This paper first introduces the SIMD (single instruction multiple data) extension technology and presents three ways to use SIMD instructions. It is considered that calling the third party library, which is optimized ...
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Architectural Design Space Exploration (DSE) is a notoriously difficult problem due to the exponentially large size of the design space and long simulation times. Previously, many studies proposed to formulate DSE as ...
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ISBN:
(纸本)9781479943944
Architectural Design Space Exploration (DSE) is a notoriously difficult problem due to the exponentially large size of the design space and long simulation times. Previously, many studies proposed to formulate DSE as a regression problem which predicts architecture responses (e.g., time, power) of a given architectural configuration. Several of these techniques achieve high accuracy, though often at the cost of significant simulation time for training the regression models. We argue that the information the architect mostly needs during the DSE process is whether a given configuration will perform better than another one in the presences of design constraints, or better than any other one seen so far, rather than precisely estimating the performance of that configuration. Based on this observation, we propose a novel ranking-based approach to DSE where we train a model to predict which of two architecture configurations will perform best. We show that, not only this ranking model more accurately predicts the relative merit of two architecture configurations than an ANN-based state-of-the-art regression model, but also that it requires much fewer training simulations to achieve the same accuracy, or that it can be used for and is even better at quantifying the performance gap between two configurations. We implement the framework for training and using this model, called ArchRanker, and we evaluate it on several DSE scenarios (unicore/multicore design spaces, and both time and power performance metrics). We try to emulate as closely as possible the DSE process by creating constraint-based scenarios, or an iterative DSE process. We find that ArchRanker makes 29.68% to 54.43 % fewer incorrect predictions on pair-wise relative merit of configurations (tested with 79,800 configuration pairs) than an ANN-based regression model across all DSE scenarios considered (values averaged over all benchmarks for each scenario). We also find that, to achieve the same accuracy as Arc
This paper proposes a decomposition based algo- rithm for revision problems in classical propositional logic. A set of decomposing rules are presented to analyze the satis- fiability of formulas. The satisfiability of...
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This paper proposes a decomposition based algo- rithm for revision problems in classical propositional logic. A set of decomposing rules are presented to analyze the satis- fiability of formulas. The satisfiability of a formula is equivalent to the satisfiability of a set of literal sets. A decomposing function is constructed to calculate all satisfiable literal sets of a given formula. When expressing the satisfiability of a formula, these literal sets are equivalent to all satisfied models of such formula. A revision algorithm based on this decomposing function is proposed, which can calculate maximal contractions of a given problem. In order to reduce the memory requirement, a filter function is introduced. The improved algorithm has a good performance in both time and space perspectives.
Recent research shows that runtime models can be used to build dynamic systems coping with changing requirements and execution environments. As software systems are getting bigger and more complex, locating malfunctio...
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Recent research shows that runtime models can be used to build dynamic systems coping with changing requirements and execution environments. As software systems are getting bigger and more complex, locating malfunctioning software parts is no trivial task because of the vast amount of possible error sources and produced logging information. This information has to be traced back to faulty components, which often leads to editing of code scattered over different software artefacts. With such a fragmented view, challenges arise in finding out the root cause of the unwanted software behaviour. In this paper we propose the usage of runtime models in combination with model-driven techniques and interactive visualizations to ease tracing between log file entries and corresponding software artefacts. The contribution of this paper is a repository-based approach to augment root cause analysis with interactive tracing views while maximizing reusability of models created during the software development process.
This book constitutes revised selected papers of the 19th International Conference on Applications of Declarative Programming and Knowledge Management, INAP 2011, and the 25th Workshop on Logic Programming, WLP 2011, ...
ISBN:
(数字)9783642415241
ISBN:
(纸本)9783642415234;9783642415241
This book constitutes revised selected papers of the 19th International Conference on Applications of Declarative Programming and Knowledge Management, INAP 2011, and the 25th Workshop on Logic Programming, WLP 2011, held in Vienna, Austria, in September 2011. The 19 papers presented in this volume were carefully reviewed and selected from 27 papers presented at the conference and initially a total of 35 submissions. The book also contains the papers of two invited talks. The papers are organized in topical sections on languages; answer-set programming and abductive reasoning; constraints and logic programming; answer-set programming and model expansion; application papers; and system descriptions.
In this contribution, we compare and analyze different methodologies of modeling for test generation. As an example, we use an industrial requirement specification given in natural language, which describes a safety f...
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In this contribution, we compare and analyze different methodologies of modeling for test generation. As an example, we use an industrial requirement specification given in natural language, which describes a safety function in a hybrid car. We model these requirements with three different paradigms and languages: as the specification imposes several timing constraints, we choose abstract State Machines, Timed Automata and UML2 State Machines to formalize the given requirements. From these models, we employ different tools for generating test cases, and compare the resulting test suites with respect to coverage and fault detection capabilities. We discuss the process of designing the models and the implications for professional software testing.
The current architectural trends in the field of multi-core processors can provide an enormous increase in processing power by exploiting the parallelism available in many applications. In particular because of their ...
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ISBN:
(纸本)9789290922650
The current architectural trends in the field of multi-core processors can provide an enormous increase in processing power by exploiting the parallelism available in many applications. In particular because of their high energy efficiency, it is obvious that multi-core processor-based systems will also be used in future space missions. In this paper we present the system architecture of a powerful optical sensor system based on the eight core multi-core processor P4080 from Freescale. The fault tolerant structure and the highly effective FDIR concepts implemented on different hardware and software levels of the system are described in detail. The space application scenario and thus the main requirements for the sensor system have been defined by a complex tracking sensor application for autonomous landing or docking manoeuvres.
Models in testing are important for describing, understanding, and managing tests. In the automotive domain, AUTOSAR is an important standard to model components of electronic control units. AUTOSAR, however, lacks in...
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A customer churn analytical model based Bayesian network is built for prediction of customer churn. We propose Bayesian Network approaches to predict churn motivation, mining the result in churn characters in order to...
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A customer churn analytical model based Bayesian network is built for prediction of customer churn. We propose Bayesian Network approaches to predict churn motivation, mining the result in churn characters in order to help decision-making manager formulate corresponding detainment strategy. Experimental results show that classification performance of both methods is resultful.
Some typical memory access patterns are provided and programmed in C, which can be used as benchmark to characterize the various techniques and algorithms aim to improve the performance of NUMA memory access. These ac...
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