Sunday, September 12, 2010

Notes on Software Design, Chapter 10: Run-Time Friction

So, here is the story. I keep a lot of notes. Some are text files with relevant links and organized ideas. Most are rather embarrassing scribbles on just about any piece of paper that is lying around when I need it. From time to time, I move some notes from paper to files, discard concepts that didn't prove themselves, and rearrange paragraphs to fake some kind of logical, sequential reasoning over a process that was, in fact, rather chaotic. Not surprisingly (since software is just another way to encode knowledge), David Parnas suggested long ago that we could do the same while documenting software design (see "A rational design process: How and why to fake it").
Well, it's not always easy. Sometimes, I try to approach the storytelling from an angle, see that it doesn't work out so well, and look for another. Sometimes I succeed, sometimes I don't (although, of course, the reader is the ultimate judge). This time, I have to confess, I feel like I couldn't find the right angle, the right way to start, to unfold a concept in a way that makes it look simple and natural. So I'll trust you to be smart enough to make sense of what follows :-). It's a very long post, and you may want to digest it in more than one session.

The physical world
I guess you all had to push some furniture around at one time or another. You have probably felt a stronger resistance in the beginning, followed by a milder form of resistance as soon as you got some movement.
The mild resistance is due to kinetic friction, while the initial, stronger resistance is usually due to static friction, that you have to overcome before moving the object (if you're not familiar with kinetic and static friction, wikipediawill tell you more than you want to know :-).

As you move your stuff around, friction makes you waste some energy, in a way that is basically proportional to the normal force, the distance, and the coefficient of kinetic friction (see the page above for the actual equation). I'll get back to this later, but if you move a constant mass on a flat surface, the energy you waste is proportional to the mass you move, the distance you go, and the magic coefficient of friction.

The beauty of all this is that it's simple and rather unambiguous. Friction is always present in mechanical engineering, but it's a well understood concept (as far as engineering is concerned; it's still blurry at the quantum level, at least for the uninitiated like myself), and there is usually no wishy-washy talking about friction. It's not a broad concept, that is, you won't be able to design the next-generation jet engine if all you have in your conceptual toolbox is friction, yet you won't be able to design an engine at all without an appreciation of friction.

The software world
I'm first and foremost a software design practitioner: I design software, almost every day. Sometimes by myself, most often with other people; therefore, I do a lot of "design talk". In many cases, at one point or another, someone is going to bring in "performance" or "efficiency" to support (or reject) a design decision.
We use those words a lot, with different meaning depending on who's saying it and why he's saying it. It seems like I'm never tired of linking wikipedia, so here is a page on computer performance. Just look at the initial list of different, context-dependent meanings. It's not surprising, then, to find out some people have very peculiar views of performance. "I use arrays because they're more efficient". Sure, except that then you do a linear search because you need multiple indexes; say "efficient" again :-)?

One might expect Computer Science (with capital letters :-) to come to the rescue and define terms more precisely, and hopefully with some relevance for practice. However, computer science is more concerned with computational complexity theory than with the nitty-gritty details of being "fast".
Now, don't get me wrong. You won't get too far as a programmer (and definitely not as a software designer) if you don't get the concept of complexity classes, if you can't see that an algorithm is O( n^2 ) and another is O( n log n ), or if you don't even know what the Big Oh notation is all about. You have to know this stuff, period. In a sense, complexity theory is part of the math of software, and there is little point in investigating a physics of software if you don't get the math first. But math alone won't cut it. However, once we get past the complexity class we get very little assistance from computer science (and I'm purposely ignoring the fact that just because an algorithm is in the O( n log n ) class in the average case doesn't mean I can't beat it with an O( n^2 ) algorithm in my practical cases).

On the "software engineering" side, the usual advice is to build the program and then use a profiler. Yeah, well, sure, beats banging your head against the wall :-), but it's not exactly like knowing what you're doing all along. Still, we make a lot of low-level design decisions while coding, and many of them will ultimately impact "performance". Lacking the basic terminology to think (and talk) about this kind of stuff is rather depressing, so why don't we try to move just a tiny step forward?

