Envisioning Model-Based Performance Engineering Frameworks.

Abstract Our daily activities depend on complex software systems that must guarantee certain performance. Several approaches have been devised in the last decade to validate software systems against performance requirements. However, software designers still encounter problems in the interpretation of performance analysis results (e.g., mean values, probability distribution functions) and in the definition of design alternatives (e.g., to split a software component in two and redeploy one of them) aimed at fulfilling performance requirements. This paper describes a general model-based performance engineering framework to support designers in dealing with such problems aimed at enhancing the system. The framework relies on a formalization of the knowledge needed in order to characterize performance flaws and provide alternative system design. Such knowledge can be instantiated based on the techniques devised for interpreting performance analysis results and providing feedback to designers. Three techniques are considered in this paper for instantiating the framework and the main challenges to face during such process are pointed out and discussed

Biologically Plausible Learning on Neuromorphic Hardware Architectures

With an ever-growing number of parameters defining increasingly complex networks, Deep Learning has led to several breakthroughs surpassing human performance. As a result, data movement for these millions of model parameters causes a growing imbalance known as the memory wall. Neuromorphic computing is an emerging paradigm that confronts this imbalance by performing computations directly in analog memories. On the software side, the sequential Backpropagation algorithm prevents efficient parallelization and thus fast convergence. A novel method, Direct Feedback Alignment, resolves inherent layer dependencies by directly passing the error from the output to each layer. At the intersection of hardware/software co-design, there is a demand for developing algorithms that are tolerable to hardware nonidealities. Therefore, this work explores the interrelationship of implementing bio-plausible learning in-situ on neuromorphic hardware, emphasizing energy, area, and latency constraints. Using the benchmarking framework DNN+NeuroSim, we investigate the impact of hardware nonidealities and quantization on algorithm performance, as well as how network topologies and algorithm-level design choices can scale latency, energy and area consumption of a chip. To the best of our knowledge, this work is the first to compare the impact of different learning algorithms on Compute-In-Memory-based hardware and vice versa. The best results achieved for accuracy remain Backpropagation-based, notably when facing hardware imperfections. Direct Feedback Alignment, on the other hand, allows for significant speedup due to parallelization, reducing training time by a factor approaching N for N-layered networks

A Cognitive Agent Computing-Based Model For The Primary School Student Migration Problem Using A Descriptive Agent-Based Approach

Students' migration from public to private schools, due to lack of school performance of public schools, is one of the major issues faced by the Government of Punjab to provide compulsory and quality education at low cost. Due to complex adaptive nature of educational system, interdependencies with society, constant feedback loops conventional linear regression methods, for evaluation of effective performance, are ineffective or costly to solve the issue. Linear regression techniques present the static view of the system, which are not enough to understand the complex dynamic nature of educational paradigm. We have presented a Cognitive Agent Computing-Based Model for the School Student Migration Problem Using a Descriptive Agent-Based Modeling approach to understand the causes-effects relationship of student migration. We have presented the primary school students' migration model using descriptive modeling approach along with exploratory modeling. Our research, in the context of Software Engineering of Simulation & Modeling, and exploring the Complex Adaptive nature of school system, is two folds. Firstly, the cause-effect relationship of students' migration is being investigated using Cognitive Descriptive Agent-Based Modeling. Secondly, the formalization extent of Cognitive Agent-Based Computing framework is analyzed by performing its comparative analysis with exploratory modeling protocol 'Overview, Design, and Detail'.Comment: 117 pages, MS thesi

Integrating Multiobjective Optimization With The Six Sigma Methodology For Online Process Control

Over the past two decades, the Define-Measure-Analyze-Improve-Control (DMAIC) framework of the Six Sigma methodology and a host of statistical tools have been brought to bear on process improvement efforts in today’s businesses. However, a major challenge of implementing the Six Sigma methodology is maintaining the process improvements and providing real-time performance feedback and control after solutions are implemented, especially in the presence of multiple process performance objectives. The consideration of a multiplicity of objectives in business and process improvement is commonplace and, quite frankly, necessary. However, balancing the collection of objectives is challenging as the objectives are inextricably linked, and, oftentimes, in conflict. Previous studies have reported varied success in enhancing the Six Sigma methodology by integrating optimization methods in order to reduce variability. These studies focus these enhancements primarily within the Improve phase of the Six Sigma methodology, optimizing a single objective. The current research and practice of using the Six Sigma methodology and optimization methods do little to address the real-time feedback and control for online process control in the case of multiple objectives. This research proposes an innovative integrated Six Sigma multiobjective optimization (SSMO) approach for online process control. It integrates the Six Sigma DMAIC framework with a nature-inspired optimization procedure that iteratively perturbs a set of decision variables providing feedback to the online process, eventually converging to a set of tradeoff process configurations that improves and maintains process stability. For proof of concept, the approach is applied to a general business process model – a well-known inventory management model – that is formally defined and specifies various process costs as objective functions. The proposed iv SSMO approach and the business process model are programmed and incorporated into a software platform. Computational experiments are performed using both three sigma (3σ)-based and six sigma (6σ)-based process control, and the results reveal that the proposed SSMO approach performs far better than the traditional approaches in improving the stability of the process. This research investigation shows that the benefits of enhancing the Six Sigma method for multiobjective optimization and for online process control are immense

