Run-time Feedback Research Articles

Leveraging Partially Context-Sensitive Profiles for Enhanced AOT Compilation: A Review

In the realm of compiler optimization, just-in-time (JIT) compilation dynamically adjusts code execution based on runtime profiling, contrasting with the static approach of ahead-of-time (AOT) compilation. While JIT benefits from real-time profiling data, AOT lacks this advantage, necessitating innovative strategies to enhance performance without runtime feedback. This review article explores the integration of partially context-sensitive profiles into AOT compilation, offering insights into optimizing statically compiled programs through advanced profiling techniques. Also, it explores the utilization of partially context-sensitive profiles in ahead-of-time (AOT) compilation to enhance program performance. It delves into the challenges of AOT optimization without runtime profiling, contrasting it with the dynamic optimization capabilities of just-in-time (JIT) compilation. The proposed algorithm strategically leverages partial profiles to identify and optimize hot code segments, presenting a promising avenue for improving AOT compilation efficiency. Through empirical evaluation of diverse benchmarks, the article validates the technique's effectiveness, underscoring its significance in advancing compiler optimization strategies for statically compiled programs.

Towards interactive explanation-based nutrition virtual coaching systems

The awareness about healthy lifestyles is increasing, opening to personalized intelligent health coaching applications. A demand for more than mere suggestions and mechanistic interactions has driven attention to nutrition virtual coaching systems (NVC) as a bridge between human–machine interaction and recommender, informative, persuasive, and argumentation systems. NVC can rely on data-driven opaque mechanisms. Therefore, it is crucial to enable NVC to explain their doing (i.e., engaging the user in discussions (via arguments) about dietary solutions/alternatives). By doing so, transparency, user acceptance, and engagement are expected to be boosted. This study focuses on NVC agents generating personalized food recommendations based on user-specific factors such as allergies, eating habits, lifestyles, and ingredient preferences. In particular, we propose a user-agent negotiation process entailing run-time feedback mechanisms to react to both recommendations and related explanations. Lastly, the study presents the findings obtained by the experiments conducted with multi-background participants to evaluate the acceptability and effectiveness of the proposed system. The results indicate that most participants value the opportunity to provide feedback and receive explanations for recommendations. Additionally, the users are fond of receiving information tailored to their needs. Furthermore, our interactive recommendation system performed better than the corresponding traditional recommendation system in terms of effectiveness regarding the number of agreements and rounds.

Real-time automatic image-based slice tracking of gadolinium-filled balloon wedge catheter during MR-guided cardiac catheterization: A proof-of-concept study.

MR-guided cardiac catheterization procedures currently use passive tracking approaches to follow a gadolinium-filled catheter balloon during catheter navigation. This requires frequent manual tracking and repositioning of the imaging slice during navigation. In this study, a novel framework for automatic real-time catheter tracking during MR-guided cardiac catheterization is presented. The proposed framework includes two imaging modes (Calibration and Runtime). The sequence starts in Calibration mode, in which the 3D catheter coordinates are determined using a stack of 10-20 contiguous saturated slices combined with real-time image processing. The sequence then automatically switches to Runtime mode, where three contiguous slices (acquired with partial saturation), initially centered on the catheter balloon using the Calibration feedback, are acquired continuously. The 3D catheter balloon coordinates are estimated in real time from each Runtime slice stack using image processing. Each Runtime stack is repositioned to maintain the catheter balloon in the central slice based on the prior Runtime feedback. The sequence switches back to Calibration mode if the catheter is not detected. This framework was evaluated in a heart phantom and 3 patients undergoing MR-guided cardiac catheterization. Catheter detection accuracy and rate of catheter visibility were evaluated. The automatic detection accuracy for the catheter balloon during the Calibration/Runtime mode was 100%/95% in phantom and 100%/97 ± 3% in patients. During Runtime, the catheter was visible in 82% and 98 ± 2% of the real-time measurements in the phantom and patients, respectively. The proposed framework enabled real-time continuous automatic tracking of a gadolinium-filled catheter balloon during MR-guided cardiac catheterization.

