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Parallelization Patterns

There are many ways that work can be divided among multiple threads. The objective of this section is to provide an overview of the most common approaches and to indicate when these might be appropriate.

Broadly speaking, there are two categories of parallelization, often referred to as data parallel and task parallel.

A data parallel application has multiple threads performing the same operation on separate items of data. For example, multiple threads could each take a chunk of iterations from a single loop and perform those iterations on different elements in a single array. All the threads would perform the same task but to different array indexes.

A task parallel application would have separate threads performing different operations on different items of data. For example, an animated film could be produced having one process render each frame and then a separate process take each rendered frame and incorporate it into a compressed version of the entire film.

Data Parallelism Using SIMD Instructions

Although this book discusses data parallelism in the context of multiple threads cooperating on processing the same item of data, the concept also extends into instruction sets. There are instructions, called single instruction multiple data (SIMD) instructions, that load a vector of data and perform an operation on all the items in the vector. Most processors have these instructions: the SSE instruction set extensions for x86 processors, the VIS instructions for SPARC processors, and the AltiVec instructions on Power/PowerPC processors.

The loop shown in Listing 3.1 is ideal for conversion into SIMD instructions.

Listing 3.1. Loop Adding Two Vectors

void vadd(double * restrict a, double * restrict b , int count)
 for (int i=0; i < count; i++)
   a[i] += b[i];

Compiling this on an x86 box without enabling SIMD instructions generates the assembly language loop shown in Listing 3.2.

Listing 3.2. Assembly Language Code to Add Two Vectors Using x87 Instructions

    fldl   (%edx)     // Load the value of a[i]
    faddl  (%ecx)     // Add the value of b[i]
    fstpl  (%edx)     // Store the result back to a[i]
    addl   8,%edx     // Increment the pointer to a
    addl   8,%ecx     // Increment the pointer to b
    addl   1,%esi     // Increment the loop counter
    cmp    %eax,%esi  // Test for the end of the loop
    jle    loop       // Branch back to start of loop if not complete

Compiling with SIMD instructions produces code similar to that shown in Listing 3.3.

Listing 3.3. Assembly Language Code to Add Two Vectors Using SSE Instructions

    movupd (%edx),%xmm0 // Load a[i] and a[i+1] into vector register
    movupd ($ecx),%xmm1 // Load b[i] and b[i+1] into vector register
    addpd  %xmm1,%xmm0  // Add vector registers
    movpd  %xmm0,(%edx) // Store a[i] and a[i+1] back to memory
    addl   16,%edx      // Increment pointer to a
    addl   16,%ecx      // Increment pointer to b
    addl   2,%esi       // Increment loop counter
    cmp    %eax,%esi    // Test for the end of the loop
    jle    loop         // Branch back to start of loop if not complete

Since two double-precision values are computed at the same time, the trip count around the loop is halved, so the number of instructions is halved. The move to SIMD instructions also enables the compiler to avoid the inefficiencies of the stack-based x87 floating-point architecture.

SIMD and parallelization are very complementary technologies. SIMD is often useful in situations where loops perform operations over vectors of data. These same loops could also be parallelized. Simultaneously using both approaches enables a multicore chip to achieve high throughput. However, SIMD instructions have an additional advantage in that they can also be useful in situations where the amount of work is too small to be effectively parallelized.

Parallelization Using Processes or Threads

The rest of the discussion of parallelization strategies in this chapter will use the word tasks to describe the work being performed and the word thread to describe the instruction stream performing that work. The use of the word thread is purely a convenience. These strategies are applicable to a multithreaded application where there would be a single application with multiple cooperating threads and to a multiprocess application where there would be an application made up of multiple independent processes (with some of the processes potentially having multiple threads).

The trade-offs between the two approaches are discussed in Chapter 1, “Hardware, Processes, and Threads.” Similarly, these patterns do not need to be restricted to a single system. They are just as applicable to situations where the work is spread over multiple systems.

Multiple Independent Tasks

As discussed earlier in the chapter, the easiest way of utilizing a CMT system is to perform many independent tasks. In this case, the limit to the number of independent tasks is determined by resources that are external to those tasks. A web server might require a large memory footprint for caching recently used web pages in memory. A database server might require large amounts of disk I/O. These requirements would place load on the system and on the operating system, but there would be no synchronization constraints between the applications running on the system.

A system running multiple tasks could be represented as a single system running three independent tasks, A, B, and C, as shown in Figure 3.13.

Figure 3.13. Three independent tasks

An example of this kind of usage would be consolidation of multiple machines down to a single machine. This consolidation might just be running the web server, e-mail server, and so on, on the same machine or might involve some form of virtualization where different tasks are isolated from each other.

This approach is very common but not terribly interesting from a parallelization strategy since there is no communication between the components. Such an approach would increase the utilization of the machine and could result in space or power savings but should not be expected to lead to a performance change (except that which is attained from the intrinsic differences in system performance).

One place where this strategy is common is in cluster, grid, or cloud computing. Each individual node (that is, system) in the cloud might be running a different task, and the tasks are independent. If a task fails (or a node fails while completing a task), the task can be retried on a different node. The performance of the cloud is the aggregate throughput of all the nodes.

