Abstract
Software patterns codify the collective knowledge and experience of software experts. Over the years, the parallel programming community has accumulated a significant amount of experience in the form of software patterns. We show that parallel programming patterns can be used in medical imaging applications on multi-core platforms. We discuss two problem decomposition patterns – task and data decomposition, which can be used to expose concurrency in programming tasks; and three program structuring patterns – task parallelism, data parallelism, and pipelining, which can be exploited to structure a parallel program. We illustrate how to use those patterns to improve the startup time, runtime throughput, and algorithm reliability in medical image viewing, visualization, and analysis on multi-core platforms. Our experience suggests that the computing power provided by multi-core and many core platforms can be systematically exploited in medical imaging applications by following the best practices established by software patterns for parallel programming.
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