CS 201 Advanced Object-Oriented Programming Lab 8 - Linked Lists and Performance Analysis Due: April 7/8, 11:30 PM

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1 CS 201 Advanced Object-Oriented Programming Lab 8 - Linked Lists and Performance Analysis Due: April 7/8, 11:30 PM Introduction to the Assignment In this lab, you will create a fourth implementation of DNA splicing, this time using a linked list. We will also use this lab to compare the performance of the four different implementations of the DNA strand. The objectives of this assignment are to gain experience performing experimentation with code and relate that experience to O() analysis and to work with linked lists. The Program Design A benchmark is a particular type of program that allows us to measure the performance of our code. It does this by recording the time at which some computation starts and the time at which the computation ends. It can then calculate the elapsed time for that computation. It would also record other information about the nature of the computation being measured. We will be benchmarking four implementations of the DNAStrand interface. This week s lab includes a new package called benchmark and two classes: DNABenchMark and OverTimeException DNABenchMark: This class contains the main program. This program does not have a GUI. When you run the program, it will open a file containing DNA data, benchmark the computation involved in creating recombinant DNA and report the results to the Console in Eclipse and save the results in a file called results.csv. OverTimeException: This is an exception that the benchmarking methods throw if a trial takes over some time limit. In addition to Java files, I am also providing a data file, ecolishort.dat. This contains a long DNA sequence, a part of E. Coli s DNA, which you should use in your experimentation. This lab is different from all other labs in that you will do very little programming. Instead, you will be experimenting with your code and essentially writing a lab report. You may decide to do some programming to improve the performance of the code that you turned in for your last lab. Doing the Lab Step 1: Code and test the DNAStrand First, you will code DNAStrand, which should implement the DNAStrand interface. In the Linked List representation, each piece of the recombinant DNA will be held in a separate node in the linked list. Java s class implements the List interface. As a result, the way that you add DNA segments to the recombinant DNA uses the same calls as what you used for. The type of your recombinant DNA variable and the type that you construct should be <String>. 1

2 For example, suppose the original DNA sequence is ACGTGAGTGG, the enzyme used for splitting is GTG and the DNA sequence to splice in is AAAA. In this case, there are 2 occurrences of the enzyme in the original DNA sequence. Each will be replaced with AAAA. The final recombinant DNA will be a linked list containing several nodes: AC AAAA A AAAA G null Notice that the recombinant DNA sequence can be read by walking the linked list and concatenating the strings together. You should create a JUnit test class for the Linked List implementation to ensure that it works correctly. Step 2: Familiarize yourself with the DNABenchMark class. You should read through this class to understand what it does. It provides 3 types of experiments: Varying DNA length - In this experiment for each trial, the length of the DNA sent to cutandsplice is increased, while the enzyme and splicee are unchanged. Varying enzyme length - In this experiment for each trial, the enzyme length is changed, while the DNA and splicee are kept the same. Varying splicee length - In this experiment for each trial, the splicee length is changed, while the DNA and enzyme are kept the same. Each trial is repeated multiple times, with the result of each execution written to the Console. A typical line of output from this program looks like this: dna.dnastrand: enzyme 6 splicee 256 time before 320,160 recomb 331,410 Here is how to interpret a line of the output: dna.dnastrand - identifies which implementation of DNA strand was used enzyme - length of the enzyme searched for splicee - length of the DNA sequence being spliced in time - how long a single call to the cutandsplice method took, measured in seconds before - length of the original DNA sequence before calling cutandsplice. recomb - The length of the recombined DNA created by cutandsplice Each experiment is run multiple times and stops when one of these conditions is met: A predefined number of executions completes successfully. A single execution takes longer than a predefined limit. In this case the output will contain a line OverTimeException. Java runs out of memory. In this case, the output will contain a line OutOfMemory- Error. In addition, the benchmark program writes output to a file called results.csv. After running the program, you will be able to load this file into Numbers, Apple s spreadsheet program, or Excel. For this step, read the DNABenchMark class and answer the following questions: How many times is each trial repeated? What is the maximum time that a trial can run before that experiment is abandoned? What is the enzyme used in the vary DNA experiment? What is the splicee used in the vary DNA experiment? (Describe it; don t type it out.) How long is the original DNA sequence used in the vary DNA experiment? 2

3 How is the DNA changed on each trial for the vary DNA experiment? How is the enzyme changed on each trial for the vary enzyme experiment? How is the splicee changed on each trial for the vary splicee experiment? Step 3: Collect performance data Since all four implementations of DNA strand use the same cutandsplice method, any significant differences in performance must be caused by one of the methods defined in the individual implementations. These are append, size and tostring. Since cutandsplice only uses append, and not the other two, we can further claim that any significant differences in the benchmarking results when we compare implementations must be due to the append method. In the strandsplicebenchmark method, there is an assignment statement that assigns a value to the strand local variable. You should see three statements, two of which are commented out, like this:!!! strand = new DNAStrand(dnaSource);!!! // strand = new DNAStrand(dnaSource);!!! // strand = new DNAStrand(dnaSource); Make sure that the version is uncommented and the other two remain commented out. Run the benchmark program. It is important that you turn off assertion checking since any assertions will slow down your code unnecessarily. To do that, right-click on DNABenchMark.java in the Package Explorer and select Run As and then Run Configurations. You should see a window that looks like the one shown to the right. Click on Arguments. In the field labeled VM arguments, enter: -da -Xmx2048M This will turn off error checking and also cause the program to run with more memory (2 GB) than the default allocated to Java. When running the benchmark programs for this lab, don t do anything else on the computer. Don t read your mail, don t go to Facebook, etc. Running any other programs at the same time will give misleading timing results. You should see a file in your project called results.csv. Right-click on this file. Select Refactor and then Rename. Change its name to Results.csv. 3

4 Repeat this for the other 3 implementations, saving each in their own file. You should turn these files in with your project. Step 4: Compare the results Load the csv files produced by the experiment into a spreadsheet program. You may use Excel or Apple s Numbers program. Use the collected data to complete these tables. For the time, it is sufficient to record just 3 significant digits. For the performance column, indicate if the performance is roughly constant, roughly linear (that is, that the time doubles as the DNA length doubles, for example), significantly worse than linear. It could be a combination. For example, you might find the times to be roughly constant up until some size at which its performance changes. If so, please note these performance changes. DNA length experiment: Max DNA 1 st exec time last exec time Performance Enzyme length experiment: Min Enzyme 1 st exec time last exec time Performance Splicee length experiment: Max Splicee 1 st exec time last exec time Performance Step 5: Discuss the Results Are any of the implementations consistently worse than others? Are any of the implementations consistently better than the others? 4

5 Given what you know about s and s and how cutandsplice is implemented, explain the similarities and differences that you observed in your data. Grading 10 Step 1: Code and test DNAStrand 20 Step 2: DNA Benchmark questions 20 Step 3: Collect performance data 20 Step 4: Compare the performance results 20 Step 5: Discuss the results 5 Comments (including javadoc comments) 5 Style Turning in Your Work Type up your answers to steps 2 through 4. Submit these either as a text file or a pdf file. Turn in the csv files that you created in Step 3. Submit your code as a jar file as usual. Credits This lab was inspired by a Nifty Assignment by Owen Astrachan of Duke University. 5

Senior Symposium Friday, April 16

1 Senior Symposium Friday, April 16 12:30 PM 12:45 PM 2 public DnaStrand cutandsplice(string enzyme, String splicee) { int enzymestart = dnasequence.indexof(enzyme); if (enzymestart == -1) { StringDNA