Scaling laws (models) #

Suppose we have a simulation that has been parallelised. What should we expect of its performance when we run it in parallel? Answering this question will allow us to determine an appropriate level of parallelism to use when running the simulation. It can also help us determine how much effort to put into parallelising a serial code, or improving an existing parallel code.

Some simple examples¹ #

The type of parallelism we will cover in this course is that where we, as programmers, are explicitly in control of what is going on. That is, we decide how to divide up work between processes, and how to schedule that work. We will generally focus on programming models that enable “tightly coupled” parallelism. That is, we have a single task that we want to split up (so that we can get the answer faster, or ask a larger question, or both).

Here’s a very simple example, adding together two vectors of length \(n\). The core loop of this operation written in C looks like

for (size_t i = 0; i < n; i++)
  a[i] = b[i] + c[i];

We can carry out this loop on up to \(n\) processors ². A more general strategy would be to chunk the loop up into \(p\) pieces (if we have \(p\) processors, and let each processor handle part of the loop

size_t start = figure_out_start(...)
size_t end = figure_out_end(...)
for (size_t i = start; i < end; i++)
  a[i] = b[i] + c[i];

So the execution time is linearly reduced with the number of processors. Suppose that each addition takes one time unit. In serial, our code takes time \(n\), and parallel execution takes time \(\frac{n}{p}\), and is a factor of \(p\) faster.

This algorithm didn’t really require any reworking to run in parallel. Let’s now look at example that does.

Consider, instead of adding two vectors pointwise, computing the dot product of two vectors

$$ s = a \cdot b =: \sum_{i=1}^n a_i b_i $$

In serial, the C code looks familiar and straightforward

s = 0;
for (size_t i = 0; i < n; i++)
  s += a[i] * b[i];

Now parallelisation does not look as straightforward, since there is a loop carried dependency. Iteration 1 requires that iteration 0 is finished, iteration 2 requires that iteration 1 is finished, and so forth.

However, if we are willing to pay some more storage, we can rework this single loop into the following.

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10

double tmp[n];
/* We can parallelise this as before */
for (size_t i = 0; i < n; i++)
  tmp[i] = a[i] * b[i];

for (size_t s = 2; s < 2*n; s *= 2)
  for (size_t i = 0; i < n - s/2; i += s)
    tmp[i] += tmp[i + s/2];

s = tmp[0]

First we compute the pointwise product, then we perform some partial sums. The inner loop on line 7 is now a loop with \(\frac{n}{s}\) iterations that can be carried out in parallel. The outer loop now has \(\log_2 n\) iterations. So if we parallelise the inner loop, we have a parallel runtime of \(\frac{n \log_2 n}{p}\). So the speedup is \(\frac{p}{\log_2 n}\).

Even for these two very simple examples we can make some observations.

1. Sometimes, we might need to rewrite an algorithm slightly to expose parallelism;
2. A parallel algorithm may not exhibit perfect speedup. That is, we may not get a performance increase of \(p\) when we employ \(p\) processors.

In these examples we note that we’ve rewritten them to expose potential parallelism, but haven’t looked yet at how to realise that parallelism. We’ll do so when we introduce programming models.

A thought experiment #

In the previous examples, we set up a problem where we had some piece of code and needed to parallelise it. We saw that even if the code is perfectly parallel, the potential speedup may be less than perfect.

The examples we showed were not whole programs, and the real world is far from perfect, so we might expect that more realistic performance models are more complicated. Before we look at two of them, let’s see if we can construct a model for ourselves.

The setting we have in mind is that we have some computation that requires \(N\) units of work, taking \(T\) seconds on a single process.

Exercise

Write down a formula (model) for the time to solution on \(P\) processes, given that a program takes \(T\) seconds on one computer.

Question

Given your model, how would you define speedup and efficiency?

What assumptions did you make in constructing your model?

Can you think of any reasons why the observed speedup in “real life” might be lower than predicted by your model?

Amdahl’s law and strong scaling #

This model considers a fixed problem size. That is, we fix the \(N\) units of work that we want to perform. It then makes an assumption about how much of that work is (potentially) parallelisable and attempts to answer the question of what speedup we might obtain if we were able to parallelise that part of the code perfectly.

Let \(F_s\) be the serial fraction and \(F_p := 1 - F_s\) the parallel fraction of a given code and suppose that a problem takes \(T_1\) seconds on a single process.

