Core model

Abstract: Large core counts and complex cache hierarchies are increasing the burden placed on commonly used simulation and modeling techniques. Although analytical models provide fast results, they do not apply to complex, many-core shared-memory systems. In contrast, detailed cycle-level simulation can be accurate but also tends to be slow, which limits the number of configurations that can be evaluated. A middle ground is needed that provides for fast simulation of complex many-core processors while still providing accurate results. In this article, we explore, analyze, and compare the accuracy and simulation speed of high-abstraction core models as a potential solution to slow cycle-level simulation. We describe a number of enhancements to interval simulation to improve its accuracy while maintaining simulation speed. In addition, we introduce the instruction-window centric (IW-centric) core model, a new mechanistic core model that bridges the gap between interval simulation and cycle-accurate simulation by enabling high-speed simulations with higher levels of detail. We also show that using accurate core models like these are important for memory subsystem studies, and that simple, naive models, like a one-IPC core model, can lead to misleading and incorrect results and conclusions in practical design studies. Validation against real hardware shows good accuracy, with an average single-core error of 11.1p and a maximum of 18.8p for the IW-centric model with a 1.5× slowdown compared to interval simulation.

Abstract: With emphasis on the adjective "practical" and the normative verb "should," my answer to the question of this session is a resounding yes. Indeed, I spent a good deal of time between January 1993 and January 1996 acting on the belief that there is such a core macro model; so I certainly hope it exists.' This believable core model falls well short of perfection, leaves many questions unanswered, and is subject to substantial stochastic errors. Nonetheless, it is both useful and extensively used in policy analysis, where contact with reality is a necessity, and you cannot beat something with nothing. It also closely resembles, but does not quite match, the way macroeconomics is taught to beginning and intermediate (but not to graduate) students. In this short presentation, I will describe briefly the main practical elements that I think we should agree on, without worrying too much about their theoretical underpinnings. Then I will turn to two critical failings of the standard macro model which cry out for theoretical and empirical repair. My organizing principle is the textbook exposition that has been standard, though not universal, in teaching intermediate macroeconomics for years. The question is: how does it differ from the "core model" used in policy analysis?

Abstract: 5 1 Motivation 6 2 Organizational Setting 6 21 The Knowledge-based System Interoperability Project 6 22 Related Projects 7 3 Related Research 7 31 Product Modeling 7 32 Design Information Flow Modeling 9 4 Objectives 10 5 Pragmatic Study 10 6 The Core Representation 11 61 Representation of Attributes and Class Types 11 62 Semantics 12 63 Class Hierarchy 16 64 Associations and Aggregations 17 65 Relationships 18 7 Present and Potential Applications 19 71 Design Repository 19 72 Open Assembly Design Environment 73 Design for Tolerancing 21 74 Design-Process Planning Integration 21 8 Areas for Future Research 22 81 Extensions to Support of Interoperable Tools 24 82 Exploration of Applicability to Full Lifecycle of Artifact 24 9 Summary 25 Acknowledgements 25 References 26 Appendix A: Comparison of Four Product Models 33 Appendix B: The Core Model 36

Abstract: In this thesis, we apply as well as develop techniques and methodologies for the examination of the complex systems that are lexicalized statistical parsing models. The primary idea is that of treating the “model as data”, which is not a particular method, but a paradigm and a research methodology. Our argument is that lexicalized statistical parsing models have become increasingly complex, and therefore require thorough scrutiny, both to achieve the scientific aim of understanding what has been built thus far, and to achieve both the scientific and engineering goal of using that understanding for progress. In this thesis, we take a particular, dominant type of parsing model and perform a macro analysis, to reveal its core (and design a software engine that modularizes the periphery), and we also crucially perform a detailed analysis, which provides for the first time a window onto the efficacy of specific parameters. These analyses have not only yielded insight into the core model, but they have also enabled the identification of “inefficiencies” in our baseline model, such that those inefficiencies can be reduced to form a more compact model, or exploited for finding a better-estimated model with higher accuracy, or both.