Less is More: Exploiting the Standard Compiler Optimization Levels for Better Performance and Energy Consumption

This paper presents the interesting observation that by performing fewer of the optimizations available in a standard compiler optimization level such as -O2, while preserving their original ordering, significant savings can be achieved in both execution time and energy consumption. This observation has been validated on two embedded processors, namely the ARM Cortex-M0 and the ARM Cortex-M3, using two different versions of the LLVM compilation framework; v3.8 and v5.0. Experimental evaluation with 71 embedded benchmarks demonstrated performance gains for at least half of the benchmarks for both processors. An average execution time reduction of 2.4% and 5.3% was achieved across all the benchmarks for the Cortex-M0 and Cortex-M3 processors, respectively, with execution time improvements ranging from 1% up to 90% over the -O2. The savings that can be achieved are in the same range as what can be achieved by the state-of-the-art compilation approaches that use iterative compilation or machine learning to select flags or to determine phase orderings that result in more efficient code. In contrast to these time consuming and expensive to apply techniques, our approach only needs to test a limited number of optimization configurations, less than 64, to obtain similar or even better savings. Furthermore, our approach can support multi-criteria optimization as it targets execution time, energy consumption and code size at the same time.Comment: 15 pages, 3 figures, 71 benchmarks used for evaluatio

PENCIL: Towards a Platform-Neutral Compute Intermediate Language for DSLs

We motivate the design and implementation of a platform-neutral compute intermediate language (PENCIL) for productive and performance-portable accelerator programming

A study of systems implementation languages for the POCCNET system

The results are presented of a study of systems implementation languages for the Payload Operations Control Center Network (POCCNET). Criteria are developed for evaluating the languages, and fifteen existing languages are evaluated on the basis of these criteria

Building Efficient Query Engines in a High-Level Language

Abstraction without regret refers to the vision of using high-level programming languages for systems development without experiencing a negative impact on performance. A database system designed according to this vision offers both increased productivity and high performance, instead of sacrificing the former for the latter as is the case with existing, monolithic implementations that are hard to maintain and extend. In this article, we realize this vision in the domain of analytical query processing. We present LegoBase, a query engine written in the high-level language Scala. The key technique to regain efficiency is to apply generative programming: LegoBase performs source-to-source compilation and optimizes the entire query engine by converting the high-level Scala code to specialized, low-level C code. We show how generative programming allows to easily implement a wide spectrum of optimizations, such as introducing data partitioning or switching from a row to a column data layout, which are difficult to achieve with existing low-level query compilers that handle only queries. We demonstrate that sufficiently powerful abstractions are essential for dealing with the complexity of the optimization effort, shielding developers from compiler internals and decoupling individual optimizations from each other. We evaluate our approach with the TPC-H benchmark and show that: (a) With all optimizations enabled, LegoBase significantly outperforms a commercial database and an existing query compiler. (b) Programmers need to provide just a few hundred lines of high-level code for implementing the optimizations, instead of complicated low-level code that is required by existing query compilation approaches. (c) The compilation overhead is low compared to the overall execution time, thus making our approach usable in practice for compiling query engines

O'Mega: An Optimizing Matrix Element Generator

We sketch the architecture of O'Mega, a new optimizing compiler for tree amplitudes in quantum field theory, and briefly describe its usage. O'Mega generates the most efficient code currently available for scattering amplitudes for many polarized particles in the Standard Model and its extensions.Comment: 29 pages, LaTe

Revolutionizing bantu lexicography: a Zulu case study

Zulu uses a conjunctive writing system, that is, a system whereby relatively short linguistic words are joined together to form long orthographic words with complex morphological structures. This has led to the so-called 'stem tradition in dictionary making for Zulu, as well as for most other Bantu languages. Given this lemmatization approach has been found to be inadequate for young learners (who fail to isolate stems), the development of a new approach was imperative for them, but until recently deemed impossible to implement. In this paper it is argued that it is now perfectly possible to reverse the unproductive trend, and to opt for the lemmatization of full words for all but one of the word classes in Bantu. This revolution is made possible thanks to the recent availability of relatively large corpora, with which the really frequent citation options may be pinpointed. Rather than a mission statement, this paper offers the result for all word classes. To do so, an actual guide to the use of a Zulu dictionary is re-represented and annotated

Program automated documentation methods

The mission analysis and trajectory simulation program is summarized; it provides an understanding of the size and complexity of one simulation for which documentation is mandatory. Programs for automating documentation of subroutines, flow charts, and internal cross reference information are also included