How Misaligning Data Can Increase Performance 12x by Reducing Cache Misses

Here’s the graph of a toy benchmark1 of page-aligned vs. mis-aligned accesses; it shows a ratio of performance between the two at different working set sizes. If this benchmark seems contrived, it actually comes from a real world example of the disastrous performance implications of using nice power of 2 alignment, or page aligment in an actual system2.

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Except for very small working sets (1-8), the unaligned version is noticeably faster than the page-aligned version, and there’s a large region up to a working set size of 512 where the ratio in performance is somewhat stable, but moreso on our Sandy Bridge chip than our Westmere chip.

To understand what’s going on here, we have to look at how caches organize data. By way of analogy, consider a 1,000 car parking garage that has 10,000 permits. With a direct mapped scheme (which you could call 1-way associative3), each of the ten permits that has the same 3 least significant digits would be assigned the same spot, i.e., permits 0618, 1618, 2618, and so on, are only allowed to park in spot 618. If you show up at your spot and someone else is in it, you kick them out and they have to drive back home. The next time they get called in to work, they have to drive all the way back to the parking garage.

Instead, if each car’s permit allows it to park in a set that has ten possible spaces, we’ll call that a 10-way set associative scheme, which gives us 100 sets of ten spots. Each set is now defined by the last 2 significant digits instead of the last 3. For example, with permit 2618, you can park in any spot from the set {018, 118, 218, …, 918}. If all of them are full, you kick out one unlucky occupant and take their spot, as before.

Let’s move out of analogy land and back to our benchmark. The main differences are that there isn’t just one garage-cache, but a hierarchy of them, from the L14, which is the smallest (and hence, fastest) to the L2 and L3. Each seat in a car corresponds to an address. On x86, each addresses points to a particular byte. In the Sandy Bridge chip we’re running on, we’ve got a 32kB L1 cache with 64-byte line size and, 64 sets, with 8-way set associativity. In our analogy, a line size of 64 would correspond to a car with 64 seats. We always transfer things in 64-byte chunks and the bottom log₂(64) = 6 bits of an address refer to a particular byte offset in a cache line. The next log₂(64) = 6 bits determine which set an address falls into5. Each of those sets can contain 8 different things, so we have 64 sets * 8 lines/set * 64 bytes/line = 32kB. If we use the cache optimally, we can store 32,768 items. But, since we’re accessing things that are page (4k) aligned, we effectively lose the bottom log₂(4k) = 12 bits, which means that every access falls into the same set, and we can only loop through 8 things before our working set is too large to fit in the L1! But if we’d misaligned our data to different cache lines, we’d be able to use 8 * 64 = 512 locations effectively.

Similarly, our chip has a 512 set L2 cache, of which 8 sets are useful for our page aligned accesses, and a 12288 set L3 cache, of which 192 sets are useful for page aligned accesses, giving us 8 sets * 8 lines / set = 64 and 192 sets * 8 lines / set = 1536 useful cache lines, respectively. For data that’s misaligned by a cache line, we have an extra 6 bits of useful address, which means that our L2 cache now has 32,768 useful locations.

In the Sandy Bridge graph above, there’s a region of stable relative performance between 64 and 512, as the page-aligned version version is running out of the L3 cache and the unaligned version is running out of the L1. When we pass a working set of 512, the relative ratio gets better for the aligned version because it’s now an L2 access vs. an L3 access. Our graph for Westmere looks a bit different because its L3 is only 3072 sets, which means that the aligned version can only stay in the L3 up to a working set size of 384. After that, we can see the terrible performance we get from spilling into main memory, which explains why the two graphs differ in shape above 384.

For a visualization of this, you can think of a 32 bit pointer looking like this to our L1 and L2 caches:

TTTT TTTT TTTT TTTT TTTT SSSS SSXX XXXX

TTTT TTTT TTTT TTTT TSSS SSSS SSXX XXXX

The bottom 6 bits are ignored, the next bits determine which set we fall into, and the top bits are a tag that let us know what’s actually in that set. Note that page aligning things, i.e., setting the address to

???? ???? ???? ???? ???? 0000 0000 0000

was just done for convienence in our benchmark. Not only will aligning to any large power of 2 cause a problem, generating addresses with a power of 2 offset from each other will cause the same problem.

Nowadays, the importance of caches is well understood enough that, when I’m asked to look at a cache related performance bug, it’s usually due to the kind of thing we just talked about: conflict misses that prevent us from using our full cache effectively6. This isn’t the only way for that to happen – bank conflicts and and false dependencies are also common problems, but I’ll leave those for another blog post.

Well, I’ve now managed to blog about two of the areas where I have the biggest comparative advantage. Three or four more blog posts and I’ll be able to write myself straight out of my job. I must be moving up in the world, because I was able to automate myself out of my first job with a couple shell scripts and a bit of Ruby. At least this requires some human intervention.


  1. The Sandy Bridge is an i7 3930K and the Westmere is a mobile i3 330M

  2. Or anyone who aligned their data too nicely on a calculation with two source arrays and one destination when running on a chip with a 2-way associative or direct mapped cache. This is surprisingly common when you set up your arrays in some nice way in order to do cache blocking, if you’re not careful.

  3. Don’t call it that. People will you look at you funny the same way they would if you pronounced SQL as squeal or squll.

  4. In this post, L1 refers to the l1d. Since we’re only concerned with data, the l1i isn’t relevant. Apologies for the sloppy use of terminology.

  5. If it seems odd that the least significant available address bits are used for the set index, that’s because of the cardinal rule of computer architecture, make the common case fast – Google Instant completes “make the common” to “make the common case fast”, “make the common case fast mips”, and “make the common case fast computer architecture”. The vast majority of accesses are close together, so moving the set index bits upwards would cause more conflict misses. You might be able to get away with a hash function that isn’t simply the least significant bits, but most proposed schemes hurt about as much as they help while adding extra complexity.

  6. Cache misses are often described using the 3C model: conflict misses, which are caused by the type of aliasing we just talked about; compulsory misses, which are caused by the first access to a memory location; and capacity misses, which are caused by having a working set that’s too large for a cache, even without conflict misses. Page-aligned accesses like these also make compulsory misses worse, because prefetchers won’t prefetch beyond a page boundary. But if you have enough data that you’re aligning things to page boundaries, you probably can’t do much about that anyway.