Demonstrating ADOPT: Adaptively Optimizing Attribute Orders for Worst-Case Optimal Joins via Reinforcement Learning

Performance of worst-case optimal join algorithms depends on the order in which the join attributes are processed. It is challenging to identify suitable orders prior to query execution due to the huge search space of possible orders and unreliable execution cost estimates in case of data skew or data correlation. We demonstrate ADOPT, a novel query engine that integrates adaptive query processing with a worst-case optimal join algorithm. ADOPT divides query execution into episodes, during which different attribute orders are invoked. With runtime feedback on performance of different attribute orders, ADOPT rapidly approaches near-optimal orders. Moreover, ADOPT uses a unique data structure which keeps track of the processed input data to prevent redundant work across different episodes. It selects attribute orders to try via reinforcement learning, balancing the need for exploring new orders with the desire to exploit promising orders. In experiments, ADOPT outperforms baselines, including commercial and open-source systems utilizing worst-case optimal join algorithms, particularly for complex queries that are difficult to optimize.

ADOPT: Adaptively Optimizing Attribute Orders for Worst-Case Optimal Join Algorithms via Reinforcement Learning

The performance of worst-case optimal join algorithms depends on the order in which the join attributes are processed. Selecting good orders before query execution is hard, due to the large space of possible orders and unreliable execution cost estimates in case of data skew or data correlation. We propose ADOPT, a query engine that combines adaptive query processing with a worst-case optimal join algorithm, which uses an order on the join attributes instead of a join order on relations. ADOPT divides query execution into episodes in which different attribute orders are tried. Based on run time feedback on attribute order performance, ADOPT converges quickly to near-optimal orders. It avoids redundant work across different orders via a novel data structure, keeping track of parts of the join input that have been successfully processed. It selects attribute orders to try via reinforcement learning, balancing the need for exploring new orders with the desire to exploit promising orders. In experiments with various data sets and queries, it outperforms baselines, including commercial and open-source systems using worst-case optimal join algorithms, whenever queries become complex and therefore difficult to optimize.

SkinnerMT

SkinnerMT is an adaptive query processing engine, specialized for multi-core platforms. SkinnerMT features different strategies for parallel processing that allow users to trade between average run time and performance robustness.First, SkinnerMT supports execution strategies that execute multiple query plans in parallel, thereby reducing the risk to find near-optimal plans late and improving robustness. Second, SkinnerMT supports data-parallel processing strategies. Its parallel multi-way join algorithm is sensitive to the assignment from tuples to threads. Here, SkinnerMT uses a cost-based optimization strategy, based on runtime feedback. Finally, SkinnerMT supports hybrid processing methods, mixing parallel search with data-parallel processing.The experiments show that parallel search increases robustness while parallel processing increases average-case performance. The hybrid approach combines advantages from both. Compared to traditional database systems, SkinnerMT is preferable for benchmarks where query optimization is hard. Compared to prior adaptive processing baselines, SkinnerMT exploits parallelism better.

Analytical Model for Interface Traps-Dependent Back Bias Capability and Variability in Ultrathin Body and Box FDSOI MOSFETs

Independent back bias in the ultrathin body and Box (UTBB) fully depleted silicon-on-insulator (FDSOI) serves as the critical knob for exploiting the performance and power tradeoffs and process/aging compensation. The effectiveness of back bias is one of the major metrics in the stages of circuit design and runtime feedback. In this article, new closed-form expressions accounting for the interface traps and short channel effects-dependent back gate capability in the UTBB FDSOI device are proposed. The developed model allows the accurate assessment of body factor ( $\gamma $ ) with the dependence of front/back interface trap density in the ultrascaled FDSOI device, taking the impact of gate area scaling as well as quantum effects into account. It also facilitates the evaluation of back bias effectiveness variability induced by interface traps and benefits the dynamic threshold voltage strategy in terms of adaptive back-biasing for performance optimization, which is consistent with 3-D device simulations and measurements. Moreover, the results confirm that $\gamma $ variability follows Pelgrom’s rule.

