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CPU Caching and Performance


Caching is one of the most important concepts in computer systems. Understanding how caching works is the key to understanding system performance on all levels. On a PC, caching is everywhere. We’ll focus our attention here on CPU-related cache issues; specifically, how code and data are cached for maximum performance. We’ll start at the top and work our way down, stopping just before we get to the RAM. Feeding the beast

L1, L2, RAM, page files, etc…Why all these caches? The answer is simple: you’ve got to feed the beast if you want it to work. When there’s a large access-time or transfer rate disparity between the producer (the disk or RAM) and the consumer (the CPU), we need caches to speed things along. The CPU needs code and data to crunch, and it needs it now. There are two overriding factors in setting up a caching scheme: cost and speed. Faster storage costs more (add that to the list of things that are certain, along with death and taxes). As you move down the storage hierarchy from on-chip L1 to hard disk swap-file, you’ll notice that access time and latency increase as the cost-per-bit decreases. So we put the fastest, most expensive, and lowest-capacity storage closest to the CPU, and work our way out by adding slower, cheaper, and higher-capacity storage. What you wind up with is a pyramid-shaped caching hierarchy.

So what it all boils down to is this: if the CPU needs something, it checks its fastest cache. If what it needs isn’t there, it checks the next fastest cache. If that’s no good, then it checks the next fastest cache . . . all the way down the storage hierarchy. The trick is to make sure that the data that’s used most often is closest to the top of the hierarchy (in the smallest, fastest, and most expensive cache), and the data that’s used the least often is near the bottom (in the largest, slowest, and cheapest cache). Most discussions on caching break things down according to issues in cache design, e.g. “cache coherency and consistency”, “caching algorithms”, etc. I’m not going to do that here. If you want to read one of those discussions, then you should buy a good textbook. The approach I’ll take here is to start at the top of the cache hierarchy and work my way down, explaining in as much detail as I can each cache’s role in enhancing system performance. In particular, I’ll focus on how code and data work with and against this caching scheme. One more thing, I look forward to getting feedback on this article. As always, if I’m out of line, then please feel free to correct me. And as always, leave the ‘tude at the door. We all try to be professional and courteous here; we’ll respect you if you respect us. That having been said, lets get on with the show.

First, the registers

The first and most important cache is the on-chip register set. Now, I know you’re not used to thinking of the
registers as a cache, but they certainly fit the description. Whenever the ALU (Arithmetic Logic Unit) needs
two numbers to crunch and a place to put the results, it uses the registers. These registers are the fastest form of
storage on the CPU, and they’re also the most expensive. They’re expensive in terms of the logic it takes to
implement them. More registers means more chip real estate, and chip real estate is the priciest real estate there
is (with Boston and NYC running second and third respectively). However, having more registers makes
parallelizing code easier.
Parallel Power
The beauty of modern superscalar architectures is that they can perform more than one operation at once. Now
there’s more than one reason you’d want to do this. Some types of problems can be solved quicker if you break
them up into smaller parts and then do all the smaller parts all at once. For instance, say you wanted to find all
the prime numbers between 1 and 100. One way to do it would be to start at 1 and check each consecutive
CECS 440 Computer Architecture © 1999 R. W. Allison
Caching – Page 2
integer for primeness until you get to 100. However, there’s a better way to do this if you have the resources:
Split the problem up 10 ways among 10 friends. Give each of your 10 friends 10 numbers to check for
primeness, and let them all do it at the same time. Assuming all your friends work at the same speed, then
you’ll get the answers ten times as fast. That’s parallel processing.
The problem with parallel processing is that only certain types of problems lend themselves to this type of
processing. Some problems you just can’t break up. For instance, if you have a string of complex calculations
(or decisions), and each calculation (or decision) depends on the one before it for at least one of its inputs, then
you can only do those one at a time in sequence. Or can you? Here’s where branch prediction and large
register sets come in. What branch prediction does is let you use subsets of registers to execute down multiple
branch paths simultaneously. So while you’re waiting for that calculation to finish, you can make a guess as to
what its outcome will be and then go on with the sequence using a different register subset while the calculation
you were waiting on is still wrapping up. Of course if you guess wrong, then you’ve got to throw out all of
those speculative results, which means you’ve just wasted your resources on worthless calculations. Not a good
thing. Modern branch prediction algorithms are very good, though, and bad guesses don’t happen often.
When you need data from a register to do a calculation but the data in that register is unstable or is still subject
to unfinished calculations, this is called a dependency. Dependencies are bad, and the more registers you have,
the easier it is to avoid them. Branch prediction sounds wonderful, but the problem with it is that it takes a lot
of logic. Prediction logic must unravel code as it’s running, look for dependencies, and make predictions.
Often, that logic could be better used elsewhere. When IA-64 comes out . . . nope, I’d better save that for the
next article.

