Lost in translation: Exposing hidden compiler optimization opportunities

Existing iterative compilation and machine-learning-based optimization techniques have been proven very successful in achieving better optimizations than the standard optimization levels of a compiler. However, they were not engineered to support the tuning of a compiler's optimizer as part of the compiler's daily development cycle. In this paper, we first establish the required properties which a technique must exhibit to enable such tuning. We then introduce an enhancement to the classic nightly routine testing of compilers which exhibits all the required properties, and thus, is capable of driving the improvement and tuning of the compiler's common optimizer. This is achieved by leveraging resource usage and compilation information collected while systematically exploiting prefixes of the transformations applied at standard optimization levels. Experimental evaluation using the LLVM v6.0.1 compiler demonstrated that the new approach was able to reveal hidden cross-architecture and architecture-dependent potential optimizations on two popular processors: the Intel i5-6300U and the Arm Cortex-A53-based Broadcom BCM2837 used in the Raspberry Pi 3B+. As a case study, we demonstrate how the insights from our approach enabled us to identify and remove a significant shortcoming of the CFG simplification pass of the LLVM v6.0.1 compiler.Comment: 31 pages, 7 figures, 2 table. arXiv admin note: text overlap with arXiv:1802.0984

Safety in Numbers: Developing a Shared Analytics Services for Academic Libraries

Purpose It is clear that libraries consider the use of data to inform decision making a top priority in the next five years. Jisc’s considerable work on activity data has highlighted the lack of tools and services for libraries to exploit this data. The purpose of this paper is to explore the potential of a shared analytics service for UK academic libraries and introduce the Jisc Library Analytics and Metrics Project (LAMP). The project aims to help libraries effectively management collections and services as well as delivering pre-emptive indicators and ‘actionable insights’ to help identify new trends, personalise services and improve efficiencies, economies and effectiveness (student attainment and satisfaction and institutional reputation, for example) . The project builds on the Library Impact Data Project at the University of Huddersfield and the work of the Copac Activity Data and Collections Management tools. The paper will deliver a case study of the project, its progress to date, the challenges of such an approach and the implications the service has for academic libraries. Design, methodology or approach The paper will be a case study of the project and its institutional partners and early adopters work to date and explore both the technical and cultural challenges of the work as well as its implications for the role of the library within the institution and the services it provides. Specifically the case study will comprise of the following aspects: 1. A brief history of the work and the context of library analytics services in the UK (and internationally). A description of the approach adopted by the project, and the vision and goals of the project 2. Exploration of the challenges associated with the project. In particular the challenges around accessing and sharing the data, ‘warehousing’ and data infrastructure considerations and the design challenge of visualising the data sources in a useful and coherent way 3. Outline of the implications of the project and the resultant service. In particular the implications for benchmarking (within the UK and beyond), standards development for library statistics and impact (in particular the development of ISO 16439), service development, the role of the library within the wider institution and skills and expertise of librarians. Findings This paper will report on the initial findings of the project, which will run from January 2013 to October 2013. In particular it will consider the issues surfaced through the close engagement with the academic library community (through the projects community advisory and planning group) and the institutional early-adopters around data gathering and analysis. Practical implications Data accumulated in one context has the potential to inform decisions and interventions elsewhere. While there are a number of recognised and well understood use-cases for library analytics these tend to revolve around usage and collection management. Yet, the potential of a shared analytics service is in uncovering those links and indicators across diverse data sets. The paper will consider a number of practical impacts: Performance: benchmarking, student attainment, research productivity Design: fine tuning services, personalised support Trends: research landscape, student marketplace, utilisation of resources. The case study will explore these practical implications for libraries and what they mean for the future of the library within the academy. Originality and value of the proposal The paper will present a case study of a unique service that currently fills an important gap within the library analytics space. The paper will focus on the services potential to transform both the way the library works and how it is erceived by its users, as well as its role and relationship within the broader institution

