Computer Architecture: A Quantitative Approach, 4th Edition

Info icon This preview shows pages 1–8. Sign up to view the full content.

1 University of California, Berkeley College of Engineering Computer Science Division EECS Fall 1999 John Kubiatowicz Midterm II December 1, 1999 CS252 Graduate Computer Architecture Your Name: SID Number: Problem Possible Score 1 25 2 25 3 20 4 30 Total 100
Image of page 1

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

2 [ This page left for π ] 3.141592653589793238462643383279502884197169399375105820974944
Image of page 2
3 Question #1: Prediction 1a) Why does branch prediction work? 1b) What is the aliasing problem for branch predictors? Is aliasing always bad? 1c) How does the branch target buffer (BTB) help modern branch predictors? ( hint: we want to be able to remove all branch delay slots): 1d) Draw the hardware for a gshare branch predictor. Why does this type of predictor generally perform better than an equivalent GAg branch predictor?
Image of page 3

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

4 1e) For correct performance, out-of-order processors must detect and resolve RAW, WAR, and WAW hazards between loads and stores to memory. Describe the hardware support for detecting these hazards and provide a short, pseudo-code description of the questions that must be asked before a load or store is released to the memory system. Assume no dependence speculation for the moment (conservative removal of hazards). 1f) “Naive dependence speculation” assumes that loads and stores are not dependent on each other, if their addresses are unknown. How does this change your algorithm above? On average, is this a better idea than being exact (as in 1e)? Why or why not?
Image of page 4
5 1g) What is memory dependence prediction and why does it help to improve performance in an modern processor with-out-of-order execution? ( hint: compare with naive dependence speculation)? 1h) What hardware might be used to detect and predict repeating patterns of four or less data values (e.g. something like: 4, 5, 6, 2, 4, 5, 6, 2, etc...)? Assume that these are 32-bit values. 1i) How might data prediction be used in a modern processor? Under what circumstances might this improve performance (try to be specific, i.e. don’t just say “when it is correct”)?
Image of page 5

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

6 [This page intentionally left blank]
Image of page 6
7 Problem #2: Error Correction and RAID The error correction coding process can be viewed as a transformation from one space of bits (the unencoded data) to another (the coded data). 2a) In the (n,k) notation for an error correction code, k is the unencoded data width in bits and n is the encoded width. Which is larger, n or k ? Why must it be this way?
Image of page 7

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

Image of page 8
This is the end of the preview. Sign up to access the rest of the document.
  • Spring '07
  • Kubiatowicz
  • Computer Architecture, Hamming Code, Error detection and correction, Parity bit, Minimum distance, Level-5

{[ snackBarMessage ]}

What students are saying

  • Left Quote Icon

    As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

    Student Picture

    Kiran Temple University Fox School of Business ‘17, Course Hero Intern

  • Left Quote Icon

    I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

    Student Picture

    Dana University of Pennsylvania ‘17, Course Hero Intern

  • Left Quote Icon

    The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

    Student Picture

    Jill Tulane University ‘16, Course Hero Intern