LAWS: Locality-AWare Scheme for Automatic Speech Recognition

Automatic Speech Recognition (ASR) systems are changing the way people interact with different applications on mobile devices. Fulfilling such user-interactivity requires not only a highly accurate, large-vocabulary recognition system, but also a real-time, energy-efficient solution. However, these ASR systems need high memory bandwidth and power budget, which may be impractical for most of small form-factor battery-operated devices. In this article, we propose two combined techniques implemented on top of a state-of-the-art ASR accelerator in order to significantly reduce its energy consumption and memory requirements. First, by leveraging the locality among consecutive segments of the speech signal, we develop a Locality-AWare-Scheme (LAWS) which exploits the on-chip recently-explored data while removing most of the off-chip accesses during the ASR's decoding process. As a result, we remove up to 60 percent of ASR's workload. As the second step, we introduce an approach to improve LAWS's effectiveness by selectively adapting the amount of ASR's workload, based on run-time feedback. In particular, we exploit the fact that the confidence of the ASR system varies along the recognition process. When confidence is high, the ASR system can be more restrictive and reduce the amount of work. The end design including both techniques provides a saving of more than 87 percent in the memory requests and 2.3x reduction in energy consumption, and a speedup of 2.1x with respect to a state-of-the-art baseline design.

Integrated Synthesis and Execution of Optimal Plans for Multi-Robot Systems in Logistics

Model-based synthesis allows to generate plans to achieve high-level tasks while satisfying certain properties of interest. However, when such plans are executed on concrete systems, several modeling assumptions may be challenged, jeopardizing their real applicability. This paper presents an integrated system for generating, executing and monitoring optimal-by-construction plans for multi-robot systems. This system unites the power of Optimization Modulo Theories with the flexibility of an on-line executive, providing optimal solutions for high-level task planning, and runtime feedback on their feasibility. After presenting how our system orchestrates static and runtime components, we demonstrate its capabilities using the RoboCup Logistics League as testbed. We do not only present our final solution but also its chronological development, and draw some general observations for the development of OMT-based approaches.

A heuristic transition executability analysis method for generating EFSM-specified protocol test sequences

Automatic executable test sequence generation is a challenging task for protocol test based on the Extended Finite State Machine (EFSM) model. Transition executability analysis (TEA) can guarantee the executability of generated test sequences. However, as a reachability analysis technique, it often suffers from the state explosion problem. To remedy the situation, this paper proposes a heuristic transition executability analysis (HTEA) method for compressing the explored state space during test sequences derivation. In our approach, a transition feasibility guidance matrix is first constructed through extracting the inherent constraint relationship between each adjacent transitions in the EFSM under testing. Then, relying on the built matrix and related runtime feedback, we define an objective function to dynamically evaluate state nodes in the HTEA expanding tree. Finally, the proposed objective function is utilized to heuristically guide the state space traversal in the HTEA tree for executable test sequences generation. Consequently, the executable test generation problem can be reformulated into a multi-objective search problem based on a HTEA tree. Experimental results impressively show the potential of the novel method for avoiding the state explosion during test generation, compared with the classic TEA technique in the breath-first way.

