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Multicore processors present new software challenges that you must overcome to fully take advantage of the processing capabilities in test, control, and embedded design applications. Explore the following resources to learn how you can use graphical programming to multithread your applications, implement parallel programming strategies, and harness the power of dual-core and multicore processors. If you have written parallel programs in G and have a multicore computer, congratulations! You have been successfully developing interactive parallel programs that execute in multicore PC processors.
Figure 1. Interactive Multicore G Program
The following sections discuss some multicore programming techniques to improve the performance of G programs.
Data Parallelism
Matrix multiplication is a compute-intensive operation that can leverage data parallelism. Figure 2 shows a G program with eight sequential frames to demonstrate the performance improvement via data parallelism.
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Sequence Frame |
Operation Description |
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First Frame |
Generates two square matrices initialized with random numbers |
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Second Frame |
Records start time for single-core matrix multiply |
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Third Frame |
Performs single-core matrix multiply |
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Fourth Frame |
Records stop time of single-core matrix multiply |
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Fifth Frame |
Splits the matrix into top and bottom matrices |
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Sixth Frame |
Records start time for multicore matrix multiply |
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Seventh Frame |
Performs multicore matrix multiply |
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Eighth Frame |
Records stop time of multicore matrix multiply |
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The Create Matrix function generates a square matrix containing random numbers between 0 and 1 whose size is indicated by Size. The Create Matrix function is shown in the adjacent “Creating a Square Matrix” diagram. |
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The Split Matrix function determines the number of rows in the matrix and shifts right the resulting number of rows by one (integer divide by 2). This value is used to split the input matrix into the top half and bottom half matrices. The Split Matrix function is shown in Figure 4. |
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The rest of the calculations determine the execution time in milliseconds of the single-core and multicore matrix multiply operations and the performance improvement of using data parallelism in a multicore computer.
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This program was executed in a dual-core 1.83 GHz laptop. The results are shown in Figure 5. By leveraging data parallelism, the same operation has nearly a 2X performance improvement. You can obtain similar performance benefits with higher multicore processors. |
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Task Pipelining
A variety of applications require you to program tasks sequentially and continually iterate on these tasks. Most notable are telecommunications applications that require simultaneous transmit and receive. The following simple telecommunications example illustrates how you can pipeline these sequential tasks to leverage multicore environments.
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Consider this simple modulation-demodulation example where a noisy signal is modulated, transmitted, and demodulated. A typical diagram is shown in Figure 6. |
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Adding a shift register to the loop allows you to pipeline tasks and execute them in parallel in separate cores should they be available. Task pipelining is shown in Figure 7. |
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The program shown in Figure 8 times the sequential task and the pipelined tasks to establish its performance improvement when executed in multicore computers.
Figure 8. Task Pipelining Program Example
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Figure 9 shows the results of running the above G program in a dual-core 1.8 GHz laptop. Pipelining shows a nearly 2X performance improvement. |
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Pipelining Using Feedback Nodes
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Feedback Nodes provide a storage mechanism between loop iterations. They are programmatically identical to the Shift Registers. Feedback Nodes consist of an Initializer Terminal and the Feedback Node itself. |
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To add a Feedback Node, right-click on the G programming window and select Feedback Node from the Functions>>Programming>>Structures pop-up menu. You can change the direction of the Feedback Node by right-clicking on the node and selecting Change Direction. |
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The diagram shown in Figure 12 is programmatically identical to the diagram in Figure 7. |
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Similarly, the diagram in Figure 13 is programmatically identical to that in Figure 8.
Figure 13. Pipelining Tasks with Feedback Nodes
Related Links
Scientific Computing with Graphical System Design
Scientific Computing with NI LabVIEW
Scripting Languages and NI LabVIEW
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