Run-time profiling of software applications is key to energy efficiency. Even the most optimized hardware combined to an optimally designed software may become inefficient if operated poorly. Moreover, the diversification of modern computing platforms and broadening of their run-time configuration space make the task of optimally operating software ever more complex. With the growing financial and environmental impact of data center operation and cloud-based applications, optimal software operation becomes increasingly more relevant to existing and next-generation workloads. In order to guide software operation towards energy savings, energy and performance data must be gathered to provide a meaningful assessment of the application behavior under different system configurations, which is not appropriately addressed in existing tools. In this work we present Containergy, a new performance evaluation and profiling tool that uses software containers to perform application run-time assessment, providing energy and performance profiling data with negligible overhead (below 2%). It is focused on energy efficiency for next generation workloads. Practical experiments with emerging workloads, such as video transcoding and machine-learning image classification, are presented. The profiling results are analyzed in terms of performance and energy savings under a Quality-of-Service (QoS) perspective. For video transcoding, we verified that wrong choices in the configuration space can lead to an increase above 300% in energy consumption for the same task and operational levels. Considering the image classification case study, the results show that the choice of the machine-learning algorithm and model affect significantly the energy efficiency. Profiling datasets of AlexNet and SqueezeNet, which present similar accuracy, indicate that the latter represents 55.8% in energy saving compared to the former.

High-performance computing systems have become increasingly dynamic, complex, and unpredictable. To help build software that uses full-system capabilities, performance measurement and analysis tools exploit extensive execution analysis focusing on single-run results. Despite being effective in identifying performance hotspots and bottlenecks, these tools are not sufficiently suitable to evaluate the overall scalability trends of parallel applications. Either they lack the support for combining data from multiple runs or collect excessive data, causing unnecessary overhead. In this work, we present a tool for automatically measuring and comparing several executions of a parallel application according to various scenarios characterized by the input arrangements, the number of threads, number of cores, and frequencies. Unlike other existing performance analysis tools, the proposed work covers some gaps in specialized features necessary to better understand computational resources scalability trends across configurations. In order to improve scalability analysis and productivity over the vast spectrum of possible configurations, the proposed tool features automatic instrumentation, direct mapping of parallel regions, accuracy-preserving data reductions, and ease of use. As it aims at accurately understanding scalability trends of parallel applications, detailed single-run performance analyses show minimal intrusion (less than 1% overhead).

In conventional inertial navigation systems, accelerometers are used to recognize linear acceleration and gyroscopes are used to recognize the angular velocity during the movement of a rigid body in order to identify its displacement. However, the gyroscope is a device of larger cost and larger power consumption when compared to the accelerometer. This paper proposes a scheme for constructing an inertial measurement unit using only signals from three triaxial accelerometers to identify the displacement of the body. The sensors can be assembled and distributed in a single plane of a coordinate system, facilitating the configuration of these devices and subsequent construction of a device capable of measuring the displacement of a rigid body. The installation of the acceleration sensors on the same plane enables the deployment of the devices on integrated circuit boards.

High-performance computing systems have become increasingly large, dynamic, and complex, making building software that uses full-system resources difficult. While established profiling tools are excellent at identifying performance hotspots and bottlenecks in single runs, they are generally unsuitable for evaluating an application’s overall scalability trends across multiple configurations. This work presents PaScal Suite, a set of tools for automatically measuring and collecting data from the execution of a parallel application with different input arrangements and numbers of processing units in a shared-memory system. In addition to manual instrumentation, the analyzer tool automatically instruments code regions for investigation. It employs trace-based measurement techniques to identify boundaries and collect the required metrics to calculate efficiency values for the regions of interest in each execution condition, with minimal overhead. The viewer tool uses color maps to show variations in efficiency values, highlighting possible hierarchical scalability trends per region. We show that the analyzer tool is minimally intrusive and demonstrate that the suite supports the development and optimization of scalable parallel applications. By combining this automated multi-run workflow with a visual paradigm designed to reveal scalability trends, PaScal Suite offers a highly productive approach to analysis and optimization.