Static and Dynamic Scheduling for Effective Use of Multicore Systems

Multicore systems have increasingly gained importance in high performance computers. Compared to the traditional microarchitectures, multicore architectures have a simpler design, higher performance-to-area ratio, and improved power efficiency. Although the multicore architecture has various advantages, traditional parallel programming techniques do not apply to the new architecture efficiently. This dissertation addresses how to determine optimized thread schedules to improve data reuse on shared-memory multicore systems and how to seek a scalable solution to designing parallel software on both shared-memory and distributed-memory multicore systems. We propose an analytical cache model to predict the number of cache misses on the time-sharing L2 cache on a multicore processor. The model provides an insight into the impact of cache sharing and cache contention between threads. Inspired by the model, we build the framework of affinity based thread scheduling to determine optimized thread schedules to improve data reuse on all the levels in a complex memory hierarchy. The affinity based thread scheduling framework includes a model to estimate the cost of a thread schedule, which consists of three submodels: an affinity graph submodel, a memory hierarchy submodel, and a cost submodel. Based on the model, we design a hierarchical graph partitioning algorithm to determine near-optimal solutions. We have also extended the algorithm to support threads with data dependences. The algorithms are implemented and incorporated into a feedback directed optimization prototype system. The prototype system builds upon a binary instrumentation tool and can improve program performance greatly on shared-memory multicore architectures. We also study the dynamic data-availability driven scheduling approach to designing new parallel software on distributed-memory multicore architectures. We have implemented a decentralized dynamic runtime system. The design of the runtime system is focused on the scalability metric. At any time only a small portion of a task graph exists in memory. We propose an algorithm to solve data dependences without process cooperation in a distributed manner. Our experimental results demonstrate the scalability and practicality of the approach for both shared-memory and distributed-memory multicore systems. Finally, we present a scalable nonblocking topology-aware multicast scheme for distributed DAG scheduling applications

A software framework for simulation studies of interaction models in agent teamwork.

This thesis proposes a new software framework that facilitates the study of agent interaction models in early development stages from a designer's perspective. Its purpose is to help reduced the design decision space through simulation experiments that provide early feedback on comparative performance of alternative solutions. This is achieved through interactive concurrent simulation of multiple teams in a representative microworld context. The generic simulator's architecture accommodates an open class of different microworlds and permits multiple communication mechanisms. It also supports interoperability with other software tools, distributed simulation, and various extensions. The framework was validated in the context of two different research projects on helpful behavior in agent teams: the Mutual Assistance Protocol, based on rational criteria for help, and the Empathic Help Model, based on a concept of empathy for artificial agents. The results show that the framework meets its design objectives and provides the flexibility needed for research experimentation. --Leaf i.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b184472

Hubungan antara tahap kompetensi dengan tahap prestasi kerja di kalangan pentadbir Kolej Universiti Teknologi Tun Hussein Onn (KUiTTHO)

This research is a case research in studying the connection of competency level with the administrator in Kolej University Teknologi Tun Hussein Onn (KUiTTHO). Research was done on 40 respondents which were identified playing an important role in giving the feedback. All the respondents are a public service officer from the management and professional group in KUiTTHO, Batu Pahat, Johor. Results from the research are gathered using the Statistical Package for Social Sciences (SPSS version 13.0) software. Model for the Managers Competency and KUiTTHO Annual Assessment Report Form are the main components in the establishing the research framework. As for the independent variable, competencies are divided into four clusters which is personal management, group management, working management and collaborative management. While the dependent variable which is job performance is divided into three dimensions which are working result, knowledge and ability and personal quality. Throughout the research the result shows that the competency level and job performance level for the KUiTTHO administrator are at the highest level. Based on Pearson correlation analysis, research result shows that the positive relationship exists is at the average level. However, competency levels are still playing a huge role in increasing the level of the administrator job performance in KUiTTHO

