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Chapter 3. Identifying Opportunities for... > Using Parallelism to Improve the Per...

Using Parallelism to Improve the Performance of a Single Task

Virtualization provides one way of utilizing a multicore or multiprocessor system by extracting parallelism at the highest level: running multiple tasks or applications simultaneously. For a user, a compelling feature of virtualization is that utilizing this level of parallelism becomes largely an administrative task.

But the deeper question for software developers is how multiple cores can be employed to improve the throughput or computational speed of a single application. The next section discusses a more tightly integrated parallelism for enabling such performance gains.

One Approach to Visualizing Parallel Applications

One way to visualize parallelization conceptually is to imagine that there are two of you; each thinks the same thoughts and behaves in the same way. Potentially, you could achieve twice as much as one of you currently does, but there are definitely some issues that the two of you will have to face.

You might imagine that your double could go out to work while you stay at home and read books. In this situation, you are implicitly controlling your double: You tell them what to do.

However, if you’re both identical, then your double would also prefer to stay home and read while you go out to work. So, perhaps you would have to devise a way to determine which of you goes to work today—maybe splitting the work so that one would go one week, and the other the next week.

Of course, there would also be problems on the weekend, when you both would want to read the same newspaper at the same time. So, perhaps you would need two copies of the paper or work out some way of sharing it so only one of you had the paper at a time.

On the other hand, there would be plenty of benefits. You could be painting one wall, while your double is painting another. One of you could mow the lawn while the other washes the dishes. You could even work together cooking the dinner; one of you could be chopping vegetables while the other is frying them.

Although the idea of this kind of double person is fanciful, these examples represent very real issues that arise when writing parallel applications. As a thought experiment, imagining two people collaborating on a particular task should help you identify ways to divide the task and should also indicate some of the issues that result.

The rest of the chapter will explore some of these opportunities and issues in more detail. However, it will help in visualizing the later parts of the chapter if you can take some of these more “human” examples and draw the parallels to the computational problems.

Parallelism provides an opportunity to get more work done. This work might be independent tasks, such as mowing the lawn and washing the dishes. These could correspond to different processes or perhaps even different users utilizing the same system. Painting the walls of a house requires a little more communication—you might need to identify which wall to paint next—but generally the two tasks can proceed independently. However, when it comes to cooking a meal, the tasks are much more tightly coupled. The order in which the vegetables are chopped should correspond to the order in which they are needed. You might even need messages like “Stop what you’re doing and get me more olive oil, now!” Preparing a meal requires a high amount of communication between the two workers.

The more communication is required, the more likely it is that the effect of the two workers will not be a doubling of performance. An example of communication might be to indicate which order the vegetables should be prepared in. Inefficiencies might arise when the person cooking is waiting for the other person to complete chopping the next needed vegetable.

The issue of accessing resources, for example, both wanting to read the same newspaper, is another important concern. It can sometimes be avoided by duplicating resources—both of you having your own copies—but sometimes if there is only a single resource, we will need to establish a way to share that resource.

In the next section, we will explore this thought experiment further and observe how the algorithm we use to solve a problem determines how efficiently the problem can be solved.

How Parallelism Can Change the Choice of Algorithms

Algorithms have characteristics that make them more or less appropriate for a multithreaded implementation. For example, suppose you have a deck of playing cards that are in a random order but you would like to sort them in order. One way to do this would be to hold the unsorted cards in one hand and place each card into its appropriate place in the other hand. There are N cards, and a binary search is needed to locate each card into its proper place. So, going back to the earlier discussion on algorithmic complexity, this is an O(n*log(n)) algorithm.

However, suppose you have someone to help, and you each decide to sort half the pack. If you did that, you would end up with two piles of sorted cards, which you would then have to combine. To combine them, you could each start with a pile of cards, and then whoever had the next card could place it onto the single sorted stack. The complexity of the sort part of this algorithm would be O(n*log(n)) (for a value of n that was half the original), and the combination would be O(n). So although we have increased the number of “threads,” we do not guarantee a doubling of performance.