Wasting energy in software
So, here I have this piece of software (executable knowledge). For most practical applications, what I need is to get some data (interactively, from a DB, through some kind of device, whatever), transform it in a meaningful way (which could be a complex process encoded in thousands of lines), and spit out some results (which is still data, anyway). The transformation is the Function.

On the artifact side, our software may be using global variables all around, or be based on a nice polymorphic structure, yet the Function doesn't care. The structure we provide on the artifact side is the domain of Form (by now, you're probably familiar with all this stuff).

Now, transformation is a process, and no real-world process is 100% efficient; it's always going to waste something. Perhaps we should look better at that "waste" part. Something I learnt a long time ago, while pondering on principles and patterns, is that overly general concepts (like "performance") must give way to more specialized notions.

Some code, please :-)
Consider this short portion of C code. I'm using C because it's a low-level language, where the implications of any given choice are relatively easy to understand.
double max( double x, double y )
if( x > y )
return x;
return y;

double max3( double x, double y, double z )
double d = max( x, y );
d = max( d, z );
return d;
The code is pretty obvious. In a common, stack-based CPU architecture, max3 will copy x and y on the stack and call max; then it will copy d and z and call max again. The return value might be stored in a CPU register or in RAM, depending on the compiler.

Copying those values is a waste of energy, of course. I could manually inline max inside max3 and get rid of that waste. I would sacrifice reusability and perhaps clarity for "higher performance" or "higher efficiency" or "reduced waste". Alternatively, the compiler could inline the function on my behalf (see Chapter 6 for the role of languages on balancing the two worlds).

What if I'm working on some large data structure? The Fortran guy down the corner will suggest that by keeping your structures in the common area / global memory, you won't even have to pass parameters around: every function knows exactly where to get input and where to store output! Again, we'll sacrifice reusability, and perhaps duplicate large portions of code, for sake of efficiency. As you go through most literature on High Performance Computing (see, for instance, The Ideal HPC Programming Language, recently reprinted in Communications of ACM), you'll see that the HPC community is constantly facing the problem of wasted cycles, and is wasting a lot of LOC to prevent that.

Moving data around is not the only way to waste energy. Consider this portion of real-world code, written by a (supposedly) performance-conscious "little meritocracy":
static int is_rfc2822_header(char *line)
int ch;
char *cp = line;
if (!memcmp(line, "From ", 5) || !memcmp(line, ">From ", 6))
return 1;
while ((ch = *cp++)) {
if (ch == ':')
return cp != line;
if ((33 <= ch && ch <= 57) ||
(59 <= ch && ch <= 126))
return 0;
yeah, it's ugly as hell, but it's also wasteful (which is funny, for reasons that are too long to explain here). If you read it carefully, you'll find a way to optimize the "while" body quite a bit, and while you're at it, you can easily make it more readable. Also, the two memcmp in the beginning are wasting cycles (going through the first 5 characters twice), but just like the coefficient of friction must be measured in practice, at this level any alternative should really be measured on a real-world CPU.

Note: as we optimize code, we have to assume that is correct. We don't change Form unless we know that the Function is right. Before posting this, I checked for any update to the codebase (I got that code a few years ago, and it never looked right to me). The code is still the same, but now there is also a comment explaining what the function is intended to do. Unfortunately, it's not what it's doing, which is even more ironic, for the same unspoken reasons above. Anyway, we could easily fix the bug and still optimize the code.

Run-time Friction
Just like in the physical world we move object around, in the run-time world of software we move knowledge around. More exactly, we move data around (we call it data flow) and we move the execution point around (we call it control flow). We move that stuff around to calculate some Function. In the process of calculating the Function, we usually waste some cycles. We waste cycles because we have to copy data on the stack, or from one data structure to another. We waste cycles because we do unnecessary comparison, computations, jumps. We waste cycles because we process the same data more than once. Most often, we waste those cycles because we get something in exchange in the Form (artifact) domain. Sometimes, we waste cycles just because of bad coding.

