On the effectiveness of phase based regression models to trade power and performance using dynamic processor adaptation

ABSTRACTThis study proposes adaptive decision making method having feed-back structure of regression and simulation models to support the quick decision making of production managers by managing and integrating the mutual relationship among historical data. For that, from historical data that have extracted and accumulated from each process, we first selected major constraint resources that are used as independent variables in regression model. The regression model is designed by using the dependent variables (objectives) that defined above by managers and independent variables selected in previous step and simulation model that are composed of constraint resources is designed. In process of simulation run, we obtain the multiple feasible solutions (alternatives) by using meta-heuristic method. Each solution is substituted by regression equation and we found the optimal solution that is minimum of difference between values obtained by regression model and simulation results. The optimal solution is delivered and incorporated to production site and current operation results from production site is used to generate new regression model after that time.

Java bytecodes can be executed with the following three methods: a Java interpreter running on a particular machine interprets bytecodes; a Just-in-Time (JIT) compiler translates bytecodes to the native primitives of the particular machine and the machine executes the translated codes; and a Java processor executes bytecodes directly. The first two methods require no special hardware support for the execution of Java bytecodes and are widely used currently. The last method requires an embedded Java processor, picoJavaI or picoJavaII for instance. The picoJavaI and picoJavaII are simple pipelined processors with no ILP (instruction level parallelism) and TLP (thread level parallelism) supports. A so-called MAJC (microprocessor architecture for Java computing) design can exploit ILP and TLP by using a modified VLIW (very long instruction word) architecture and vertical multithreading technique, but it has its own instruction set and cannot execute Java bytecodes directly. In this paper, we investigate a processor architecture which can directly execute Java bytecodes meanwhile can exploit Java ILP and TLP simultaneously. The proposed processor consists of multiple slots implementing horizontal multithreading and multiple functional units shared by all threads executed in parallel. Our architectural simulation results show that the Java processor could achieve an average 20 IPC (instructions per cycle), or 7.33 EIPC (effective IPC), with 8 slots and a 4-instruction scheduling window for each slot. We also check other configurations and give the utilization of functional units as well as the performance improvement with various kinds of working loads.

Background:Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that affects multiple organs and systems. However, in addition to physical damage, patients with SLE have an increased risk of mental...

The dramatic increase in the number of heterogeneous computing architectures (HCAs) which include CPUs, GPUs, TPUs, FPGAs, and new neuromorphic processors has transformed the high-performance computing (HPC) and artificial intelligence (AI) workloads. As the complexity of architecture increases there is corresponding uncertainty on reliability and performance checks, particularly when systems are incorporated with different processing elements, interconnections, and memory levels. Modern non-deterministic or non-stochastic heterogeneous environments are associated with traditional reliability models that feel great necessity to model nonlinear interdependencies between components. The current paper proposes an innovative AI-based modeling framework that would be capable of measuring system reliability and performance indicators in the context of HCAs. The suggested solution merges Deep Neural Networks (DNNs), Bayesian Inference Models, and the strategies of Reinforcement Learning (RL) to forecast the increase of the system reliability, the performance bottlenecks, and the mean time between failures (MTBF) at the system level. To train AI models that can capture the multi-domain interactions of computation, communication, and thermal dynamics, a large-scale simulation dataset was created with synthetic benchmarks, actual workloads (SPEC ACCEL, MLPerf, HPCG), unlikely fault profiles, and injected fault profiles to have AI models that are trained to simulate interactions in large-scale systems. The approach puts the feature engineering of runtime telemetry (e.g., power, temperature, utilization) and hardware counters (e.g., instruction-level parallelism, cache miss rates) to construct predictive and adaptive reliability estimators. As opposed to the traditional models, our AI-based system advances its state dynamically with online learning methods of training, allowing its internal state to identify fault and self-optimize it. Findings suggest that the suggested AI system can make accurate predictions in both system performance degradation and reliability estimates with a success level of 96.8 and 94.2 per cent respectively when operating in a heterogeneous environment. Comparative research with classical models of reliability, i.e. Markov and Weibull-based models reveal high gains on adaptability, precision and generalization. Moreover, the optimization using reinforcement learning provided a 1525 percentage ratio in task scheduling performance expressed in a limited thermal and power budget. This article adds to the increasing overlap between system modeling based on AI and the optimization of heterogeneous computing, sets the stage of the introduction of new intelligent reliability management of data centers, autonomous computing systems, and HPC infrastructure