Program Merge Conflict Resolution via Neural Transformers

Collaborative software development is an integral part of the modern software development life cycle, essential to the success of large-scale software projects. When multiple developers make concurrent changes around the same lines of code, a merge conflict may occur. Such conflicts stall pull requests and continuous integration pipelines for hours to several days, seriously hurting developer productivity. To address this problem, we introduce MergeBERT, a novel neural program merge framework based on token-level three-way differencing and a transformer encoder model. By exploiting the restricted nature of merge conflict resolutions, we reformulate the task of generating the resolution sequence as a classification task over a set of primitive merge patterns extracted from real-world merge commit data. Our model achieves 63-68% accuracy for merge resolution synthesis, yielding nearly a 3x performance improvement over existing semi-structured, and 2x improvement over neural program merge tools. Finally, we demonstrate that MergeBERT is sufficiently flexible to work with source code files in Java, JavaScript, TypeScript, and C# programming languages. To measure the practical use of MergeBERT, we conduct a user study to evaluate MergeBERT suggestions with 25 developers from large OSS projects on 122 real-world conflicts they encountered. Results suggest that in practice, MergeBERT resolutions would be accepted at a higher rate than estimated by automatic metrics for precision and accuracy. Additionally, we use participant feedback to identify future avenues for improvement of MergeBERT.Comment: ESEC/FSE '22 camera ready version. 12 pages, 4 figures, online appendi

Modeling and debiasing feedback loops in collaborative filtering recommender systems.

Artificial Intelligence (AI)-driven recommender systems have been gaining increasing ubiquity and influence in our daily lives, especially during time spent online on the World Wide Web or smart devices. The influence of recommender systems on who and what we can find and discover, our choices, and our behavior, has thus never been more concrete. AI can now predict and anticipate, with varying degrees of accuracy, the news article we will read, the music we will listen to, the movies we will watch, the transactions we will make, the restaurants we will eat in, the online courses we will be interested in, and the people we will connect with for various ends and purposes. For all these reasons, the automated predictions and recommendations made by AI can lead to influencing and changing human opinions, behavior, and decision making. When the AI predictions are biased, the influences can have unfair consequences on society, ranging from social polarization to the amplification of misinformation and hate speech. For instance, bias in recommender systems can affect the decision making and shift consumer behavior in an unfair way due to a phenomenon known as the feedback loop. The feedback loop is an inherent component of recommender systems because the latter are dynamic systems that involve continuous interactions with the users, whereby data collected to train a recommender system model is usually affected by the outputs of a previously trained model. This feedback loop is expected to affect the performance of the system. For instance, it can amplify initial bias in the data or model and can lead to other phenomena such as filter bubbles, polarization, and popularity bias. Up to now, it has been difficult to understand the dynamics of recommender system feedback loops, and equally challenging to evaluate the bias and filter bubbles emerging from recommender system models within such an iterative closed loop environment. In this dissertation, we study the feedback loop in the context of Collaborative Filtering (CF) recommender systems. CF systems comprise the leading family of recommender systems that rely mainly on mining the patterns of interaction between the users and items to train models that aim to predict future user interactions. Our research contributions target three aspects of recommendation, namely modeling, debiasing and evaluating feedback loops. Our research advances the state of the art in Fairness in Artificial Intelligence on several fronts: (1) We propose and validate a new theoretical model, based on Martingale differences, to model the recommender system feedback loop, and allow a better understanding of the dynamics of filter bubbles and user discovery. (2) We propose a Transformer-based deep learning architecture and algorithm to learn diverse representations for users and items in order to increase the diversity in the recommendations. Our evaluation experiments on real world datasets demonstrate that our transformer model recommends 14\% more diverse items and improves the novelty of the recommendation by more than 20\%. (3) We propose a new simulation and experimentation framework that allows studying and tracking the evolution of bias metrics in a feedback loop setting, for a variety of recommendation modeling algorithms. Our preliminary findings, using the new simulation framework show that recommender systems are deeply affected by the feedback loop, and that without an adequate debiasing or exploration strategy, this feedback loop limits the discovery of the user and increases the disparity in exposure between items that can be recommended. To help the research and practice community in studying recommender system fairness, all the tools developed to model, debias, and evaluate recommender systems are made available to the public as open source software libraries \footnote{https://github.com/samikhenissi/TheoretUserModeling}. (4) We propose a novel learnable dynamic debiasing strategy that learns an optimal rescaling parameter for the predicted rating and achieves a better trade-off between accuracy and debiasing. We focus on solving the popularity bias of the items and test our method using our proposed simulation framework and show the effectiveness of using a learnable debiasing degree to produce better results