ReuseTracker : Fast Yet Accurate Multicore Reuse Distance Analyzer

One widely used metric that measures data locality is reuse distance —the number of unique memory locations that are accessed between two consecutive accesses to a particular memory location. State-of-the-art techniques that measure reuse distance in parallel applications rely on simulators or binary instrumentation tools that incur large performance and memory overheads. Moreover, the existing sampling-based tools are limited to measuring reuse distances of a single thread and discard interactions among threads in multi-threaded programs. In this work, we propose ReuseTracker —a fast and accurate reuse distance analyzer that leverages existing hardware features in commodity CPUs. ReuseTracker is designed for multi-threaded programs and takes cache-coherence effects into account. By utilizing hardware features like performance monitoring units and debug registers, ReuseTracker can accurately profile reuse distance in parallel applications with much lower overheads than existing tools. It introduces only 2.9× runtime and 2.8× memory overheads. Our tool achieves 92% accuracy when verified against a newly developed configurable benchmark that can generate a variety of different reuse distance patterns. We demonstrate the tool’s functionality with two use-case scenarios using PARSEC, Rodinia, and Synchrobench benchmark suites where ReuseTracker guides code refactoring in these benchmarks by detecting spatial reuses in shared caches that are also false sharing and successfully predicts whether some benchmarks in these suites can benefit from adjacent cache line prefetch optimization.

CASE STUDY ON PERFORMANCE MONITORING AND STABILITY ANALYSIS OF BAISHIHU SUSPENSION BRIDGE AND SIDE SLOPE

The main structure of the Baishihu suspension bridge was connected to the anchor foundations by three main steel cables. The wooden pedestrian deck was fixed to the main steel cables using steel beams and was stabilized by two stabilizing cables. The stabilizing cables and bridge body were joined by 44 steel connecting rods. Therefore, the slope stability at the anchorage foundations of the main steel cables, as well as the performance monitoring and analysis of the main steel cables and stabilizing cables, are critical to the overall performance of the suspension bridge. This paper discusses the performance monitoring and analysis of the steel cable deflection and cable strength for this bridge, as well as the main considerations and results of the stability analysis of the bridge abutments and side slopes of the two banks. Water-level observation wells, inclinometers, and tiltmeters monitoring were used to record reference data for the analysis of the slope stability performance. Additionally, the three-dimensional dynamic analysis program VFIFE was used to analyze the deformation and motion of the bridge. The final steady-state results were used to compare the static design value and monitoring data. The dynamic response before the final steady state was also observed.

Long-Term Techno-Economic Performance Monitoring to Promote Built Environment Decarbonisation and Digital Transformation—A Case Study

Buildings’ long-term techno-economic performance monitoring is critical for benchmarking in order to reduce costs and environmental impact while providing adequate services. Reliable building stock performance data provide a fundamental knowledge foundation for evidence-based energy efficiency interventions and decarbonisation strategies. Simply put, an adequate understanding of building performance is required to reduce energy consumption, as well as associated costs and emissions. In this framework, Variable-base degree-days-based methods have been widely used for weather normalisation of energy statistics and energy monitoring for Measurement and Verification (M & V) purposes. The base temperature used to calculate degree-days is determined by building thermal characteristics, operation strategies, and occupant behaviour, and thus varies from building to building. In this paper, we develop a variable-base degrees days regression model, typically used for energy monitoring and M & V, using a “proxy” variable, the cost of energy services. The study’s goal is to assess the applicability of this type of model as a screening tool to analyse the impact of efficiency measures, as well as to understand the evolution of performance over time, and we test it on nine public schools in the Northern Italian city of Seregno. While not as accurate as M & V techniques, this regression-based approach can be a low-cost tool for tracking performance over time using cost data typically available in digital format and can work reasonably well with limited resolution, such as monthly data. The modelling methodology is simple, scalable and can be automated further, contributing to long-term techno-economic performance monitoring of building stock in the context of incremental built environment digitalization.

A Model for Performance Evaluation

As a result of the parallel element setting, performance assessment and model construction are constrained. Component functions should be observable without direct connections to programming language, for example. As a result of this, solutions that are constituted interactively at program execution necessitate recyclable performance-monitoring interactions. As a result of these restrictions, a quasi, coarse-grained Performance Evaluation (PE) approach is described in this paper. A performance framework for the application system can be polymerized from these data. To validate the evaluation and model construction techniques included in the validation framework, simplistic elements with well-known optimization models are employed.

Machine Learning Applications for Short Reach Optical Communication

With the rapid development of optical communication systems, more advanced techniques conventionally used in long-haul transmissions have gradually entered systems covering shorter distances below 100 km, where higher-speed connections are required in various applications, such as the optical access networks, inter- and intra-data center interconnects, mobile fronthaul, and in-building and indoor communications. One of the techniques that has attracted intensive interests in short-reach optical communications is machine learning (ML). Due to its robust problem-solving, decision-making, and pattern recognition capabilities, ML techniques have become an essential solution for many challenging aspects. In particular, taking advantage of their high accuracy, adaptability, and implementation efficiency, ML has been widely studied in short-reach optical communications for optical performance monitoring (OPM), modulation format identification (MFI), signal processing and in-building/indoor optical wireless communications. Compared with long-reach communications, the ML techniques used in short-reach communications have more stringent complexity and cost requirements, and also need to be more sensitive. In this paper, a comprehensive review of various ML methods and their applications in short-reach optical communications are presented and discussed, focusing on existing and potential advantages, limitations and prospective trends.

Machine Learning-Aided Optical Performance Monitoring Techniques: A Review

Future communication systems are faced with increased demand for high capacity, dynamic bandwidth, reliability and heterogeneous traffic. To meet these requirements, networks have become more complex and thus require new design methods and monitoring techniques, as they evolve towards becoming autonomous. Machine learning has come to the forefront in recent years as a promising technology to aid in this evolution. Optical fiber communications can already provide the high capacity required for most applications, however, there is a need for increased scalability and adaptability to changing user demands and link conditions. Accurate performance monitoring is an integral part of this transformation. In this paper, we review optical performance monitoring techniques where machine learning algorithms have been applied. Moreover, since many performance monitoring approaches in the optical domain depend on knowledge of the signal type, we also review work for modulation format recognition and bitrate identification. We additionally briefly introduce a neuromorphic approach as an emerging technique that has only recently been applied to this domain.