Collective Crowd Formation Transform with Mutual Information–Based Runtime Feedback

AbstractThis paper introduces a new crowd formation transform approach to achieve visually pleasing group formation transition and control. Its core idea is to transform crowd formation shapes with a least effort pair assignment using the Kuhn–Munkres algorithm, discover clusters of agent subgroups using affinity propagation and Delaunay triangulation algorithms and apply subgroup‐based social force model (SFM) to the agent subgroups to achieve alignment, cohesion and collision avoidance. Meanwhile, mutual information of the dynamic crowd is used to guide agents' movement at runtime. This approach combines both macroscopic (involving least effort position assignment and clustering) and microscopic (involving SFM) controls of the crowd transformation to maximally maintain subgroups' local stability and dynamic collective behaviour, while minimizing the overall effort (i.e. travelling distance) of the agents during the transformation. Through simulation experiments and comparisons, we demonstrate that this approach is efficient and effective to generate visually pleasing and smooth transformations and outperform several existing crowd simulation approaches including reciprocal velocity avoidances, optimal reciprocal collision avoidance and OpenSteer.

一种基于扩展有限状态机的自动化测试用例生成方法

Extended finite state machine (EFSM) is among the most popular models for model-based testing. However, automated test case generation on EFSM models is still a challenging task since an EFSM model may contains infeasible paths. This paper proposed a novel approach (ATGEM) to generate test case and construct oracle information from EFSM automatically. To address the infeasible problem, a metric based on data flow analysis is presented to predict the infeasible probability so as to bypass the infeasible paths as far as possible and improve the test case generation efficiency. Afterwards, an executable EFSM model is developed to obtain runtime feedback information as a fitness function in order to generate test data and construct oracle information automatically. This approach, which can generate various types data and has a wide range of applications, combines static analysis and dynamic analysis aims to find a preferable feasible path subset to generate test cases and meet adequacy coverage criteria. The experimental results on several EFSM models show that test case generation method and path feasibility metric have good effectiveness. Utilizing path feasibility metric can speed up the process of test case generation greatly, and ATGEM is more efficient than existing method.

Towards power-elastic systems through concurrency management

This study describes power-elastic systems, a method for designing systems whose operations are limited by applicable power. Departing from the traditional low-power design approach which minimises the power consumption for given amounts of computation throughput, power-elastic design focuses on the maximally effective use of applicable power. Centred on a run-time feedback control architecture, power-elastic systems manage their computation loads according to applicable power constraints, effectively viewing quantities of power as resources to be distributed among units of computation. Concurrency management is demonstrated as an effective means of implementing such run-time control, through both theoretical and numerical investigations. Several concurrency management techniques are studied and the effectiveness of arbitration for dynamic concurrency management with minimal prior system knowledge is demonstrated. A new type of arbitration, called soft arbitration, particularly suitable for managing the access of flexible resources such as power, is developed and proved.

PMU Guided Structure Data-Layout Optimization

Existing methods of obtaining runtime feedback for structure data-layout optimization have several drawbacks, such as large overhead and difficulty composing training sets. As a result, structure data-layout optimization is not widely used. To overcome these drawbacks, a performance monitoring unit (PMU) sampling method was developed with much less overhead and better portability and usability. An algorithm was developed to correct incomplete and inaccurate PMU sampling. With the corrected PMU feedback, a structure data-layout optimizer achieved a 45.1% performance improvement compared to a design without data-layout optimization, which is 97.6% of the performance improvement achieved with instrumented feedback. Calculation of the PMU feedback increased the execution time by 12.3%, compared to the overhead for the instrumented feedback of 341.5%. Tests show that the PMU feedback is efficient and effective for structure data-layout optimization.

The influence of stress and mood on decision making in critical situations. To what extent can we predict human performance?

In this position paper a number of hypotheses are outlined concerning the effect of three measurable human factors, namely subjective stress, arousal and mood, on human decision making performance; taking into account the amount of risk involved in the decision. The proposed domain of application involves critical situations: situations in which time-limits, uncertainty and possibly dire consequences provide an ideal context to apply the results. A tangible objective is to provide the basis for a demonstrator which can measure subjective stress, arousal and mood on the job, provide runtime feedback and positively influence the human decision making process.

