// Marian Basting takes in small sewing jobs. // She has two files sorted by date. // (The date is composed of two numbers -- month and year.) // One file holds new sewing projects // (such as "wedding dress") // and the other contains repair jobs // (such as "replace jacket zipper"). // Each file contains the month, day, client name, phone number, // job description, and price. // Currently, this program merges the files to produce // a report that lists all of Marian's jobs for the year // in date order. // Modify the program to also display her total earnings // at the end of each month as well as at the end of the year.

When making changes to optimize part of a processor, it is often the case that speeding up one type of instruction comes at the cost of slowing down something else. for example, if we put in a complicated fast floating-point unit, that takes space, and something might have to be moved farther away from the middle to accommodate it, adding an extra cycle in delay to reach that unit. the basic amdahl's law equation does not take into account this trade-off. a. if the new fast floating-point unit speeds up floating-point operations by, on average, 2ă—, and floating-point operations take 20% of the original program's execution time, what is the overall speedup (ignoring the penalty to any other instructions)? b. now assume that speeding up the floating-point unit slowed down data cache accesses, resulting in a 1.5ă— slowdown (or 2/3 speedup). data cache accesses consume 10% of the execution time. what is the overall speedup now? c. after implementing the new floating-point operations, what percentage of execution time is spent on floating-point operations? what percentage is spent on data cache accesses?