Optimized On-Chip-Pipelined Mergesort on the Cell/B.E.
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- Hultén R., Kessler C.W., Keller J. (2010) Optimized On-Chip-Pipelined Mergesort on the Cell/B.E.. In: D’Ambra P., Guarracino M., Talia D. (eds) Euro-Par 2010 - Parallel Processing. Euro-Par 2010. Lecture Notes in Computer Science, vol 6272. Springer, Berlin, Heidelberg
Limited bandwidth to off-chip main memory is a performance bottleneck in chip multiprocessors for streaming computations, such as Cell/B.E., and this will become even more problematic with an increasing number of cores. Especially for streaming computations where the ratio between computational work and memory transfer is low, transforming the program into more memory-efficient code is an important program optimization. In earlier work, we have proposed such a transformation technique: on-chip pipelining.
On-chip pipelining reorganizes the computation so that partial results of subtasks are forwarded immediately between the cores over the high-bandwidth internal network, in order to reduce the volume of main memory accesses, and thereby improves the throughput for memory-intensive computations. At the same time, throughput is also constrained by the limited amount of on-chip memory available for buffering forwarded data. By optimizing the mapping of tasks to cores, balancing a trade-off between load balancing, buffer memory consumption, and communication load on the on-chip bus, a larger buffer size can be applied, resulting in less DMA communication and scheduling overhead.
In this paper, we consider parallel mergesort on Cell/B.E. as a representative memory-intensive application in detail, and focus on the global merging phase, which is dominating the overall sorting time for larger data sets. We work out the technical issues of applying the on-chip pipelining technique for the Cell processor, describe our implementation, evaluate experimentally the influence of buffer sizes and mapping optimizations, and show that optimized on-chip pipelining indeed reduces, for realistic problem sizes, merging times by up to 70% on QS20 and 143% on PS3 compared to the merge phase of CellSort, which was by now the fastest merge sort implementation on Cell.
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