Exploring Task Mappings on Heterogeneous MPSoCs using a Bias-Elitist Genetic Algorithm

Exploration of task mappings plays a crucial role in achieving high performance in heterogeneous multi-processor system-on-chip (MPSoC) platforms. The problem of optimally mapping a set of tasks onto a set of given heterogeneous processors for maximal throughput has been known, in general, to be NP-complete. The problem is further exacerbated when multiple applications (i.e., bigger task sets) and the communication between tasks are also considered. Previous research has shown that Genetic Algorithms (GA) typically are a good choice to solve this problem when the solution space is relatively small. However, when the size of the problem space increases, classic genetic algorithms still suffer from the problem of long evolution times. To address this problem, this paper proposes a novel bias-elitist genetic algorithm that is guided by domain-specific heuristics to speed up the evolution process. Experimental results reveal that our proposed algorithm is able to handle large scale task mapping problems and produces high-quality mapping solutions in only a short time period.Comment: 9 pages, 11 figures, uses algorithm2e.st

An Online Decision-Theoretic Pipeline for Responder Dispatch

The problem of dispatching emergency responders to service traffic accidents, fire, distress calls and crimes plagues urban areas across the globe. While such problems have been extensively looked at, most approaches are offline. Such methodologies fail to capture the dynamically changing environments under which critical emergency response occurs, and therefore, fail to be implemented in practice. Any holistic approach towards creating a pipeline for effective emergency response must also look at other challenges that it subsumes - predicting when and where incidents happen and understanding the changing environmental dynamics. We describe a system that collectively deals with all these problems in an online manner, meaning that the models get updated with streaming data sources. We highlight why such an approach is crucial to the effectiveness of emergency response, and present an algorithmic framework that can compute promising actions for a given decision-theoretic model for responder dispatch. We argue that carefully crafted heuristic measures can balance the trade-off between computational time and the quality of solutions achieved and highlight why such an approach is more scalable and tractable than traditional approaches. We also present an online mechanism for incident prediction, as well as an approach based on recurrent neural networks for learning and predicting environmental features that affect responder dispatch. We compare our methodology with prior state-of-the-art and existing dispatch strategies in the field, which show that our approach results in a reduction in response time with a drastic reduction in computational time.Comment: Appeared in ICCPS 201

Identification of Evidence for Key Parameters in Decision-Analytic Models of Cost-Effectiveness : A Description of Sources and a Recommended Minimum Search Requirement

This paper proposes recommendations for a minimum level of searching for data for key parameters in decision-analytic models of cost effectiveness and describes sources of evidence relevant to each parameter type. Key parameters are defined as treatment effects, adverse effects, costs, resource use, health state utility values (HSUVs) and baseline risk of events. The recommended minimum requirement for treatment effects is comprehensive searching according to available methodological guidance. For other parameter types, the minimum is the searching of one bibliographic database plus, where appropriate, specialist sources and non-research-based and non-standard format sources. The recommendations draw on the search methods literature and on existing analyses of how evidence is used to support decision-analytic models. They take account of the range of research and non-research-based sources of evidence used in cost-effectiveness models and of the need for efficient searching. Consideration is given to what constitutes best evidence for the different parameter types in terms of design and scientific quality and to making transparent the judgments that underpin the selection of evidence from the options available. Methodological issues are discussed, including the differences between decision-analytic models of cost effectiveness and systematic reviews when searching and selecting evidence and comprehensive versus sufficient searching. Areas are highlighted where further methodological research is required

New Solution of Abstract Architecture for Control and Coordination Decentralized Systems

This paper contains a new approach that combines the advantages and disadvantages of suppressing hierarchical and heterarchical control architectures, creating a semi-heterarchical (holonic) control architecture. The degree of subordinate unit autonomy changes dynamically, depending on the presence of a system disruption, and its scope allows for a smooth transition from hierarchical to heterarchic control architecture in subordinate units. We have proposed a representation of the dynamic degree of autonomy and its possible application to subordinate units, which are, in our case, one-directional Automated Guided Vehicles (AGVs) and are guided by magnetic tape. In order to achieve such a semi-heterarchic management architecture with a dynamic degree of autonomy, approaches such as smart product, stymergic (indirect) communication, or basic principles of holon approach have been implemented

Ansor : Generating High-Performance Tensor Programs for Deep Learning

High-performance tensor programs are crucial to guarantee efficient execution of deep neural networks. However, obtaining performant tensor programs for different operators on various hardware platforms is notoriously challenging. Currently, deep learning systems rely on vendor-provided kernel libraries or various search strategies to get performant tensor programs. These approaches either require significant engineering effort to develop platform-specific optimization code or fall short of finding high-performance programs due to restricted search space and ineffective exploration strategy. We present Ansor, a tensor program generation framework for deep learning applications. Compared with existing search strategies, Ansor explores many more optimization combinations by sampling programs from a hierarchical representation of the search space. Ansor then fine-tunes the sampled programs with evolutionary search and a learned cost model to identify the best programs. Ansor can find high-performance programs that are outside the search space of existing state-of-the-art approaches. In addition, Ansor utilizes a task scheduler to simultaneously optimize multiple subgraphs in deep neural networks. We show that Ansor improves the execution performance of deep neural networks relative to the state-of-the-art on the Intel CPU, ARM CPU, and NVIDIA GPU by up to $3.8\times$, $2.6\times$, and $1.7\times$, respectively.Comment: Published in OSDI 202