Regression models are a form of supervised learning methods that are important for machine learning, statistics, and general data science. Despite the fact that classical ordinary least squares (OLS) regression models have been known for a long time, in recent years there are many new developments that extend this model significantly. Above all, the least absolute shrinkage and selection operator (LASSO) model gained considerable interest. In this paper, we review general regression models with a focus on the LASSO and extensions thereof, including the adaptive LASSO, elastic net, and group LASSO. We discuss the regularization terms responsible for inducing coefficient shrinkage and variable selection leading to improved performance metrics of these regression models. This makes these modern, computational regression models valuable tools for analyzing high-dimensional problems.

Graphics processing units (GPUs) 1 have enjoyed increasing popularity in recent years, which benefits from, for example, general-purpose GPU (GPGPU) for parallel programs and new computing paradigms, such as the Internet of Things (IoT). GPUs hold great potential in providing effective solutions for big data analytics while the demands for processing large quantities of data in real time are also increasing. However, the pervasive presence of GPUs on mobile devices presents great challenges for GPGPU, mainly because GPGPU integrates a large amount of processor arrays and concurrent executing threads (up to hundreds of thousands). In particular, the root causes of performance loss in a GPGPU program can not be revealed in detail by current approaches. In this article, we propose MiC (Multi-level Characterization), a framework that comprehensively characterizes GPGPU kernels at the instruction, Basic Block (BBL), and thread levels. Specifically, we devise Instruction Vectors (IV) and Basic Blocks Vectors (BBV), a Thread Similarity Matrix (TSM), and a Divergence Flow Statistics Graph (DFSG) to profile information in each level. We use MiC to provide insights into GPGPU kernels through the characterizations of 34 kernels from popular GPGPU benchmark suites such as Compute Unified Device Architecture (CUDA) Software Development Kit (SDK), Rodinia, and Parboil. In comparison with Central Processing Unit (CPU) workloads, we conclude the key findings as follows: (1) There are comparable Instruction-Level Parallelism (ILP); (2) The BBL count is significantly smaller than CPU workloads—only 22.8 on average; (3) The dynamic instruction count per thread varies from dozens to tens of thousands and it is extremely small compared to CPU benchmarks; (4) The Pareto principle (also called 90/10 rule) does not apply to GPGPU kernels while it pervasively exists in CPU programs; (5) The loop patterns are dramatically different from those in CPU workloads; (6) The branch ratio is lower than that of CPU programs but higher than pure GPU workloads. In addition, we have also shown how TSM and DFSG are used to characterize the branch divergence in a visual way, to enable the analysis of thread behavior in GPGPU programs. In addition, we show an optimization case for a GPGPU kernel from the bottleneck identified through its characterization result, which improves 16.8% performance.

Cloud environment is highly prone to failures due to its distributed nature and inherent complexity. Proactive identification of failures aids the service providers to avert these failures by taking corrective actions before they actually happen. In this paper, we analyze the resource usage patterns to identify failures due to resource contention in cloud. The resource usage and performance metrics of the working system are analyzed at regular time instants to model the normal and anomalous working behaviors. A two stage framework has been implemented where a hybrid of long short term memory (LSTM) and bidirectional long short term memory (BLSTM) is used to predict the future resource usage and performance metric values in the first stage. In the second stage, the hybrid model is used to classify the expected state as either normal or abnormal. We evaluate the proposed anomaly detection model in a virtual environment set up using Docker containers. The experimental results show that the proposed algorithm outperforms state-of-the-art algorithms.