The energy waste is not a constant: copying an integer is different from copying an array of integers (that's weight, of course). Also, if your array has been swapped out to the paging file, the copy is going to cost you more: that's the contribution of distance, and I'll get back to this later. Right now, remember that wasted energy is a consequence of friction, but is not friction.

Causes and types of software friction
We have already seen a few cases of software friction: when you copy data, you waste cycles. Max3 didn't strictly need to copy data: the Function didn't care about reusing max, only Form did. Before we try do define friction more precisely, it's interesting to see how deep the analogy with real-world friction really is. Indeed, we even have static software friction, and kinetic software friction!

Consider a Java (or .NET) virtual machine. When you hit a function for the first time, the code is compiled just in time. This has nothing to do with Function. It is a byproduct of a technological choice. It will cost you some cycles: that's friction. Also, it happens only once, to "put things in motion": that's static friction. In general, static friction will increase latency, while kinetic friction will reduce throughput. Good: we just sorted out the two main components of "performance".

Consider a web service. Before you can call the server, you go through a relatively lengthy process, from high-level stuff (marshaling your data) to low level stuff (establishing a network connection). This is all friction: the Function is happening on the other side, inside the service code. Here we see both static and kinetic friction at play: establishing a connection adds latency, exchanging data over the network reduces throughput.

Consider stored procedures. The ideal stored procedure takes little data in input, does significant CRUD inside, and returns little. This way, we have minimal waste due to kinetic friction, as we exchange little data with the database. Of course, this is not the only way to minimize energy waste: another approach would be to reduce distance, by bringing the database itself in-process. Interestingly, most real-time databases use the second approach.

So, what is causing friction in software? Friction is caused by:
  • A copy of data from one place to another (e.g. parameter passing, temporary variables, etc), as this adds no meaning to data, and therefore is useless as far as Function is concerned.

  • Syntactical transformation of data (e.g. marshaling) which adds no semantics (as above: this processing is not part of the Function). This includes any form of data transformation needed to talk over a non-native protocol.

  • Unnecessary statements (like those that could be removed in the C function above).

  • Redundant access / processing (some will be removed by the compiler, but some won't)

  • Bookkeeping (allocation, deallocation, reference counting, heap defragmentation, garbage collection, paging, etc). All this adds no semantics, and it's irrelevant for the Function: indeed, a well-written garbage collected program should behave properly under the so-called null garbage collector.

  • Unnecessary indirection. This is a long story and I'll leave for another time, as I've yet to talk about indirection in the physics of software.

  • In general, everything that is not strictly necessary to calculate the Function, but has been added because of Form, or because of the programmer's inability to streamline the code to the mere Function, is a source of friction and will waste run-time energy.

Defining friction
At this stage in my understanding of the physics of software, it's still hard to come up with numbers, coefficients, sometimes even formulas. Actually, I'm usually happy when I get some concept right. Still, let's look at a simplified formula for the energy wasted through friction (in the real world):

Normal Force * Coefficient of Friction * Distance.

That would hold pretty well in the software world as well, both at the qualitative (easier) and probably quantitative (not there yet) level. At the qualitative level, it tells us what we can control and perhaps leverage. I'll explore this in the next paragraph. At the quantitative level, it could help to evaluate low-level choices. First, however, we have to define Normal Force, Coefficient of Friction, and Distance.

I've defined distance in the run-time world in Chapter 9. Unfortunately, it's an ordinal scale, so we can't do math with distance. This sort of rules out any chance to have a quantitative definition of friction, but we can also look at it from the other side: a better understanding of friction energy (like: wasted cycles) could shed light on the right measurement scale for distance!

Assuming a flat world (I have no reason to think otherwise) the Normal Force is just weight. Weight could be easily defined as the number of bytes involved. For instance, the cost of a copy is linear with the number of bytes you copy.

The coefficient of friction is a dimensionless parameter. Interestingly, if we decide to measure energy in cycles (which makes some sense, although we usually think of cycles as time, not energy) that would imply that unit of measurement for Distance is cycles/byte. I'll have to think more about this.