An alternative way of doing this would be to take advantage of the fact that playing cards have an existing and easily discernible order. If instead of sorting the cards, you just place them at the correct place on a grid. The grid could have the “value” of the card as the x-axis and the “suit” of the card as the y-axis. This would be an O(n) operation since the time it takes to place a single card does not depend on the number of cards that are present in the deck. This method is likely to be slightly slower than keeping the cards in your hands because you will have to physically reach to place the cards into the appropriate places in the grid. However, if you have the benefit of another person helping, then the deck can again be split into two, and each person would have to sort only half the cards. Assuming you don’t obstruct each other, you should be able to attain a near doubling of performance. So, comparing the two algorithms, using the grid method might be slower for a single person but would scale better with multiple people.

The point here is to demonstrate that the best algorithm for a single thread may not necessarily correspond to the best parallel algorithm. Further, the best parallel algorithm may be slower in the serial case than the best serial algorithm.

Proving the complexity of a parallel algorithm is hard in the general case and is typically handled using approximations. The most common approximation to parallel performance is Amdahl’s law.

Amdahl’s Law

Amdahl’s law is the simplest form of a scaling law. The underlying assumption is that the performance of the parallel code scales with the number of threads. This is unrealistic, as we will discuss later, but does provide a basic starting point. If we assume that S represents the time spent in serial code that cannot be parallelized and P represents the time spent in code that can be parallelized, then the runtime of the serial application is as follows:

Runtime = S + P

The runtime of a parallel version of the application that used N processors would take the following:

It is probably easiest to see the scaling diagrammatically. In Figure 3.7, we represent the runtime of the serial portion of the code and the portion of the code that can be made to run in parallel as rectangles.

Figure 3.7. Single-threaded runtime


If we use two threads for the parallel portion of the code, then the runtime of that part of the code will halve, and Figure 3.8 represents the resulting processor activity.

Figure 3.8. Runtime with two threads


If we were to use four threads to run this code, then the resulting processor activity would resemble Figure 3.9.

Figure 3.9. Runtime with four threads


There are a couple of things that follow from Amdahl’s law. As the processor count increases, performance becomes dominated by the serial portion of the application. In the limit, the program can run no faster than the duration of the serial part, S. Another observation is that there are diminishing returns as the number of threads increases: At some point adding more threads does not make a discernible difference to the total runtime.

These two observations are probably best illustrated using the chart in Figure 3.10, which shows the parallel speedup over the serial case for applications that have various amounts of code that can be parallelized.

Figure 3.10. Scaling with diminishing parallel regions


If all the code can be made to run in parallel, the scaling is perfect; a code run with 18 threads will be 18x faster than the serial version of the code. However, it is surprising to see how fast scaling declines as the proportion of code that can be made to run in parallel drops. If 99% of the application can be converted to parallel code, the application would scale to about 15x the serial performance with 18 threads. At 95% serial, this would drop to about 10x the serial performance. If only half the application can be run in parallel, then the best that can be expected is for performance to double, and the code would pretty much attain that at a thread count of about 8.

There is another way of using Amdahl’s law, and that is to look at how many threads an application can scale to given the amount of time it spends in code that can be parallelized.

Determining the Maximum Practical Threads

If we take Amdahl’s law as a reasonable approximation to application scaling, it becomes an interesting question to ask how many threads we should expect an application to scale to.

If we have an application that spends only 10% of its time in code that can be parallelized, it is unlikely that we’ll see much noticeable gain when using eight threads over using four threads. If we assume it took 100 seconds to start with, then four threads would complete the task in 92.5 seconds, whereas eight threads would take 91.25 seconds. This is just over a second out of a total duration of a minute and a half. In case the use of seconds might be seen as a way of trivializing the difference, imagine that the original code took 100 days; then the difference is equivalent to a single day out of a total duration of three months.

There will be some applications where every last second is critical and it makes sense to use as many resources as possible to increase the performance to as high as possible. However, there are probably a large number of applications where a small gain in performance is not worth the effort.

We can analyze this issue assuming that a person has a tolerance, T, within which they cease to care about a difference in performance. For many people this is probably 10%; if the performance that they get is within 10% of the best possible, then it is acceptable. Other groups might have stronger or weaker constraints.

