Contents
This week’s guest post is by Matthew Butler, who shows us some insights about how the relationship between complexity and performance can be less than obvious in multiple ways. Matthew is a systems architect and software engineer developing systems for physics research, network security, law enforcement and the Department of Defense. He primarily works in C/C++ and Modern C++ and can be found on Twitter.
There’s a story that’s been told for years about Jon Bentley (Programming Pearls, AddisonWesley, 1986) coming excitedly into Bjarne Stroustrup’s office one day and posing a problem to him:
“Insert a sequence of random integers into a sorted sequence, then remove those elements one by one as determined by a random sequence of positions. Do you use a vector or a linked list?”
I’m not sure if this is a true story or even if it even happened that way, but it brings up an interesting point about algorithm complexity and data structures.
If we analyze the problem from a strict complexity viewpoint, linked lists should easily beat arrays. Randomly inserting into a linked list is O(1) for the insertion and O(n) for finding the correct location. Randomly inserting into an array is O(n) for the insertion and O(n) for finding the correct location. Removal is similar.
This is mainly because arrays require the movement of large blocks of memory on insertion or deletion while linked lists just swizzle a few pointers. So, by a strict complexity analysis, a list implementation should easily win.
But does it?
I tested this hypothesis on std::list, which is a doublylinked list, and std::vector. I did this for a data set of a small numbers of elements: 100, 1,000, 2,000, 3,000, 4,000, 5,000, 6,000, 7,000, 8,000, 9,000 & 10,000. Each run was timed using a highresolution timer.
Code segment for std::list:
while (count < n)
{
rand_num = rand();
for (it = ll.begin(); it != ll.end(); ++it)
if (rand_num < *it)
break;
ll.insert(it, rand_num);
++count;
}
while (count > 0)
{
rand_num = rand() % count;
it = ll.begin();
advance(it, rand_num);
ll.erase(it);
count;
}
Code for std::vector:
while (count < n)
{
rand_num = rand();
for (i = 0; i < count; ++i)
if (rand_num < vec[i])
break;
vec.insert(vec.begin() + i, rand_num);
++count;
}
while (count > 0)
{
rand_num = rand() % count;
vec.erase(vec.begin() + rand_num);
count;
}
Code for an optimized std::vector that uses a binary search to find the insertion point and reserve() to keep the vector from being relocated as it grows.
vec.reserve(n);
while (count < n)
{
rand_num = rand();
it = std::lower_bound(vec.begin(), vec.end(), rand_num);
vec.insert(it, rand_num);
++count;
}
while (count > 0)
{
rand_num = rand() % count;
vec.erase(vec.begin() + rand_num);
count;
}
The Results
Figure 1 – Performance curves (load vs time) for std::list (green), std::vector (blue) and an optimized std::vector (red) using integers. A lower line means better performance.
Clearly, std::list loses badly. But the surprising result is the nearly flatlined performance of the optimized version of std::vector. So how did we get graphs that defy what complexity analysis told us we should see?
This is one of the central failings of complexity analysis. Complexity analysis only looks at the data structure & algorithm as if they’re running in the aether. It doesn’t take into account the relativistic effects of the hardware we’re running on. In this case, the CPU cache and prefetcher work under the hood to make sure that the data we need is preloaded into the cache lines for faster access.
std::vector, which is just a block of memory, is easy for the prefetcher to reason about given our linear access patterns. It anticipates the next blocks of memory we’ll want to access and has them loaded and ready when we try to access them.
Linked lists, on the other hand, can’t be prefetched since each new link points somewhere else in memory and the prefetcher can’t reason about that. Each move down the list becomes a cache miss causing the CPU to spill the cache line and refill it with a different block of memory.
This means that accessing the next element goes from 0.9ns (if it’s in the cache already) to 120ns to get it loaded from main memory. In this case, the best quality of std::list – the ability to swizzle some pointers to insert or delete – is also it’s Achilles heal on cachebased architectures.
If you looked at the code above, you also noticed that I used random access to delete from the vector. While this may seem to be an advantage, it’s really not. There’s no guarantee that the next value to be removed is anywhere near the last one and the prefetcher has no understanding of how you structured your data in memory. It just sees memory as one long, formless stream. That means that you potentially take cache misses depending on how big the array is and where you’re looking.
But what about the use of binary search?
That’s a pseudorandom access pattern which should cause a fair amount of cache misses. And yet the “performancetuned” std::vector was blazingly fast even with it’s cache misses.
There are a few things to keep in mind:
 We did O(log n) accesses for a binary search which is far fewer than a linear traversal which is O(n).

The branch predictor works to make the single if() statement inside the binary search more efficient by predicting which outcome is more likely on each loop.

