Better branch prediction through prophet/critic hybrids

The prophet/critic hybrid conditional branch predictor has two component predictors. The prophet uses a branch's history to predict its direction. We call this prediction and the ones for branches following it the branch future. The critic uses the branch's history and future to critique the prophet's prediction. The hybrid combines the prophet's prediction with the critique, either agrees or disagree, forming the branch's overall prediction. Results shows these hybrids can reduce mispredicts by 39 percent and improve processor performance by 7.8 percent.Peer ReviewedPostprint (published version

The predictability of data values

reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the IEEE. Contact

Dynamic Data Dependence Tracking and Its Application to Branch Prediction

To continue to improve processor performance, microarchitects seek to increase the effective instruction level parallelism (ILP) that can be exploited in applications. A fundamental limit to improving ILP is data dependences among instructions. If data dependence information is available at run-time, there are many uses to improve ILP. Prior published examples include decoupled branch exectuion architectures and critical instruction detection. In this paper, we describe an efficient hardware mechanism to dynamically track the data dependence chains of the instructions in the pipeline. This information is available on a cycle-by-cycle basis to the microengine for optimizing its perfromance. We then use this design in a new value-based branch prediction design using Available Register Value Information (ARVI). From the use of data dependence information, the ARVI branch predictor has better prediction accuracy over a comparably sized hybrid branch perdictor. With ARVI used as the second-level branch predictor, the improved prediction accuracy results in a 12.6% performance improvement on average across the SPEC95 integer benchmark suite

On Statistical Data Compression

Im Zuge der stetigen Weiterentwicklung moderner Technik wächst die Menge an zu verarbeitenden Daten.Es gilt diese Daten zu verwalten, zu übertragen und zu speichern.Dafür ist Datenkompression unerlässlich.Gemessen an empirischen Kompressionsraten zählen Statistische Datenkompressionsalgorithmen zu den Besten.Diese Algorithmen verarbeiten einen Eingabetext buchstabenweise.Dabei verfährt man für jeden Buchstaben in zwei Phasen - Modellierung und Kodierung.Während der Modellierung schätzt ein Modell, basierend auf dem bereits bekannten Text, eine Wahrscheinlichkeitsverteilung für den nächsten Buchstaben.Ein Kodierer überführt die Verteilung und den Buchstaben in ein Codewort.Umgekehrt ermittelt der Dekodierer aus der Verteilung und dem Codewort den kodierten Buchstaben.Die Wahl des Modells bestimmt den statistischen Kompressionsalgorithmus, das Modell ist also von zentraler Bedeutung.Ein Modell mischt typischerweise viele einfache Wahrscheinlichkeitsschätzer.In der statistischen Datenkompression driften Theorie und Praxis auseinander.Theoretiker legen Wert auf Modelle, die mathematische Analysen zulassen, vernachlässigen aber Laufzeit, Speicherbedarf und empirische Verbesserungen;Praktiker verfolgen den gegenteiligen Ansatz.Die PAQ-Algorithmen haben die Überlegenheit des praktischen Ansatzes verdeutlicht.Diese Arbeit soll Theorie und Praxis annähren.Dazu wird das Handwerkszeug des Theoretikers, die Codelängenanlyse, auf Algorithmen des Praktikers angewendet.Es werden Wahrscheinlichkeitsschätzer, basierend auf gealterten relativen Häufigkeiten und basierend auf exponentiell geglätteten Wahrscheinlichkeiten, analysiert.Weitere Analysen decken Methoden ab, die Verteilungen durch gewichtetes arithmetisches und geometrisches Mitteln mischen und Gewichte mittels Gradientenverfahren bestimmen.Die Analysen zeigen, dass sich die betrachteten Verfahren ähnlich gut wie idealisierte Vergleichsverfahren verhalten.Methoden aus PAQ werden mit dieser Arbeit erweitert und mit einer theoretischen Basis versehen.Experimente stützen die Analyseergebnisse.Ein weiterer Beitrag dieser Arbeit ist Context Tree Mixing (CTM), eine Verallgemeinerung von Context Tree Weighting (CTW).Durch die Kombination von CTM mit Methoden aus PAQ entsteht ein theoretisch fundierter Kompressionsalgorithmus, der in Experimenten besser als CTW komprimiert.The ongoing evolution of hardware leads to a steady increase in the amount of data that is processed, transmitted and stored.Data compression is an essential tool to keep the amount of data manageable.In terms of empirical performance statistical data compression algorithms rank among the best.A statistical data compressor processes an input text letter by letter and compresses in two stages --- modeling and coding.During modeling a model estimates a probability distribution on the next letter based on the past input.During coding an encoder translates this distribution and the next letter into a codeword.Decoding reverts this process.The model is exchangeable and its choice determines a statistical data compression algorithm.All major models use a mixer to combine multiple simple probability estimators, so-called elementary models.In statistical data compression there is a gap between theory and practice.On the one hand, theoreticians put emphasis on models that allow for a mathematical analysis, but neglect running time and space considerations and empirical improvements.On the other hand practitioners focus on the very reverse.The family of PAQ statistical compressors demonstrated the superiority of the practitioner's approach in terms of empirical compression.With this thesis we attempt to bridge the aforementioned gap between theory and practice with special focus on PAQ.To achieve this we apply the theoretician's tools to practitioner's approaches:We provide a code length analysis for several practical modeling and mixing techniques.The analysis covers modeling by relative frequencies with frequency discount and modeling by exponential smoothing of probabilities.For mixing we consider linear and geometrically weighted averaging of probabilities with Online Gradient Descent for weight estimation.Our results show that the models and mixers we consider perform nearly as well as idealized competitors.Experiments support our analysis.Moreover, our results add a theoretical basis to modeling and mixing from PAQ and generalize methods from PAQ.Ultimately, we propose and analyze Context Tree Mixing (CTM), a generalization of Context Tree Weighting (CTW).We couple CTM with modeling and mixing techniques from PAQ and obtain a theoretically sound compression algorithm that improves over CTW, as shown in experiments

Analysis of Branch Prediction via Data Compression

Branch prediction is an important mechanism in modem microprocessor design. The focus of research in this area has been on designing new branch prediction schemes. In contrast, very few studies address the theoretical basis behind these prediction schemes. Knowing this theoretical basis helps us to evaluate how good a prediction scheme is and how much we can expect to improve its accuracy