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TwoFactorExperimentsNoReps

# TwoFactorExperimentsNoReps - CPE 619 Two-Factor Full...

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CPE 619 Two-Factor Full Factorial Design Without Replications Aleksandar Milenković The LaCASA Laboratory Electrical and Computer Engineering Department The University of Alabama in Huntsville http://www.ece.uah.edu/~milenka http://www.ece.uah.edu/~lacasa

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2 Overview Computation of Effects Estimating Experimental Errors Allocation of Variation ANOVA Table Visual Tests Confidence Intervals For Effects Multiplicative Models Missing Observations
3 Two Factors Full Factorial Design Used when there are two parameters that are carefully controlled Examples: To compare several processors using several workloads (factors: CPU, workload) To determine two configuration parameters, such as cache and memory sizes Assumes that the factors are categorical For quantitative factors, use a regression model A full factorial design with two factors A and B having a and b levels requires ab experiments First consider the case where each experiment is conducted only once

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4 Model
5 Computation of Effects Averaging the jth column produces: Since the last two terms are zero, we have: Similarly, averaging along rows produces: Averaging all observations produces Model parameters estimates are: Easily computed using a tabular arrangement

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6 Example 21.1: Cache Comparison Compare 3 different cache choices on 5 workloads
7 Example 21.1: Computation of Effects An average workload on an average processor requires 72.2 ms of processor time The time with two caches is 21.2 ms lower than that on an average processor The time with one cache is 20.2 ms lower than that on an average processor. The time without a cache is 41.4 ms higher than the average

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8 Example 21.1 (cont’d) Two-cache - One-cache = 1 ms One-cache - No-cache = 41.4 - 20.2 = 21.2 ms The workloads also affect the processor time required The ASM workload takes 0.5 ms less than the average TECO takes 8.8 ms higher than the average
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