The L1 cache — It’s the code, stupid
I’m not going to talk about write policy, or unified vs. instruction/data caches. These things affect performance,
but there are excellent write-ups of them elsewhere. For a good introduction to L1 cache technology, check out
the PCGuide.
What I want to discuss here is how code and data interact with the cache to affect performance. For this
particular discussion, I’m going to take the Pentium II’s L1 cache as my starting point. The PII’s L1 is split into
a 16KB instruction cache and a 16KB data cache. That’s all we need to know about the PII’s L1 to get started.
The L1 cache’s job is to keep the CPU supplied with instructions and the registers supplied with data.
Whenever the CPU needs its next instruction, it should be able to look into the L1 cache and find it instantly.
When the CPU doesn’t find the instruction in the L1 cache, this event is called a cache miss. The CPU then has
to go out to the L2 and hunt down that instruction. If it ain’t in the L2, the CPU goes out to RAM, and so on.
The same goes for data.
The ideal caching algorithm knows which bits of code and data will be needed next, and it goes out and gets
them. Of course, such an ideal algorithm can’t actually be implemented, so you have to do your best to guess
what’s coming up. Modern caching algorithms are pretty good at guessing, generating hit rates of over 90%.
Caching Code
The key to caching instructions is to know what code is being used. Lucky for all of us, code exhibits a property
called locality of reference. This phrase is a fancy way of saying that after you execute an instruction, the odds
that the next instruction you’ll need to execute is stored somewhere very nearby in memory are damn good. But
the type of code you’re running affects those odds. Some code gives better odds on easily finding the next
instruction, and some gives worse.

Business Apps
With a business app like MS Word, there are lots of buttons and menus and pretty things to play with on the
screen. You could press any one of them at any time, and completely different bits of code from completely
different parts of memory would have to be put into the L1. There’s no way of knowing whether the user is
going to stop typing and fire up the “insert table wizard”, or the annoying “Office Assistant” (does anybody else
hate that thing?), or the drawing tools. Such are the joys of event-driven programming. Now, within a particular
menu selection or wizard, there’s a pretty high degree of locality of reference. Lines of code that do related
tasks tend to be near each other. But overall, an office program like Word tends to call on code from all over the
map. In a situation like this, you can see why the number of L1 cache misses would be pretty high. It’s just
tough to predict what code will need to execute next when the user switches tasks so often.

3D Games/Simulation/Image Processing
So now let’s talk about 3D Games like Quake and uh…what’s that other game…Unplayable, Unoriginal,
Uncool, Unstable,…oh! Unreal! (I’d blocked it out of my mind.) These games, and any similar program, make
very good use of the L1 cache. Why is that? There are a number of reasons, but one thing probably accounts
for most of their success: matrices.
Simulations use matrices like you wouldn’t believe. 3D simulations use matrices for a whole host of tasks,
including transformation and translation. Translating or transforming a 3D object requires iterating through
various matrices. Even other types of simulations like neural networks use matrix math. All a neural net really
consists of is a set of matrices describing the weights on connections between neurons, along with input and
output vectors. When the network learns, these weights have to be updated, a task which requires you to iterate
through the weight matrices.

Image processing programs are also matrix intensive. When an image is loaded into memory, it’s converted into
a matrix of RGB values. Histograms, filters, edge detection, and other image processing functions are all done
by iterating through the image matrix and changing the values in it. To iterate through a matrix you need lots of
loops, and these loops often fit in the L1 cache. So when you’re operating on matrix data you’re using a small
amount of code to crunch a large amount of data. The algorithms which this code implements are loop-based
and very sequential (those of you who’ve sat through a differential equations course know what I’m talking
about). Actually, most numerical methods programming involves highly sequential code which doesn’t jump
around too much.

Some Clarifications
I can already see people writing in and explaining to me in detail how the network code or some other piece of a
game like Quake involves all sorts of user input and unpredictability just like an office app. This is true, but it’s
also true that the part of a simulation/game like Quake that is the most CPU-intensive is the rendering engine.
And the rendering engine is a fairly sequential, loopy, and predictable mathematical beast—just exactly the sort
of animal that makes excellent use of the L1 cache.

Caching Data
The differences between business apps and simulations in terms of code caching pretty much apply to data
caching, as well. I haven’t written any business apps, so I won’t say any more about them than I’ve already
said. If anybody out there has written a word processor or something and would like to explain how it uses the
L1 cache, you’re welcome to send in a good explanation, and we’ll post it.
I may not have written any business apps, but I sure as hell have written simulations and math routines out the
waz (my undergraduate focus was on math and programming). So I do know something about how these types
of programs use data. As in the above discussion on code, the most important thing you can say about these
types of programs is that they use lots of matrices. Most good programmers know how to use double pointers
to create a dynamically allocated matrix which stores its data in a grid of contiguous memory addresses. Since
the matrix data are stored in contiguous memory addresses, and they get iterated through a lot, then you can
imagine how easy it is to load a whole hunk of a matrix into the L1 and crunch on it for a while. And when
you get done with that piece, you know right where the next one is coming from.

The L2 cache

The L2 isn’t as glamorous (in my opinion) as the L1. It’s gotten lots of press lately though, so I guess I should
say a bit about it. It sort of occupies this in-between limbo between the L1 cache (which is very important for
increasing performance) and the RAM (which is essential for the machine to even function at all). Basically, the
only reason that the L2 is there is in case there’s a miss in the L1. If the cache hit rate for the L1 were 100%,
then we wouldn’t need the L2. The reason the L2 does actually get press is because the L1 does miss, and when
it misses it hurts to have to go all the way out to RAM to get the needed data. So the L2 sort of acts as a waystation
between the L1 and the RAM. That way the penalty for an L1 miss isn’t quite as stiff. And now that I’ve
explained what types of things affect whether or not the L1 misses, it should be pretty obvious why the L2 is
more important in business apps than in games.
A Word About FP and INT Performance and Cache Usage
A business application like Word is integer, not floating-point, intensive. This fact, along with Word’s poor
performance on the cacheless Celeron, misled some folks into thinking that integer intensiveness and L2 cache
usage are somehow related. This isn’t necessarily the case. It just so happens that applications that are integerintensive
also happen to have lower locality of reference than floating-point-intensive apps.





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  1. Jessika_Smit

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    Answer: No






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