An Alternate Approach: Parallel Error Diffusion
To transform the conventional error diffusion algorithm into an approach that is more conducive to a parallel solution, consider the different decomposition that were covered previously in this chapter. Which would be appropriate in this case? As a hint, consider Figure 3, which revisits the error distribution in Figure 2, from a slightly different perspective.
Given that a pixel may not be processed until its spatial predecessors have been processed, the problem appears to lend itself to an approach where we have a producer — or in this case, multiple producers — producing data (error values) which a consumer (the current pixel) will use to compute the proper output pixel. The flow of error data to the current pixel is critical. Therefore, the problem seems to break down into a data-flow decomposition.
Now that we identified the approach, the next step is to determine the best pattern that can be applied to this particular problem. Each independent thread of execution should process an equal amount of work (load balancing). How should the work be partitioned? One way, based on the algorithm presented in the previous section, would be to have a thread that processed the even pixels in a given row, and another thread that processed the odd pixels in the same row. This approach is ineffective however; each thread will be blocked waiting for the other to complete, and the performance could be worse than in the sequential case.
To effectively subdivide the work among threads, we need a way to reduce (or ideally eliminate) the dependency between pixels. Figure 3 illustrates an important point that's not obvious in Figure 2 — that in order for a pixel to be able to be processed, it must have three error values (labeled eA, eB, and eC in Figure 2) from the previous row, and one error value from the pixel immediately to the left on the current row. Thus, once these pixels are processed, the current pixel may complete its processing. This ordering suggests an implementation where each thread processes a row of data. Once a row has completed processing of the first few pixels, the thread responsible for the next row may begin its processing. Figure 4 shows this sequence.
Notice that a small latency occurs at the start of each row. This latency is due to the fact that the previous row's error data must be calculated before the current row can be processed. These types of latency are generally unavoidable in producer-consumer implementations; however, you can minimize the impact of the latency as illustrated here. The trick is to derive the proper workload partitioning so that each thread of execution works as efficiently as possible. In this case, you incur a two-pixel latency before processing of the next thread can begin. An 8.5x11-inch page, assuming 1,200 dots per inch (dpi), would have 10,200 pixels per row. The two-pixel latency is insignificant here.
The sequence in Figure 4 illustrates the data flow common to the wavefront pattern.