A mechanistic model for out-of-order superscalar processors is developed and then applied to the study of microarchitecture resource scaling. The model divides execution time into intervals separated by disruptive miss events such as branch mispredictions and cache misses. Each type of miss event results in characterizable performance behavior for the execution time interval. By considering an interval's type and length (measured in instructions), execution time can be predicted for the interval. Overall execution time is then determined by aggregating the execution time over all intervals. The mechanistic model provides several advantages over prior modeling approaches, and, when estimating performance, it differs from detailed simulation of a 4-wide out-of-order processor by an average of 7p.The mechanistic model is applied to the general problem of resource scaling in out-of-order superscalar processors. First, we use the model to determine size relationships among microarchitecture structures in a balanced processor design. Second, we use the mechanistic model to study scaling of both pipeline depth and width in balanced processor designs. We corroborate previous results in this area and provide new results. For example, we show that at optimal design points, the pipeline depth times the square root of the processor width is nearly constant. Finally, we consider the behavior of unbalanced, overprovisioned processor designs based on insight gained from the mechanistic model. We show that in certain situations an overprovisioned processor may lead to improved overall performance. Designs where a processor's dispatch width is wider than its issue width are of particular interest.

https://biblio.ugent.be/publication/2093763/file/2093773.pdf

A mechanistic performance model for superscalar out-of-order processors

Decision rules are one of the most expressive languages for machine learning. In this paper we present Adaptive Model Rules (AMRules), the first streaming rule learning algorithm for regression problems. In AMRules the antecedent of a rule is a conjunction of conditions on the attribute values, and the consequent is a linear combination of attribute values. Each rule uses a Page-Hinkley test to detect changes in the process generating data and react to changes by pruning the rule set. In the experimental section we report the results of AMRules on benchmark regression problems, and compare the performance of our system with other streaming regression algorithms.

/pdf/adaptive-model-rules-from-data-streams-1l7ti6t60h.pdf

Adaptive Model Rules from Data Streams

Predicting wave run-up on rubble-mound structures using M5 model tree

https://www.ece.lsu.edu/lpeng/papers/jsa09.pdf

Accurate and efficient processor performance prediction via regression tree based modeling

The volume and velocity of data is increasing at astonishing rates. In order to extract knowledge from this huge amount of information there is a need for efficient on-line learning algorithms. Rule-based algorithms produce models that are easy to understand and can be used almost offhand. Ensemble methods combine several predicting models to improve the quality of prediction. In this paper, a new on-line ensemble method that combines a set of rule-based models is proposed to solve regression problems from data streams. Experimental results using synthetic and real time-evolving data streams show the proposed method significantly improves the performance of the single rule-based learner, and outperforms two state-of-the-art regression algorithms for data streams.

/pdf/ensembles-of-adaptive-model-rules-from-high-speed-data-58vgf4zp7o.pdf

Ensembles of Adaptive model rules from high-speed data streams

The identification of performance issues on specific computer architectures has a variety of important benefits such as tuning software to improve performance, comparing the performance of various platforms and assisting in the design of new platforms. In order to enable this analysis, most modern micro-processors provide access to hardware-based event counters. Unfortunately, features such as out-of-order execution, pre-fetching and speculation complicate the interpretation of the raw data. Thus, the traditional approach of assigning a uniform estimated penalty to each event does not accurately identify and quantify performance limiters. This paper presents a novel method employing a statistical regression-modeling approach to better achieve this goal. Specifically, a model-tree based approach based on the M5' algorithm is implemented and validated that accounts for event interactions and workload characteristics. Data from a subset of the SPEC CPU2006 suite is used by the algorithm to automatically build a performance-model tree, identifying the unique performance classes (phases) found in the suite and associating with each class a unique, explanatory linear model of performance events. These models can be used to identify performance problems for a given workload and estimate the potential gain from addressing each problem. This information can help orient the performance optimization efforts to focus available time and resources on techniques most likely to impact performance problems with highest potential gain. The model tree exhibits high correlation (more than 0.98) and low relative absolute error (less than 8 %) between predicted and measured performance, attesting it as a sound approach for performance analysis of modern superscalar machines

S. Abraham

Papers

Using Model Trees for Computer Architecture Performance Analysis of Software Applications