Although the coefficient of friction, in the real world, cannot be predicted but only measured, we have some intuitive grasp of it being related to the materials. As the aforementioned wikipedia page explains, it's a relatively complex "system property", depending on many factors. The same applies in the software world. The cost to move a bunch of bytes from one position to another dependes on a bunch of factors. If we want to raise the abstraction level and think in terms of objects, and not bytes, things become more complex. The exact copy semantics (reference, shallow, deep) kicks in. That's fine: a software material with shallow copy semantics would have a different coefficient of friction than one with reference copy semantics.

Overall, I think we have little control over the coefficient of friction (I might be wrong), so for any practical purpose, distance and weight are the most interesting parameters.

Is it useful, anyway?
A good theory, and a good concept, must have a good explanatory power, that is, we should be able to use them to explain known phenomena, explain why something works, rationalize widespread practice or beliefs, etc.

As I've already discussed, the evolution of programming languages can be largely seen as an attempt to balance the world of artifact / form with the run-time / function world. In this sense, we can look for instance at the perfect forwarding problem, solved by right value references in the next C++ standard, as a further attempt to remove some energy waste, by avoiding unnecessary copy of data. C++ provides many ways to control friction energy, mostly in the area of generic programming and also template metaprogramming. The Curiously Recurring Template Pattern, for instance, provides a form of static polymorphism exactly to avoid some friction due to unnecessary indirection (virtual dispatch).

More generally, the simple equation for energy waste provides a clue on what we can actually control: weight, distance, coefficient of friction. This is it. As we shape software, this is what we can actually change if we want to reduce friction energy.

Consider HTTP compression: distance couldn't be changed, so we had to change weight.

Also, understanding the difference between static and kinetic friction explains a lot of existing practices. Think of the Nagle's algorithm. It works by increasing static friction (therefore latency) in exchange for lower kinetic friction (therefore throughput). Once you get your concepts right, so many things unfold so easily :-).

Finally, the analogy holds to the extremes: just like excessive friction in mechanical systems can lead to jam, excessive friction due to paging can jam a software system. This is commonly known as Trashing.

I think a caveat is in order: friction in the physical world is not necessarily evil. Wasn't it for friction, we couldn't even walk. Mechanical devices have to deal with friction all the time, but they also exploit friction all the time. It's harder to exploit friction in software (although the Nagle's algorithm does). Most often, we must see friction as a trade/off with other properties, mostly in the artifact side. Still, an understanding of the different types of friction, and of the constituents of friction energy, can help evaluate alternatives and even generate new, better ideas in a more systematic and (dare I say it :-) scientific way.

A different angle
I choose friction as a physical analogy because it's a simple, familiar concept. Intuition and everyday experience can easily compensate any lack of engineering knowledge. Still, I've been tempted to use different analogies, like hydraulic or electrical analogies. Indeed, there are several analogies between electrical, mechanical, hydraulic and even acoustic and optical systems (see here for a start), so it's always possible to choose a different reference system.

Anyway, my alternative would have been to model everything after resistance and current. Current would be the equivalent of throughput, or "performance", and resistance would cause thermal dissipation. In the end, I didn't go this way for a number of reasons; for instance, one-shot stuff like JIT would require something like a thermistor (think of a PTC in CRT degaussing), but I would lose a few readers that way :-).

Still, if you followed so far, there is an interesting result I'd like to share. Consider a trivial circuit where we apply 1V to a 1 ohm resistor, resulting in 1A current. Now, I'll replace the resistor with a series of 2, with resistance (1-P) and P ohms. Nothing changes, same current. Resistors represent processes.

Now say that we have this concept of parallel execution, so the process carried out by P can be parallelized. By way of the analogy, to increase throughput (current) I can simply add up to N resistors in parallel. Now the circulating current is obviously 1 / (1-P + P/N) A. Guess what, I just rediscovered Amdahl's Law using Ohm's Law. That's cute :-).

Ok guys, next time I'll have a much shorter post on the artifact-side notion of friction. If we survive that, we'll be ready for tangling.