NOTICE: A Framework for Non-functional Testing of Compilers

International audience—Generally, compiler users apply different optimizations to generate efficient code with respect to non-functional properties such as energy consumption, execution time, etc. However, due to the huge number of optimizations provided by modern compilers, finding the best optimization sequence for a specific objective and a given program is more and more challenging. This paper proposes NOTICE, a component-based framework for non-functional testing of compilers through the monitoring of generated code in a controlled sand-boxing environment. We evaluate the effectiveness of our approach by verifying the optimizations performed by the GCC compiler. Our experimental results show that our approach is able to auto-tune compilers according to user requirements and construct optimizations that yield to better performance results than standard optimization levels. We also demonstrate that NOTICE can be used to automatically construct optimization levels that represent optimal trade-offs between multiple non-functional properties such as execution time and resource usage requirements

Coevolution of Firm Capabilities and Industry Competition

This paper proposes that rival firms not only search for new capabilities within their organization, but also for those that rest in their competitive environment. An integrated analysis of these search processes at both firm and industry levels of analysis shows how their interaction makes industries and firms coevolve over time. To contribute to an enhanced understanding of the concept of coevolution, a dynamic and integrative framework crossing meso and micro levels of analysis is constructed. This framework is applied to a longitudinal study of the music industry with a time-span of 120 years. The first part, a historical study, covers the period 1877 - 1990. The second part, a multiple-case study, covers the period 1990 - 1997. We conclude that search behavior drives coevolution through competitive dynamics among new entrants and incumbent firms and manifests itself in the simultaneous emergence of new business models and new organizational forms.coevolution;competitive regime;longitudinal research;multilevel research;music industry

NOTICE: A Framework for Non-functional Testing of Compilers

International audience—Generally, compiler users apply different optimizations to generate efficient code with respect to non-functional properties such as energy consumption, execution time, etc. However, due to the huge number of optimizations provided by modern compilers, finding the best optimization sequence for a specific objective and a given program is more and more challenging. This paper proposes NOTICE, a component-based framework for non-functional testing of compilers through the monitoring of generated code in a controlled sand-boxing environment. We evaluate the effectiveness of our approach by verifying the optimizations performed by the GCC compiler. Our experimental results show that our approach is able to auto-tune compilers according to user requirements and construct optimizations that yield to better performance results than standard optimization levels. We also demonstrate that NOTICE can be used to automatically construct optimization levels that represent optimal trade-offs between multiple non-functional properties such as execution time and resource usage requirements

Coevolution of Firm Capabilities and Industry Competition

This paper proposes that rival firms not only search for new capabilities within their organization, but also for those that rest in their competitive environment. An integrated analysis of these search processes at both firm and industry levels of analysis shows how their interaction makes industries and firms coevolve over time. To contribute to an enhanced understanding of the concept of coevolution, a dynamic and integrative framework crossing meso and micro levels of analysis is constructed. This framework is applied to a longitudinal study of the music industry with a time-span of 120 years. The first part, a historical study, covers the period 1877 - 1990. The second part, a multiple-case study, covers the period 1990 - 1997. We conclude that search behavior drives coevolution through competitive dynamics among new entrants and incumbent firms and manifests itself in the simultaneous emergence of new business models and new organizational forms

Benchmarking optimization algorithms for auto-tuning GPU kernels

Recent years have witnessed phenomenal growth in the application, and capabilities of Graphical Processing Units (GPUs) due to their high parallel computation power at relatively low cost. However, writing a computationally efficient GPU program (kernel) is challenging, and generally only certain specific kernel configurations lead to significant increases in performance. Auto-tuning is the process of automatically optimizing software for highly-efficient execution on a target hardware platform. Auto-tuning is particularly useful for GPU programming, as a single kernel requires re-tuning after code changes, for different input data, and for different architectures. However, the discrete, and non-convex nature of the search space creates a challenging optimization problem. In this work, we investigate which algorithm produces the fastest kernels if the time-budget for the tuning task is varied. We conduct a survey by performing experiments on 26 different kernel spaces, from 9 different GPUs, for 16 different evolutionary black-box optimization algorithms. We then analyze these results and introduce a novel metric based on the PageRank centrality concept as a tool for gaining insight into the difficulty of the optimization problem. We demonstrate that our metric correlates strongly with observed tuning performance.Comment: in IEEE Transactions on Evolutionary Computation, 202