'Branch prediction' presentation slideshows

IntelPentiumM_r4.ppt

Pentium (586) - first superscalar - included two pipelines, u and v - virtual-8086 mode ... Pentium Pro (686 or P6) - three-way superscalar ...

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Lecture 8: Vector Processing, Branch Prediction, Dependence Speculation

Lecture 8: Vector Processing, Branch Prediction, Dependence Speculation. Prof. John Kubiatowicz Computer Science 252 Fall 1998. Review. Precise exceptions/Speculation: Out-of-order execution, In-order commit (reorder buffer)

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Chapter 4 Exploiting Instruction-Level Parallelism with Software Approaches

Chapter 4 Exploiting Instruction-Level Parallelism with Software Approaches. Basic Compiler Techniques for Exposing. Basic pipeline scheduling and loop unrolling

998 views • 47 slides

Krste Asanovic Electrical Engineering and Computer Sciences University of California at Berkeley

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Krste Asanovic krste@eecs.berkeley inst.eecs.berkeley /~cs252/sp14

CS252 Graduate Computer Architecture Spring 2014 Lecture 7: Branch Prediction and Load-Store Queues. Krste Asanovic krste@eecs.berkeley.edu http:// inst.eecs.berkeley.edu /~cs252/sp14. Last Time in Lecture 6. Modern Out-of-Order Architectures with Precise Traps Data-in-ROB design

594 views • 41 slides

ECE 454 Computer Systems Programming CPU Architectures

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Microprocessors I

Microprocessors I. Time: Sundays & Tuesdays 07:30 to 8:45 Place: EE 4 ( New building) Lecturer: Bijan Vosoughi Vahdat Room: VP office, NE of Uni Office Hours: After 16 please Vahdat@sharif.edu http://sharif.edu/~vahdat Phone: (6616) 5001 Fax: 6600 5816. Grading Policy.

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CS 152 Computer Architecture and Engineering Lecture 23: Putting it all together: Intel Nehalem

CS 152 Computer Architecture and Engineering Lecture 23: Putting it all together: Intel Nehalem. Krste Asanovic Electrical Engineering and Computer Sciences University of California, Berkeley http://www.eecs.berkeley.edu/~krste http://inst.cs.berkeley.edu/~cs152 . Intel Nehalem.

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Part IV Data Path and Control

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NTPT: On the End-to-End Traffic Prediction in the On-Chip Networks

NTPT: On the End-to-End Traffic Prediction in the On-Chip Networks. Yoshi Shih-Chieh Huang 1 , June 16, 2010. Kaven Chun-Kai Chou 1 Chung-Ta King 1 Shau -Yin Tseng 2. 1 Department of Computer Science, National Tsing Hua University, Hsinchu , Taiwan

303 views • 22 slides

Branch Prediction in SimpleScalar

Branch Prediction in SimpleScalar. Brooks Lombardy David Austin. Overview. Problem Statement Solution SimpleScalar Branch Predictor Overview Command Line Options Data Conclusion. Problem. Far too many combinations of branch prediction hardware to justify cost of implementing them all.

593 views • 17 slides

Module 3: Branch Prediction

Module 3: Branch Prediction. Control Dependencies. Dependencies. Data. Name. Control. Structural . Anti . Output . Control dependencies determine execution order of instructions Instructions are control dependent on a branch instruction Why do we conserve control dependencies?

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Limits on Instruction Level Parallelism (ILP)

Limits on Instruction Level Parallelism (ILP). EE 548 Prof. Warter-Perez. ILP Available. Effects of Window Size. Window Size per Benchmark. Effects of Branch-Prediction Schemes. Branch Prediction per Benchmark. Branch Prediction Accuracy. Number of Registers for Renaming.

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A Review of Processor Design Flow

A Review of Processor Design Flow. How to design a CPU ?. Instruction-set architecture (ISA) design Function-level (RTL) design Component-level design Gate-level/switch-level design Circuit-level design. Design Method. Gate Level: full CAD Register Level: CAD + heuristics/intuition

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4th JILP Workshop on Computer Architecture Competitions Championship Branch Prediction (CBP-4)

4th JILP Workshop on Computer Architecture Competitions Championship Branch Prediction (CBP-4) -Moinuddin Qureshi (GT). Drum Rolls. The Reward 1. Trophy for winner in each category Certificates for winner and runners up in each category

376 views • 9 slides

Platform Abstraction

Platform Abstraction. Group 3. Question. How to deal with different types hardware and software platforms? What detail to expose to the programmer? What detail to expose to the modeler? What are the layers in the abstraction?. Properties of hardware platform. 1. Number of cores

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RESULTS

RESULTS. Experimental Framework. Goal Simplicity of a trace based simulator Flexibility to model special predictors ( e.g., using data values) Trace driven with pipeline timing information Tracing Methodology: Detailed timing simulator with perfect branch predictor 50M uops per trace

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CPU Optimization

259 views • 6 slides