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In order to further enhance the performance, the code parameters were fine-tuned for different source correlation strengths.
In order to obtain a high performance, the code must be optimized for the GPU architecture, i.e., an implementation that exploits the memory hierarchy of the CUDA programming model.
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In order to achieve such high performance, the codes need to be designed such that iterative decoding works well.
Aspect weavers' performance, as well as the performance of the code that they produce, has been a subject of analysis.
In order to systematically analyze the performance of the code assignment strategy, we implement a simulator to analyze the code selection behavior of each strategy, and the performance of the code blocking can be anticipated based on the analysis.
The performance of the code was satisfactory in all cases.
Other numerical experiments to show the performance of the code are also described.
Figure 7 compares the FER performance of the code under both random and optimized interleaving.
Finally, we discuss the performance of the code on a parallel supercomputer with distributed memory architecture.
Optimization techniques and their effects on the performance of the code are described.
The performance of the code has been compared in terms of CPU, accuracy, speed etc.
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CEO of Professional Science Editing for Scientists @ prosciediting.com