Dynamic binary translation using run-time feedbacks

Dynamic binary translation is the process of translating, modifying and rewriting executable (binary) code from one machine to another at run-time. This process of low-level re-engineering consists of a reverse engineering phase followed by a forward engineering phase. UQDBT, the University of Queensland Dynamic Binary Translator, is a machine-adaptable translator. Adaptability is provided through the specification of properties of machines and their instruction sets, allowing the support of different pairs of source and target machines. Most binary translators are closely bound to a pair of machines, making analyses and code hard to reuse. Like most virtual machines, UQDBT performs generic optimizations that apply to a variety of machines. Frequently executed code is translated to native code by the use of edge weight instrumentation, which makes UQDBT converge more quickly than systems based on instruction speculation. In this paper, we describe the architecture and run-time feedback optimizations performed by the UQDBT system, and provide results obtained in the x86 and SPARC ® platforms.

SableSpMT

Speculative multithreading (SpMT) is a promising optimisation technique for achieving faster execution of sequential programs on multiprocessor hardware. Analysis of and data acquisition from such systems is however difficult and complex, and is typically limited to a specific hardware design and simulation environment. We have implemented a flexible, software-based speculative multithreading architecture within the context of a full-featured Java virtual machine. We consider the entire Java language and provide a complete set of support features for speculative execution, including return value prediction. Using our system we are able to generate extensive dynamic analysis information, analyse the effects of runtime feedback, and determine the impact of incorporating static, offline information. Our approach allows for accurate analysis of Java SpMT on existing, commodity multiprocessor hardware, and provides a vehicle for further experimentation with speculative approaches and optimisations.

Energy-aware wireless systems with adaptive power-fidelity tradeoffs

Wireless networked embedded systems, such as multimedia terminals, sensor nodes, etc., present a rich domain for making energy/performance/quality tradeoffs based on application needs, network conditions, etc. Energy awareness in these systems is the ability to perform tradeoffs between available battery energy and application quality requirements. In this paper, we show how operating system directed dynamic voltage scaling and dynamic power management can provide for such a capability. We propose a real-time scheduling algorithm that uses runtime feedback about application behavior to provide adaptive power-fidelity tradeoffs. We demonstrate our approach in the context of a static priority-based preemptive task scheduler. Simulation results show that the proposed algorithm results in significant energy savings compared to state-of-the-art dynamic voltage scaling schemes with minimal loss in system fidelity. We have implemented our scheduling algorithm into the eCos real-time operating system running on an Intel XScale-based variable voltage platform. Experimental results obtained using this platform confirm the effectiveness of our technique

Managing Web server performance with AutoTune agents

Managing the performance of e-commerce sites is challenging. Site content changes frequently, as do customer interests and business plans, contributing to dynamically varying workloads. To maintain good performance, system administrators must tune their information technology environment on an ongoing basis. Unfortunately, doing so requires considerable expertise and increases the total cost of system ownership. In this paper, we propose an agent-based solution that not only automates the ongoing system tuning but also automatically designs an appropriate tuning mechanism for the target system. We illustrate this in the context of managing a Web server. There we study the problem of controlling CPU and memory utilization of an Apache® Web server using the application-level tuning parameters MaxClients and KeepAlive, which are exposed by the server. Using the AutoTune agent framework under the Agent Building and Learning Environment (ABLE), we construct agents to fully automate a control-theoretic methodology that involves model building, controller design, and run-time feedback control. Specifically, we design (1) a modeling agent that builds a dynamic system model from the controlled server run data, (2) a controller design agent that uses optimal control theory to derive a feedback control algorithm customized to that server, and (3) a run-time control agent that deploys the feedback control algorithm in an on-line real-time environment to automatically manage the Web server. The designed autonomic feedback control system is able to handle the dynamic and interrelated dependencies between the tuning parameters and the performance metrics with guaranteed stability from control theory. The effectiveness of the AutoTune agents is demonstrated through experiments involving variations in workload, server capacity, and business objectives. The results also serve as a validation of the ABLE toolkit and the AutoTune agent framework.