Purpose Based on the conserved features of radiation response, we integrated the human and plant genomes to identify human ionizing radiation-responsive genes, aiming to identify novel radiation indicators and develop dose reconstruction models for radiation exposure assessment. Methods and materials We proposed a method employing homologous gene comparisons between 53 plant species and human genomes to identify the potential human ionizing radiation-responsive genes. Multiple linear regression models (optimized via stepwise regression), lasso regression model, ridge regression model, and elastic net regression model were constructed to predict radiation doses based on the expression profiles of these genes from four independent datasets. Model training and validation were performed using the leave-one-out-cross-validation (LOOCV) approach. The predictive performances were evaluated using the correlation coefficient (R) and root mean square error (RMSE). Results We identified a total of 39 plant-based human ionizing radiation-responsive genes as potential radiation indicators, comprising 23 previously known human genes and 16 potential candidates derived from plants. The linear model outperformed the other three models in radiation dose reconstruction across multiple radiation exposure scenarios, as evaluated by the performance metrics R and RMSE. The dose reconstruction models achieved high predictive accuracy for radiation exposure doses in both training and test sets at different dose rate conditions and time points after irradiation. Conclusions In conclusion, we identified a panel of human ionizing radiation-responsive genes as promising indicators and developed dose reconstruction models with potential applications in radiation exposure assessment. These findings provide a new strategy for expanding the pool of human ionizing radiation biomarkers and hold promise for improving dose estimation during radiological emergencies.

With the advent of GPU computing, profiling tools are now widely used to assist developers in identifying and solving performance bottlenecks. Those tools are commonly relying on hardware performance counters to grant users the access to low-level activities. Considering the increasing complexity of modern GPUs and also the efforts needed to associate program behaviors with hardware events, it is not trivial to construct the profiling tools with assured correctness. As a result, profiling tools should be strictly validated to make sure that the program behaviors and resource usages can be correctly captured and analyzed. To aid the validation, we create a testing prototype DELTA, on top of the open-source Radeon Open Compute platform (ROCm), to investigate the values of the derived profiling metrics and their underlying basic counters. The tests of DELTA are generally based on the classical microbenchmarks, which are capable to control program behaviors to generate predictable statistics. Differing from prior dissecting works, our tests are to examine the profiled results and to compare against desired patterns, reporting whether the profiling tools are correctly working with appropriate data collection and processing. This paper presents the validation methodology and experimental results of cache and main memory on the recent GPUs and ROCm platform, and the case studies demonstrate that the tests are helpful to scrutinize the profiling tools.

A review on EMG-based motor intention prediction of continuous human upper limb motion for human-robot collaboration

Predicting energy consumption of mosque buildings during the operation stage using deep learning approach

Increasingly, software should dynamically adapt its behavior at run-time in response to changing conditions in the supporting computing and communication infrastructure, and in the surrounding physical environment. In order for an adaptive program to be trusted, it is important to have mechanisms to ensure that the program functions correctly during and after adaptations. Adaptive programs are generally more difficult to specify, verify, and validate due to their high complexity. Particularly, when involving multi-threaded adaptations, the program behavior is the result of the collaborative behavior of multiple threads and software components. This paper introduces an approach to create formal models for the behavior of adaptive programs. Our approach separates the adaptation behavior and non-adaptive behavior specifications of adaptive programs, making the models easier to specify and more amenable to automated analysis and visual inspection. We introduce a process to construct adaptation models, automatically generate adaptive programs from the models, and verify and validate the models. We illustrate our approach through the development of an adaptive GSM-oriented audio streaming protocol for a mobile computing application.

