Haskell Refactorer). HaRe now supports the Glasgow Haskell Compiler's implementation of the Haskell 2010 standard and its extensions, and uses some of GHC's internal packages in its implementation