Computer Systems: A Programmer's Perspective (3rd Edition)
Computer Systems: A Programmer's Perspective (3rd Edition)
3rd Edition
ISBN: 9780134092669
Author: Bryant, Randal E. Bryant, David R. O'Hallaron, David R., Randal E.; O'Hallaron, Bryant/O'hallaron
Publisher: PEARSON
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Chapter 12.1, Problem 12.2PP
Program Plan Intro

Concurrent programming is built with processes and uses the functions such as fork, exec, and waitpid.

  • For example, Client connection request is accepted by concurrent server in the parent and then new child process is created to service each other.

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1. Problem 1 1-(a) Consider H8, what is the entry with row index 00101101 and column index 11110010? 1-(b) Consider H®4, what would be the result of adding columns with indexes 0000 and 1000?
5.14 Write a version of the inner product procedure described in Problem 5.13 that uses 6 x 1 loop unrolling. For x86-64, our measurements of the unrolled version give a CPE of 1.07 for integer data but still 3.01 for both floating-point data. A. Explain why any (scalar) version of an inner product procedure running on an Intel Core i7 Haswell processor cannot achieve a CPE less than 1.00. B. Explain why the performance for floating-point data did not improve with loop unrolling.
(3) (a) Consider the following interaction with Python: x= [1,2,34 ,5 ,6 , np.nan] y= (10,i,2,5, 'Missing',6.3) z= [0.1, 1.2 , np.nan , 4,5.1,0.5] df1=DataFrame ({'col1':Series (z),'co12':Series (y), 'col3': Series (x)}). df1.index= ['a','b','c', 'd','e','f'] Replace the NaN value in coll with -9, the Missing value in col2 with -99, and the NaN value in col3 with -999 with relevant functions. Name as dfl_replaced (b) Consider the following interaction with Python: df2=DataFrame (np. array ( [[1, np.nan ,3, 8], [np.nan , 2,3,5] , [10,2,3, np.nan], [10,2,3 , np.nan], [10,2,3,11]])) df2.columns = ['one', 'two', three', four '] df2. index= ['a','b','c 'd','e'] Remove the rows that have nan values from df2 and name as df2_row. Remove the columns that have nan values from df2 and name as df2_column. Use relevant functions.
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