We preallocated the entire array which means that it didn’t have to be relocated as it grew and potentially ran out of space.
Larger Data
But what happens if the data we’re handling isn’t an integer? What if it’s something larger, like a 4K buffer?
Here are the results using the same code but using a 4K buffer.
Figure 2 – Performance curves (load vs time) for std::list (green), std::vector (blue) and an optimized std::vector (red) using 4K buffers. A lower line means better performance.
The same code with a larger data size now performs very differently. Linked lists come into their own and not only erase the speed advantage of an array, they nearly erase the advantage of the optimized version.
That’s because blocks of our array no longer fit neatly into a cache line and the prefetcher is having to go back to main memory over and over again causing the same sort of cache misses we see in linked lists. Plus you have the overhead of inserting into an array which causes memory moves of large segments of memory.
The Takeaways:

Always test your solutions because that’s the only true measure of performance. Our intuition is almost always wrong. In this case, complexity analysis was wrong about the outcome because complexity analysis fails to take into account the operational environment. Specifically, the effects of caching, the prefetcher, branch prediction and access patterns in memory.

Treat operations involving > as very expensive operations because they involve cache misses. That’s the primary reason that std::list failed so badly. std::vector used the same linear search that std::list used but because the prefetcher & branch predictor kept the cache full for us it performed much better.

Know the standard algorithms. Knowing that lower_bound() is a binary search gives us a massive performance boost. It also simplified the algorithm and added some safety margin because looping through a vector using operator[] is somewhat dangerous in that it potentially allows us to run past the end of the vector without knowing it. Rangebased for loops are a better choice.

Understand the performance characteristics of the containers you’re using and know what specific implementations they use. std::multimap is typically built on a redblack tree while std::unordered_map is based on a hash table with closed addressing and buckets. Both are associative containers, but both have very different access patterns and performance characteristics.

Don’t automatically assume that std::vector is always the fastest solution. That’s heresy today given how well it performs on cachebased hardware. With larger sized elements, though, it loses many of its advantages. And even though it’s not hard to roll a vector into an associative container, there are problems it does not handle well such as parsing (tries are better for that) or networks (directed graphs are better). Saying that all we need is a vector and a flat hash map with open addressing and local probing is a bit shortsighted.

Don’t assume that the branch predictor, prefetcher or cache will make inefficient code run faster. In the vector implementation, it would be tempting to assume that reading vec.size() on each iteration instead of using count would be just as fast. In this case, that’s actually not true so test to be sure.

Element size counts. Integers are small but if the items being accessed are large (structured data, say), linked lists erase a lot of that speed advantage that arrays have.

Remember that complexity analysis is a measure of efficiency – not performance.
Permalink
Similar to Fritz, I disagree with your statements regarding complexity theory. Taken as a whole, all three code variants each form an algorithm of O(n^2) w.r.t. to the number of elements. From a complexity point of view they are therefore equivalent.
After all, the big O only describes the worst asymptitical complexity for n towards infinity. It is sure that there will be a finite N where the algorithm of lower complexity outperforms the algorithms of higher complexity. But such an N can be quite big, and it is common that “worse” algorithms outperform “better” alternatives for common data set sizes. This is not a fail, it is something which is considered irrelevant, at least by mathematicians :). I think it is kind of obvious: If there is a bound for the problem size, measure and take the better alternative independent of its complexity class. If it is or may be necessary to scale up, you should consider to prefer an algorithm of lower over one of higher complexity event if it is currently somewhat slower.
Your assumptions regarding performance advantages (and disadvantages) look sound, but eventually remain kind of vague. It would be interesting to substantiate those by an analysis using hardware performance counters or by doing experiments with disabled prefetchers or similar.
Permalink
That’s was actually the point of the post.
All three algorithms should have similar runtimes because they are roughly equivalent. Although lower_bound() is a O(log n) algorithm so the optimized vector is an O(n log n) algorithm not an O(N^2) algorithm.
But first two don’t behave the same because the runtime complexity measure we use is inadequate. It does not take into account (or “fails” to take into account) how the hardware effects the runtime performance of the data structures and algorithms.
And that’s the issue.
Thanks for your comment.
Permalink
Hi Matthew,
even if you replace the lower_bound with vec.begin() + count / 2 (which is O(1), the upper loop remains O(n^2) – not to forget the second loop, which is the same as in the unoptimized variant and happens to be O(n^2), too.
So one could also put it the other way around: As long as your whole algorithm stays in the same complexity class, optimizing individual operations may not matter that much anymore if your N gets larger!
Best regards,
Markus
Permalink
To say „complexity theory“ fails is a bit odd. You are actually using a hammer to drill a hole. The complexity measure you use was invented to separate polynomial time solvable problems from those which are not. A better measure would be the external memory model by Aggarwal and Vitter or the cache oblivious model by Leiserson and Prokop.
Permalink
Runtime complexity in this form is a bit of a blunt instrument. That was the premise of the post. It’s also the generally accepted standard for the software industry which is why it’s an important topic.
It’s only partially correct to say that it “was invented to separate polynomial time solvable problems from those which are not.” While it does do that, it’s more used to compare different algorithms and data structures within the same class. Vector vs Linked List, Quick Sort vs Merge Sort, Set vs Hash Map, linear vs binary search, et al.
The articles look interesting. The problem with academic papers, though, is that they tend not to translate well into the industry until they’re results are generalized
I think that would make an excellent post for you to write since you clearly have an interest in this subject. I would be very interested in reading it and I’m sure that Arne would love to consider it for a guest post.
Thanks for your comment.