*† ‡ We develop an approach for estimating model parameters which result in the “best distribution fit” between experimental and simulation data. Best distribution fit means matching moments of experimental data to those of a simulation (and possibly matching a full probability distribution). This approach extends typical nonlinear least squares methods which identify parameters maximizing agreement between experimental points and computational simulation results. Several analytic formulations for the distribution matching problem are provided, along with results for solving test problems and comparisons of this parameter estimation technique with a deterministic least squares approach. I. Introduction Nonlinear models are frequently used to model physical phenomena, including engineering applications. In this paper, we refer to a nonlinear model very broadly: the output of the model is a nonlinear function of the parameters [Draper98]. Thus, nonlinear models can include systems of partial differential equations (PDEs). Some examples include CFD (computational fluid dynamics), groundwater flow, heat transport, etc. Nonlinear models also include functional approximations of uncertain data via regression or response surface models. In most cases, we have some type of simulation model which is a nonlinear model, so we use the term nonlinear model and simulation model interchangeably in this paper. In addition to a simulation model, we assume that we have experimental data which may be used to calibrate the model. The calibration often requires the solution of an optimization problem to determine the optimal parameter settings for the simulation model. In this paper, we are concerned with identifying model parameters which result in a “best fit” between experimental data and simulation results in a nondeterministic context. That is, instead of matching point estimates we are concerned with matching moments (e.g., mean or variance) between experimental and simulation data where the variability in the model output is due to parametric uncertainty. We develop an approach extending the typical nonlinear least squares formulation to allow for this distribution matching. Note that “parameters” may be parameters in an approximation model such as a regression model, or physics modeling parameters which are used in physical simulation models such as PDEs. We distinguish data from parameters: data are physical data which are input either to a regression or physical simulation. For example, in groundwater flow modeling, hydraulic conductivity is a parameter and data may include measured flow rates from well tests. In this paper, we denote parameters that will be calibrated as θ, and the independent input data (e.g., state variables, configuration data, boundary conditions) as x. We also assume that there are uncertain variables, denoted by u, that represent inherent variability or lack of exact knowledge influencing the simulation, but which we cannot observe. The effect of the uncertain variables is reflected in both the output variability of the nonlinear model and the experimental data, but we can only explicitly account for these uncertain variables in the simulation model. Thus, our simulation model, f,

Finding a new drug which is both safe and efficient is an expensive and time-consuming endeavour. In particular, establishing the ‘dose-effect relationship’ – how beneficial a drug is at different dosages – can be challenging. Predicting this curve requires gathering experimental data by exposing and recording how cells respond to various levels of the drug. However, extreme values are often observed at low and high dosages, potentially introducing errors that are hard to correct in the prediction process. Yet, these extreme observations are sometimes genuine so researchers cannot just ignore them. To improve dose-effect estimation, Zhou, Liu, Fang et al. developed a new general-purpose approach. It uses advanced statistical modelling to account for extremes in lab data. This strategy outperformed other methods when dealing with these observations while also providing higher efficiency in data analysis with more uniform data in experiments. To facilitate implementation, Zhou, Liu, Fang et al. set up a user-friendly tool baptized ‘REAP’; this free online resource allows scientists without advanced statistical experience to harness the new approach and to perform dose-effect analysis more easily and accurately. This could boost research across many different disciplines that examine the effects of chemicals on cells.

The problem of atmospheric duct inversion is usually solved as a single objective optimization problem. Based on ground-based Global Positioning System (GPS) phase delay and propagation loss, this paper develops a multi-objective method including the effect of source frequency and receiving antenna height. The diversity and convergence of solution sets are evaluated for seven multi-objective evolutionary algorithms with three performance metrics: Hypervolume (HV), Inverted Generational Distance (IGD), and the averaged Hausdorff distance (). The inversion results are compared with the simulation results, and the experimental comparison is conducted on three groups of test situations. The results demonstrate that the ranking of algorithm performance varies because of the different methods used to calculate performance metrics. Moreover, when the algorithms show overwhelming performance using performance metrics, the inversion result is not more close to the real value. In the comparison of computational experiments, it was found that, as the retrieved parameter dimension increases, the inversion result becomes more unstable. When the observed data are sufficient, the inversion result seems to be improved.