What is interesting about this strategy is that because the tasks are independent, performance (measured as throughput) should increase nearly linearly with the number of available threads.

Multiple Loosely Coupled Tasks

A slight variation on the theme of multiple independent tasks would be where the tasks are different, but they work together to form a single application. Some applications do need to have multiple independent tasks running simultaneously, with each task generally independent and often different from the other running tasks. However, the reason this is an application rather than just a collection of tasks is that there is some element of communication within the system. The communication might be from the tasks to a central task controller, or the tasks might report some status back to a status monitor.

In this instance, the tasks themselves are largely independent. They may occasionally communicate, but that communication is likely to be asynchronous or perhaps limited to exceptional situations.

Figure 3.14 shows a single system running three tasks. Task A is a control or supervisor, and tasks B and C are reporting status to task A.

Figure 3.14. Loosely coupled tasks

The performance of the application depends on the activity of these individual tasks. If the CPU-consuming part of the “application” has been split off into a separate task, then the rest of the components may become more responsive. For an example of this improved responsiveness, assume that a single-threaded application is responsible for receiving and forwarding packets across the network and for maintaining a log of packet activity on disk. This could be split into two loosely coupled tasks—one receives and forwards the packets while the other is responsible for maintaining the log. With the original code, there might be a delay in processing an incoming packet if the application is busy writing status to the log. If the application is split into separate tasks, the packet can be received and forwarded immediately, and the log writer will record this event at a convenient point in the future.

The performance gain arises in this case because we have shared the work between two threads. The packet-forwarding task only has to process packets and does not get delayed by disk activity. The disk-writing task does not get stalled reading or writing packets. If we assume that it takes 1ms to read and forward the packet and another 1ms to write status to disk, then with the original code, we can process a new packet every 2ms (this represents a rate of 5,000 packets per second). Figure 3.15 shows this situation.

Figure 3.15. Single thread performing packet forwarding and log writing

If we split these into separate tasks, then we can handle a packet every 1ms, so throughput will have doubled. It will also improve the responsiveness because we will handle each packet within 1ms of arrival, rather than within 2ms. However, it still takes 2ms for the handling of each packet to complete, so the throughput of the system has doubled, but the response time has remained the same. Figure 3.16 shows this situation.

Figure 3.16. Using two threads to perform packet forwarding and log writing

Multiple Copies of the Same Task

An easy way to complete more work is to employ multiple copies of the same task. Each individual task will take the same time to complete, but because multiple tasks are completed in parallel, the throughput of the system will increase.

This is a very common strategy. For example, one system might be running multiple copies of a rendering application in order to render multiple animations. Each application is independent and requires no synchronization with any other.

Figure 3.17 shows this situation, with a single system running three copies of task A.

Figure 3.17. Multiple copies of a single task

Once again, the performance of the system is an increase in throughput, not an improvement in the rate at which work is completed.

Single Task Split Over Multiple Threads

Splitting a single task over multiple threads is often what people think of as parallelization. The typical scenario is distributing a loop’s iterations among multiple threads so that each thread gets to compute a discrete range of the iterations.

This scenario is represented in Figure 3.18 as a system running three threads and each of the threads handling a separate chunk of the work.

Figure 3.18. Multiple threads working on a single task

In this instance, a single unit of work is being divided between the threads, so the time taken for the unit of work to complete should diminish in proportion to the number of threads working on it. This is a reduction in completion time and would also represent an increase in throughput. In contrast, the previous examples in this section have represented increases in the amount of work completed (the throughput), but not a reduction in the completion time for each unit of work.

This pattern can also be considered a fork-join pattern, where the fork is the division of work between the threads, and the join is the point at which all the threads synchronize, having completed their individual assignments.

Another variation on this theme is the divide-and-conquer approach where a problem is recursively divided as it is divided among multiple threads.

Using a Pipeline of Tasks to Work on a Single Item

A pipeline of tasks is perhaps a less obvious strategy for parallelization. Here, a single unit of work is split into multiple stages and is passed from one stage to the next rather like an assembly line.

Figure 3.19 represents this situation. A system has three separate threads; when a unit of work comes in, the first thread completes task A and passes the work on to task B, which is performed by the second thread. The work is completed by the third thread performing task C. As each thread completes its task, it is ready to accept new work.

Figure 3.19. Pipeline of tasks

There are various motivations for using a pipeline approach. A pipeline has some amount of flexibility, in that the flow of work can be dynamically changed at runtime. It also has some implicit scalability because an implementation could use multiple copies of a particular time-consuming stage in the pipeline (combining the pipeline pattern with the multiple copies of a single task pattern), although the basic pipeline model would have a single copy of each stage.

This pattern is most critical in situations where it represents the most effective way the problem can be scaled to multiple threads. Consider a situation where packets come in for processing, are processed, and then are retransmitted. A single thread can cope only with a certain limit of packets per second. More threads are needed in order to improve performance. One way of doing this would be to increase the number of threads doing the receiving, processing, and forwarding. However, that might introduce additional complexity in keeping the packets in the same order and synchronizing the multiple processing threads.

