Step 1: Select any four sorting algorithm and two searching algorithms Step 2: Understand the logic of all the algorithms
Step 3: Create java program and use your sorting/searching source codes and integrate it into your main java project.
Step 4: Create a separate java class for each algorithm
Step 5: Create a random function that generates at least 100000 random integer numbers from 1 to 1 million(No need to print out or store the numbers)
Step 6: Insert start transaction and end transaction for each sorting and searching methods
Step 7: Calculate the time in milliseconds for each sorting and searching class
Step 8: Compare the performance of each algorithm
Project Deliverables -Determine the best and worst sorting and searching algorithm -Submit only a single word document with all the screenshots -Java Source code -Output screeshot that shows the transaction time for each algorithm -6 class files for sorting(4) and searching algorithm(2) -1 class file with main function -Conclusion -No power points only source code

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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?