We assume that we can perfectly parallelise the parallel fraction, and so the total parallel execution time on \(P\) processors is

$$ T_P := T_1 \left(F_s + \frac{F_p}{P}\right). $$

Defining the speedup as the ratio $$ S_P := \frac{T_1}{T_P} $$

we observe that the maximum speedup we can obtain is

$$ S_\infty = \lim_{P \to \infty} \frac{T_1}{T_1\left(F_s + \frac{F_p}{P}\right)} = \frac{1}{F_s}. $$

This phenomenon, where the maximum speedup is limited by the serial fraction of the code, is known as Amdahl’s law after Gene Amdahl.

Exercise

Consider a code that has a 2 second serial setup phase, and then carries out a parallelisable computation for 1200 seconds on a single processor.

What does Amdahl’s law predict the speedup and efficiency to be when executed on

1. 100 processors;
2. 500 processors?

Exercise

Consider the implications of Amdahl’s law. Suppose we wish to maintain a fixed parallel efficiency of 80%. How does the parallel fraction of the code have to increase as a function of the number of processors?

This type of parallel scaling, where we fix the problem size and add more compute resources, is termed strong scaling. That is, fix have a fixed total work \(N\) and each process performs a decreasing fraction of the work \(\frac{N}{P}\) as we add more processes.

This is an appropriate metric to consider when what we want to do is take a fixed size problem and ask ourselves how fast we might possibly run it.

Amdahl’s law seems to paint a rather disappointing picture. If I have even 1% serial fraction in my code, the best possible speedup I can expect from applying parallelism is 100.

Fortunately, there is an implicit assumption hiding in Amdahl’s model, which is that we are only ever interested in fixed problem sizes. In typical use, this is not the case, since there is often a way we can increase the size of a problem. For example, we might add more resolution to a weather forecast so that we have more accurate predictions of where exactly in Durham it will be raining³.

This leads us to a different way of thinking about parallel scaling and efficiency.

Gustafson’s law and weak scaling #

Rather than starting with sequential code and asking how it will speedup when parallelising it, here we start with a problem that runs in parallel, again with known serial and parallel fractions. Now we ask ourselves if we had run the code in serial, how long would it have taken?

That is, we know \(T_P\), the time on \(P\) processors, and we derive the time on one processor

$$ T_1 = T_P F_s + P F_p T_P. $$

Here, we used that the parallel splits into a serial part and a parallelisable part. The serial part contributes the same to the runtime in serial or parallel, in serial the parallelisable part takes \(P\) times as long one one process as on \(P\) processes.

Again, we can write the speedup

$$ S_P = \frac{T_1}{T_P} = F_s + P F_p = P + (1 - P)F_s. $$

This approach models the setup where we have a serial fraction that is independent of the problem size, and a parallel fraction that we can replicate arbitrarily.

This type of parallel scalability is called weak scaling. In this scenario we fix the problem size per process. In this scenario, the total work \(N\) increases when we add more processes, since we require that the ratio \(\frac{N}{P}\) is constant as we change \(P\).

This metric is an appropriate one to consider when we have a fixed runtime we are targetting and are asking how large a problem we could run (by adding more compute resources).

Exercise

Produce plots of the normalised speedup as given by the two approaches for a range of serial fractions from \( F_s = 0.01 \) to \( F_s = 0.5 \).

Compare the behaviour of the speedup curve over a range of processes from 1 to 1000.

What observations can you make?

Pitfalls and shortcomings #

These two speedup models are our first encounter with performance models. They can be useful, but we should note some of their shortcomings.

First, they are very simplistic. They suppose that a (potentially complicated) program can be divided into two separate parts which may then be treated independently. In fact, it is very likely that changing a program such that it runs in parallel will effect the execution of serial parts of that same program on real hardware. This might be, for example, due to frequency scaling or cache effects.

Another issue is that these models do not consider any overhead that comes from introducing parallelism. We will see some examples of this later in the course.

Some of these problems, especially in relation to Amdahl’s law, are discussed in more detail by Michael Wolfe in an article on hpcwire.

We should also note that speedup in these models is reported relative to the serial performance. Can you think of why that might be a bad idea?

Exercise

Can you think of any ways that you could “cheat” these scaling models?

For example, how could you improve the scalability without actually improving the time to solution?

Hint: is scalable code good code? What information does a scaling plot not give you?

Summary #

We considered two different ways of measuring the speedup of a parallel program. Which one is most relevant depends on what the properties of the problem you are trying to parallelise are.