In this situation, a pipeline looks attractive because each stage can be working on a separate packet, which means that the performance gain is proportional to the number of active threads. The way to view this is to assume that the original processing of a packet took three seconds. So, every three seconds a new packet could be dealt with. When the processing is split into three equal pipeline stages, each stage will take a second. More specifically, task A will take one second before it passes the packet of work on to task B, and this will leave the first thread able to take on a new packet of work. So, every second there will be a packet starting processing. A three-stage pipeline has improved performance by a factor of three. The issues of ordering and synchronization can be dealt with by placing the items in a queue between the stages so that order is maintained.

Notice that the pipeline does not reduce the time taken to process each unit of work. In fact, the queuing steps may slightly increase it. So, once again, it is a throughput improvement rather than a reduction in unit processing time.

One disadvantage to pipelines is that the rate that new work can go through the pipeline is limited by the time that it takes for the work of the slowest stage in the pipeline to complete. As an example, consider the case where task B takes two seconds. The second thread can accept work only every other second, so regardless of how much faster tasks A and C are to complete, task B limits the throughput of the pipeline to one task every two seconds. Of course, it might be possible to rectify this bottleneck by having two threads performing task B. Here the combination would complete one task every second, which would match the throughput of tasks A and C. It is also worth considering that the best throughput occurs when all the stages in the pipeline take the same amount of time. Otherwise, some stages will be idle waiting for more work.

Division of Work into a Client and a Server

With a client-server configuration, one thread (the client) communicates requests to another thread (the server), and the other thread responds. The split into client and server might provide a performance improvement, because while the server is performing some calculation, the client can be responding to the user; the client might be the visible UI to the application, and the server might be the compute engine that is performing the task in the background. There are plenty of examples of this approach, such as having one thread to manage the redraw of the screen while other threads handle the activities of the application. Another example is when the client is a thread running on one system while the server is a thread running on a remote system; web browsers and web servers are obvious, everyday examples.

A big advantage of this approach is the sharing of resources between multiple clients. For example, a machine might have a single Ethernet port but have multiple applications that need to communicate through that port. The client threads would send requests to a server thread. The server thread would have exclusive access to the Ethernet device and would be responsible for sending out the packets from the clients and directing incoming packets to the appropriate client in an orderly fashion.

This client-server relationship can be represented as multiple clients: A, communicating with a server, B, as shown in Figure 3.20. Server B might also control access to a set of resources, which are not explicitly included in the diagram.

Figure 3.20. Client-server division of work

Implicit in the client-server pattern is the notion that there will be multiple clients seeking the attention of a single server. The single server could, of course, be implemented using multiple threads.

The client-server pattern does not improve responsiveness but represents a way of sharing the work between multiple threads, especially where the server thread actually does some work. Alternatively, it represents a way of sharing a common resource between multiple clients (in which case any gains in throughput are a fortunate by-product rather than a design goal).

Splitting Responsibility into a Producer and a Consumer

A producer-consumer model is similar to both the pipeline model and the client-server. Here, the producer is generating units of work, and the consumer is taking those units of work and performing some kind of process on them.

For example, the movie-rendering problem described earlier might have a set of producers generating rendered frames of a movie. The consumer might be the task that has the work of ordering these frames correctly and then saving them to disk.

This can be represented as multiple copies of task A sending results to a single copy of task B, as shown in Figure 3.21. Alternatively, there could be multiple producers and a single consumer or multiple producers and consumers.

Figure 3.21. Producer-consumer division of work

Again, this approach does not necessarily reduce the latency of the tasks but provides an improvement in throughput by allowing multiple tasks to progress simultaneously. In common with the client-server task, it may also provide a way of reducing the complexity of combining the output from multiple producers of data.

Combining Parallelization Strategies

In many situations, a single parallelization strategy might be all that is required to produce a parallel solution for a problem. However, in other situations, there is no single strategy sufficient to solve the problem effectively, and it is necessary to select a combination of approaches.

The pipeline strategy represents a good starting point for a combination of approaches. The various stages in the pipeline can be further parallelized. For example, one stage might use multiple threads to perform a calculation on one item of data. A different stage might have multiple threads working on separate items of data.

When mapping a process to an implementation, it is important to consider all the ways that it is possible to exploit parallelism and to avoid limiting yourself to the first approach that comes to mind. Consider a situation where a task takes 100 seconds to complete. Suppose that it’s possible to take 80 of those seconds and use four threads to complete the work. Now the runtime for the task is 20 serial seconds, plus 20 seconds when four threads are active, for a total of 40 seconds. Suppose that it is possible to use a different strategy to spread the serial 20 seconds over two threads, leading to a performance gain of 10 seconds, so the total runtime is now 30 seconds: 10 seconds with two threads and 20 seconds with four threads. The first parallelization made the application two and a half times faster. The second parallelization made it 1.3x faster, which is not nearly as great but is still a significant gain. However, if the second optimization had been the only one performed, it would have resulted in only a 1.1x performance gain, not nearly as dramatic a pay-off as the 1.3x gain that it obtained when other parts of the code had already been made parallel.

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