Depending on the nature of the particular steps taken in the image-processing algorithm, an individual image could take from a few seconds to a minute to be processed on a typical desktop computer. Processing the few thousand images in the FlyEx database might then take from half a day to a few days, a time frame that perhaps is acceptable in the context of a research program in developmental biology.
In a pharmaceutical context, however, a typical high-throughput screening library contains one or two million compounds that are tested by an HTS robot at a rate of up to 100,000 compounds per day. A time frame of several months to process these generated images would be an unacceptable delay.
Parallel Computing Toolbox enables scientists to solve computationally intensive problems using MATLAB on multicore and multiprocessor computers, or scaled to a cluster, using Distributed Computing Server. By distributing the processing of high-throughput screening data across multiple computers, researchers can decrease analysis time by orders of magnitude.
Simple parallel-programming constructs, such as parallel for-loops, allow scientists to convert algorithms to run in parallel with minimal code changes and at a high level without programming for specific hardware and network architectures (Figure 2).
It is crucial that scientists are able to easily convert their algorithms to work in parallel while remaining at this high level, without needing to become experts in the traditionally complex techniques of programming for high-performance computing.