Safety in Numbers: Developing a Shared Analytics Service for Academic Libraries

Purpose It is clear that libraries consider the use of data to inform decision making a top priority in the next five years. Jisc’s considerable work on activity data has highlighted the lack of tools and services for libraries to exploit this data. The purpose of this paper is to explore the potential of a shared analytics service for UK academic libraries and introduce the Jisc Library Analytics and Metrics Project (LAMP). The project aims to help libraries effectively management collections and services as well as delivering pre-emptive indicators and ‘actionable insights’ to help identify new trends, personalise services and improve efficiencies, economies and effectiveness (student attainment and satisfaction and institutional reputation, for example) . The project builds on the Library Impact Data Project at the University of Huddersfield and the work of the Copac Activity Data and Collections Management tools. The paper will deliver a case study of the project, its progress to date, the challenges of such an approach and the implications the service has for academic libraries. Design, methodology or approach The paper will be a case study of the project and its institutional partners and early adopters work to date and explore both the technical and cultural challenges of the work as well as its implications for the role of the library within the institution and the services it provides. Specifically the case study will comprise of the following aspects: 1. A brief history of the work and the context of library analytics services in the UK (and internationally). A description of the approach adopted by the project, and the vision and goals of the project 2. Exploration of the challenges associated with the project. In particular the challenges around accessing and sharing the data, ‘warehousing’ and data infrastructure considerations and the design challenge of visualising the data sources in a useful and coherent way 3. Outline of the implications of the project and the resultant service. In particular the implications for benchmarking (within the UK and beyond), standards development for library statistics and impact (in particular the development of ISO 16439), service development, the role of the library within the wider institution and skills and expertise of librarians. Findings This paper will report on the initial findings of the project, which will run from January 2013 to October 2013. In particular it will consider the issues surfaced through the close engagement with the academic library community (through the projects community advisory and planning group) and the institutional early-adopters around data gathering and analysis. Practical implications Data accumulated in one context has the potential to inform decisions and interventions elsewhere. While there are a number of recognised and well understood use-cases for library analytics these tend to revolve around usage and collection management. Yet, the potential of a shared analytics service is in uncovering those links and indicators across diverse data sets. The paper will consider a number of practical impacts: Performance: benchmarking, student attainment, research productivity Design: fine tuning services, personalised support Trends: research landscape, student marketplace, utilisation of resources. The case study will explore these practical implications for libraries and what they mean for the future of the library within the academy. Originality and value of the proposal The paper will present a case study of a unique service that currently fills an important gap within the library analytics space. The paper will focus on the services potential to transform both the way the library works and how it is erceived by its users, as well as its role and relationship within the broader institution

“A space for myself to go:” Early patterns in Small YA spaces

While young adults (teenagers) are routinely recognized as constituting nearly 25 percent of the nation\u27s public library users, the vast majority of libraries devote more space and design attention to restrooms than to young people. Worse, there are currently no consistent or established metrics, no evaluation criteria, few conceptual standards of best practices, and little consistency in the methods by which we collect empirical evidence about young adult (YA) spaces. This study is the first systematic attempt to both collect and analyze empirical data on libraries\u27 recent trend toward providing greater spatial equity for YA library service

Deep Learning for Single Image Super-Resolution: A Brief Review

Single image super-resolution (SISR) is a notoriously challenging ill-posed problem, which aims to obtain a high-resolution (HR) output from one of its low-resolution (LR) versions. To solve the SISR problem, recently powerful deep learning algorithms have been employed and achieved the state-of-the-art performance. In this survey, we review representative deep learning-based SISR methods, and group them into two categories according to their major contributions to two essential aspects of SISR: the exploration of efficient neural network architectures for SISR, and the development of effective optimization objectives for deep SISR learning. For each category, a baseline is firstly established and several critical limitations of the baseline are summarized. Then representative works on overcoming these limitations are presented based on their original contents as well as our critical understandings and analyses, and relevant comparisons are conducted from a variety of perspectives. Finally we conclude this review with some vital current challenges and future trends in SISR leveraging deep learning algorithms.Comment: Accepted by IEEE Transactions on Multimedia (TMM

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