Returning to Amdahl’s law, recall that the runtime of an application that has a proportion P of parallelizable code and S of serial code and that is run with N threads is as follows:

The optimal runtime, when there are an infinite number of threads, is S. So, a runtime within T percent of the optimal would be as follows:

Acceptable runtime = S*(1 + T)

We can compare the acceptable runtime with the runtime with N threads:

We can then rearrange and solve for N to get the following relationship for N:

Using this equation, Figure 3.11 shows the number of threads necessary to get a runtime that is within 10% of the best possible.

Figure 3.11. Minimum number of threads required to get 90% of peak performance


Reading this chart, it is clear that an application will have only limited scalability until it spends at least half of its runtime in code that can be parallelized. For an application to scale to large numbers of cores, it requires that 80%+ of the serial runtime is spent in parallelizable code.

If Amdahl’s law were the only constraint to scaling, then it is apparent that there is little benefit to using huge thread counts on any but the most embarrassingly parallel applications. If performance is measured as throughput (or the amount of work done), it is probable that for a system capable of running many threads, those threads may be better allocated to a number of processes rather than all being utilized by a single process.

However, Amdahl’s law is a simplification of the scaling situation. The next section will discuss a more realistic model.

How Synchronization Costs Reduce Scaling

Unfortunately, there are overhead costs associated with parallelizing applications. These are associated with making the code run in parallel, with managing all the threads, and with the communication between threads. You can find a more detailed discussion in Chapter 9, “Scaling on Multicore Systems.”

In the model discussed here, as with Amdahl’s law, we will ignore any costs introduced by the implementation of parallelization in the application and focus entirely on the costs of synchronization between the multiple threads. When there are multiple threads cooperating to solve a problem, there is a communication cost between all the threads. The communication might be the command for all the threads to start, or it might represent each thread notifying the main thread that it has completed its work.

We can denote this synchronization cost as some function F(N), since it will increase as the number of threads increases. In the best case, F(N) would be a constant, indicating that the cost of synchronization does not change as the number of threads increases. In the worst case, it could be linear or even exponential with the number threads. A fair estimate for the cost might be that it is proportional to the logarithm of the number of threads (F(N)=K*ln(N)); this is relatively easy to argue for since the logarithm represents the cost of communication if those threads communicated using a balanced tree. Taking this approximation, then the cost of scaling to N threads would be as follows:

The value of K would be some constant that represents the communication latency between two threads together with the number of times a synchronization point is encountered (assuming that the number of synchronization points for a particular application and workload is a constant). K will be proportional to memory latency for those systems that communicate through memory, or perhaps cache latency if all the communicating threads share a common level of cache. Figure 3.12 shows the curves resulting from an unrealistically large value for the constant K, demonstrating that at some thread count the performance gain over the serial case will start decreasing because of the synchronization costs.

Figure 3.12. Scaling with exaggerated synchronization overheads


It is relatively straightforward to calculate the point at which this will happen:

Solving this for N indicates that the minimal value for the runtime occurs when

This tells us that the number of threads that a code can scale to is proportional to the ratio of the amount of work that can be parallelized and the cost of synchronization. So, the scaling of the application can be increased either by making more of the code run in parallel (increasing the value of P) or by reducing the synchronization costs (reducing the value of K). Alternatively, if the number of threads is held constant, then reducing the synchronization cost (making K smaller) will enable smaller sections of code to be made parallel (P can also be made smaller).

What makes this interesting is that a multicore processor will often have threads sharing data through a shared level of cache. The shared level of cache will have lower latency than if the two threads had to communicate through memory. Synchronization costs are usually proportional to the latency of the memory through which the threads communicate, so communication through a shared level of cache will result in much lower synchronization costs. This means that multicore processors have the opportunity to be used for either parallelizing regions of code where the synchronization costs were previously prohibitive or, alternatively, scaling the existing code to higher thread counts than were previously possible.

So far, this chapter has discussed the expectations that a developer should have when scaling their code to multiple threads. However, a bigger issue is how to identify work that can be completed in parallel, as well as the patterns to use to perform this work. The next section discusses common parallelization patterns and how to identify when to use them.

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