Concept Challenge: Relating Timing Data to Algorithm Analysis

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来自 University of California San Diego 的课程

Data structures: Measuring and Optimizing Performance

1481 个评分

University of California San Diego

Data structures: Measuring and Optimizing Performance

1481 个评分

课程 4（共 4 门，Specialization Object Oriented Programming in Java）

How do Java programs deal with vast quantities of data? Many of the data structures and algorithms that work with introductory toy examples break when applications process real, large data sets. Efficiency is critical, but how do we achieve it, and how do we even measure it? In this course, you will use and analyze data structures that are used in industry-level applications, such as linked lists, trees, and hashtables. You will explain how these data structures make programs more efficient and flexible. You will apply asymptotic Big-O analysis to describe the performance of algorithms and evaluate which strategy to use for efficient data retrieval, addition of new data, deletion of elements, and/or memory usage. The program you will build throughout this course allows its user to manage, manipulate and reason about large sets of textual data. This course is designed around the same video series as in our first course in this specialization, including explanations of core content, learner videos, student and engineer testimonials, and support videos -- to better allow you to choose your own path through the course!

从本节课中

Efficiency Analysis and Benchmarking

Welcome to week 3! The text-editor application you worked with last week does something, but it doesn't do it particularly fast. This week we'll start talking about efficiency. We'll introduce the concept of "Big-O" notation, which sounds a little silly, but is really a powerful (and extremely common) way of analyzing a program's efficiency, independent of the system that it's running on and the exact details of how it's implemented. Then we'll go the other direction and dive into the details, talking about how to measure the actual running time of a piece of code to get an idea of how it really performs in practice.

Core: Introduction to Benchmarking6:56

Core: Using Java Time3:24

Core: Analyzing Timings7:25

Concept Challenge: Relating Timing Data to Algorithm Analysis4:49

与讲师见面

Christine Alvarado
Associate Teaching Professor
Computer Science and Engineering
Mia Minnes
Assistant Teaching Professor
Computer Science and Engineering
Leo Porter
Assistant Teaching Professor
Computer Science and Engineering

[NOISE] So, we've talked about some algorithms and we've talked about using

benchmarking and timing to try to analyze the performance of those algorithms.

And so now we're ready for a concept challenge to bring those ideas together.

And so as you know, for a concept challenge,

what we ask you to do is to pause and try to solve the problem yourself.

Then try to find and opportunity to discuss your solution with other learners,

either on the forum or with people learning with you.

Then you'll have an opportunity to watch the UC San Diego Learners

discuss the problem, and then you'll be able to answer the question again.

And then confirm your understanding as we go through the solution together.

So, for this particular question, what we'd like to do is look at some real data

for a sorting algorithm that we ran on bigger and bigger and bigger collections.

And so, in particular, could these be the timings,

the real timings for insertion sort?

So, take a look, answer the question, and then we'll come back later.

>> Hi, I'm Tina.

>> Hi, my name is Gev.

>> Hi, I'm Mia.

>> All right, so looking at the visual data on this graph,

it seems to fall along a linear one mostly.

So, I would think it'd be like say, of and for the run time.

I don't know if it would be for insertion sort.

>> Cuz we learned about a lot of different sorts and

insertion sort wasn't really the most efficient, you know?

>> Yeah.

You're right.

I remember talking about merge sort and quick sort and

there are n log n and that's one of the best possible times you can get.

And what this looks like, it looks like all then and

that kind of sounds too good to be true for insertion sort.

>> If it's too good to be true,

then maybe we can probably rule out that this wouldn't be a worst case time.

>> Yeah. >> Maybe it could be, best case then?

>> So you've seen the UC San Diego learners discuss these timings that we

were seeing and think really about whether an actual machine

running insertion sort on bigger and bigger data could produce these timings.

So, let's tie back the timings that we got from running the code

to the analysis that we did earlier in the course trying to think about

the asymptotic behavior of various sorting algorithms and procedures.

And, in particular we're thinking about insertion sort.

And when we analyze insertion sort using big O notation,

we saw that on average, and in the worst case, big O behaves like, I'm sorry,

insertion sort has a quadratic behavior.

It's got big O of n squared.

But when we look at this graph, that doesn't seem to jive, right?

The graph looks very linear, it's going up at a constant rate.

It appears linear and not quadratic.

It doesn't look like a parabola.

And so we're trying to understand how is this possible.

And one clue comes from looking at that other cell in the table and

seeing that there is a case when insertion sort can behave like a linear algorithm.

And so let's think back to when we might be in that best case performance,

that best case scenario.

And to think about that,

we have to go back to the code of how we go ahead and implement insertionSort.

And we realize that the variations in performance,

the variations in timing for the insertion sort algorithm come from this while loop.

And this while loop executes a different number of times

based on how organized or unorganized the data is to begin with.

So, if we start with really almost sorted or fully sorted data,

then this while loop just checks whether our current element that

we care about is in it's correct location relative to the other elements and

if it is, we don't need to do anything.

And, so, if all of the tests that we did to create that timing chart

that we saw before.

If all of those tests were on really, really big, but sorted data,

then we could very well have gotten that linear behavior that we saw, because that

insertion sort algorithm is super fast on sorted or nearly sorted data.

So indeed, that timing chart that we saw could be the result of running just basic

old insertion sort on really big data as long as we know something about that data.

And so the moral of this story is that when we look at timing data we have to be

aware of drawing conclusions from that timing data.

And, we wanna be cautious about trying to deduce the big O complexity and

classes of an algorithm just based on the timing.

That's not enough.

Usually we need to understand what the data that we were testing on looks like

and whether there's other factors at play as well.

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