Pipeline Modeling for Timing Analysis

Langenbach, Marc; Thesing, Stephan; Heckmann, Reinhold

doi:10.1007/3-540-45789-5_22

Cited by 44 publications

(17 citation statements)

References 21 publications

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“…Exclusion of these so-called timing accidents tightens the upper bound by the associated timing penalty, e.g., the cache miss penalty or the time to refill the pipeline. Examples of static analyses to exclude timing accidents can be found in [1,2,3]. A designer that introduces caches, deep pipelines, or other performance boosting components into his system may find himself in the paradoxical situation that he has successfully improved the average-case performance of the system, but fails to derive sufficient timing guarantees despite his best efforts.…”

Section: Introductionmentioning

confidence: 99%

Timing predictability of cache replacement policies

et al. 2007

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Abstract. Hard real-time systems must obey strict timing constraints. Therefore, one needs to derive guarantees on the worst-case execution times of a system's tasks. In this context, predictable behavior of system components is crucial for the derivation of tight and thus useful bounds. This paper presents results about the predictability of common cache replacement policies. To this end, we introduce three metrics, evict, f ill, and mls that capture aspects of cache-state predictability. A thorough analysis of the LRU, FIFO, MRU, and PLRU policies yields the respective values under these metrics. To the best of our knowledge, this work presents the first quantitative, analytical results for the predictability of replacement policies. Our results support empirical evidence in static cache analysis.

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Section: Introductionmentioning

confidence: 99%

Timing predictability of cache replacement policies

et al. 2007

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“…It is demonstrated for the Motorola ColdFire processor, a processor quite popular in the aeronautics and the submarine industry. The presentation follows closely that of [LTH02] 2 .…”

Section: Pipeline Modelingmentioning

confidence: 86%

Determining Bounds on Execution Times

Wilhelm¹

2009

Industrial Information Technology

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play an important role in the area of embedded systems and especially hard real-time systems. These systems are typically subject to stringent timing constraints, which often result from the interaction with the surrounding physical environment. It is essential that the computations are completed within their associated time bounds; otherwise severe damages may result, or the system may be unusable. Therefore, a schedulability analysis has to be performed which guarantees that all timing constraints will be met. Schedulability analyses require upper bounds for the execution times of all tasks in the system to be known. These bounds must be safe, i.e., they may never underestimate the real execution time. Furthermore, they should be tight, i.e., the overestimation should be as small as possible.In modern microprocessor architectures, caches, pipelines, and all kinds of speculation are key features for improving (average-case) performance. Unfortunately, they make the analysis of the timing behaviour of instructions very difficult, since the execution time of an instruction depends on the execution history. A lack of precision in the predicted timing behaviour may lead to a waste of hardware resources, which would have to be invested in order to meet the requirements. For products which are manufactured in high quantities, e.g., in the automobile or telecommunications markets this would result in intolerable expenses.Subject of this chapter are one particular approach and the subtasks involved in computing safe and precise bounds on the execution times for real-time systems. IntroductionHard real-time systems are subject to stringent timing constraints which are dictated by the surrounding physical environment. We assume that a real-time system consists of a number of tasks, which realize the required functionality. A schedulability analysis for this set of tasks and a given hardware has to be performed in order to guarantee that all the timing constraints of these tasks will be met ("timing validation"). Existing techniques for schedulability analysis require upper bounds for the execution times of all the system's tasks to be known. These upper bounds are commonly called the worst-case execution times (WCETs), a misnomer that causes a lot of confusion and will therefore not be adopted in this presentation. In analogy, lower bounds on the execution time have been named best-case execution times, (BCET). These upper bounds (and lower bounds) have to be safe, i.e., they must never underestimate (overestimate) the real execution time. Furthermore, they should be tight, i.e., the overestimation (underestimation) should be as small as possible. Figure 0.1 depicts the most important concepts of our domain. The system shows a certain variation of execution times depending on the input data or different behaviour of the environment. In general, the state space is too large to exhaustively explore all possible executions and so determine the exact worst-case and best-case execution times. Some abstraction of the system is...

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“…For example, the WCET analysis for the Motorola ColdFire 5307 described in [8] assumes an empty cache for the WCET analysis (worst-case assumption) whenever the cache content is unknown. This strategy fails if the cache is configured as "copy back".…”

Section: Wcet Estimate or Wcet Bound?mentioning

confidence: 99%

Obstacles in Worst-Case Execution Time Analysis

Kirner

Puschner

2008

2008 11th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC)

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The analysis of the worst-case execution time (WCET) requires detailed knowledge of the program behavior. In practice it is still not possible to obtain all needed information automatically.In this paper we present the current state of the art of WCET analysis and point to the main problems to be solved. The most eminent problem is the state problem, i.e., the precise determination of possible processor states at different program locations. The path problem refers to the fact that current tools are not able to calculate all (in)feasible paths automatically. We discuss how the main open problems manifest themselves in static and in measurement-based WCET analysis methods.

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Pipeline Modeling for Timing Analysis

Cited by 44 publications

References 21 publications

Timing predictability of cache replacement policies

Timing predictability of cache replacement policies

Determining Bounds on Execution Times

Obstacles in Worst-Case Execution Time Analysis

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