ESSENTIALS OF COMPUTER ORGAN..-TEXT
ESSENTIALS OF COMPUTER ORGAN..-TEXT
4th Edition
ISBN: 9781284033144
Author: NULL
Publisher: JONES+BART
bartleby

Concept explainers

Expert Solution & Answer
Book Icon
Chapter 11, Problem 4E

Explanation of Solution

Verification of the ratio being consistent when compared with the other system:

  • System performance is considered as one of main factor of a processor.
  • It is used to determine the speed a problem can be solved.
  • It is also used to determine the factors such as number of problems that can be allocated at particular amount of time and also the number of problems that can be handled by the processor.
  • The relative performance between the two systems is measured and expected by the run time of the program of the individual.

Given:

The information about the system A and System c are shown in the below table:

Program

Execution time

System A(sec)

Execution time

System B(sec)

Execution time

System C(sec)

V4512575
W300275350
X250100200
Y400300500
Z8001200700

Consider there are n programs and each programs are considered to have their own runtime on each systems.

Program

Execution time

System A(sec)

Execution time

System B(sec)

Execution time

System C(sec)

V4512575
W300275350
X250100200
Y400300500
Z8001200700

System A:

calculate the arithmetic mean for the system A:

A=(45+300+250+400+8005)=359

System B:

calculate the arithmetic mean for the system B:

A=(125+275+100+300+12005)=400

System C:

calculate the arithmetic mean for the system C:

A=(75+350+200+500+7005)=365

Calculating the ratio of the geometic mean obtained for the various systems are:

AM_AAM_B=359400=0.8975

AM_BAM_C=400365=1.095

AM_AAM_C=359365=0.983

Consider there are n programs and each programs are considered to have their own runtime on each systems.

The geometric mean of the one system’s runtime is obtained by normalizing it with the another system.

The process of normalization is carried out by taking the products of the ratio of the run time and by taking the nth root of the product.

The below tables illustrates the how the system B and system C is being normalized with that of the system A.

Program

Execution time

System A(sec)

Normalized to A

Execution time

System B(sec)

Normalized to B

Execution time

System C(sec)

Normalized  to C
V4511250.36750.6
W30012751.093500.857
X25011002.52001.25
Y40013001.335000.8
Z800112000.677001.14

Calculating geometic Mean:

The formula to calculate the geometric mean:

G=(x1×x2×x3×...×xn)1n

System A:

calculate the geometric mean for the system A:

G=(1×1×1×1×1)15=1

System B:

calculate the geometric mean for the system B:

G=(0.36×1.09×2.5×1.33×0.67)15=(0.874)15=0.973

System C:

calculate the geometric mean for the system C:

G=(0.6×0.857×1.25×0.8×1.14)15=(3.7042)15=1.299

Calculating the ratio of the geometic mean obtained for the various systems are:

GM_AGM_B=10.973=1.027

GM_BGM_C=0.9731.299=0.749

GM_AGM_C=11.299=0.769

From the comparison of the  arithmetic and geometric mean that is obtained with the every pair of system, the consistency is found only with the sytem A because the ratio of runtine in the system B and system C  is less than 1.

Likewise, The relative performance compariosn seems to be difficult to compared with that of the system A or B or with the system B or C.

Want to see more full solutions like this?

Subscribe now to access step-by-step solutions to millions of textbook problems written by subject matter experts!
Students have asked these similar questions
here is a diagram code : graph LR subgraph Inputs [Inputs] A[Input C (Complete Data)] --> TeacherModel B[Input M (Missing Data)] --> StudentA A --> StudentB end subgraph TeacherModel [Teacher Model (Pretrained)] C[Transformer Encoder T] --> D{Teacher Prediction y_t} C --> E[Internal Features f_t] end subgraph StudentA [Student Model A (Trainable - Handles Missing Input)] F[Transformer Encoder S_A] --> G{Student A Prediction y_s^A} B --> F end subgraph StudentB [Student Model B (Trainable - Handles Missing Labels)] H[Transformer Encoder S_B] --> I{Student B Prediction y_s^B} A --> H end subgraph GroundTruth [Ground Truth RUL (Partial Labels)] J[RUL Labels] end subgraph KnowledgeDistillationA [Knowledge Distillation Block for Student A] K[Prediction Distillation Loss (y_s^A vs y_t)] L[Feature Alignment Loss (f_s^A vs f_t)] D -- Prediction Guidance --> K E -- Feature Guidance --> L G --> K F --> L J -- Supervised Guidance (if available) --> G K…
details explanation and background   We solve this using a Teacher–Student knowledge distillation framework: We train a Teacher model on a clean and complete dataset where both inputs and labels are available. We then use that Teacher to teach two separate Student models:  Student A learns from incomplete input (some sensor values missing). Student B learns from incomplete labels (RUL labels missing for some samples). We use knowledge distillation to guide both students, even when labels are missing. Why We Use Two Students Student A handles Missing Input Features: It receives input with some features masked out. Since it cannot see the full input, we help it by transferring internal features (feature distillation) and predictions from the teacher. Student B handles Missing RUL Labels: It receives full input but does not always have a ground-truth RUL label. We guide it using the predictions of the teacher model (prediction distillation). Using two students allows each to specialize in…
We are doing a custom JSTL custom tag to make display page to access a tag handler.   Write two custom tags: 1) A single tag which prints a number (from 0-99) as words. Ex:    <abc:numAsWords val="32"/>   --> produces: thirty-two   2) A paired tag which puts the body in a DIV with our team colors. Ex:    <abc:teamColors school="gophers" reverse="true">     <p>Big game today</p>     <p>Bring your lucky hat</p>      <-- these will be green text on blue background   </abc:teamColors> Details: The attribute for numAsWords will be just val, from 0 to 99   - spelling, etc... isn't important here. Print "twenty-six" or "Twenty six" ... .  Attributes for teamColors are: school, a "required" string, and reversed, a non-required boolean.   - pick any four schools. I picked gophers, cyclones, hawkeyes and cornhuskers   - each school has two colors. Pick whatever seems best. For oine I picked "cyclones" and       red text on a gold body   - if…
Knowledge Booster
Background pattern image
Computer Science
Learn more about
Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, computer-science and related others by exploring similar questions and additional content below.
Similar questions
SEE MORE QUESTIONS
Recommended textbooks for you
Text book image
Database System Concepts
Computer Science
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:McGraw-Hill Education
Text book image
Starting Out with Python (4th Edition)
Computer Science
ISBN:9780134444321
Author:Tony Gaddis
Publisher:PEARSON
Text book image
Digital Fundamentals (11th Edition)
Computer Science
ISBN:9780132737968
Author:Thomas L. Floyd
Publisher:PEARSON
Text book image
C How to Program (8th Edition)
Computer Science
ISBN:9780133976892
Author:Paul J. Deitel, Harvey Deitel
Publisher:PEARSON
Text book image
Database Systems: Design, Implementation, & Manag...
Computer Science
ISBN:9781337627900
Author:Carlos Coronel, Steven Morris
Publisher:Cengage Learning
Text book image
Programmable Logic Controllers
Computer Science
ISBN:9780073373843
Author:Frank D. Petruzella
Publisher:McGraw-Hill Education