Application task and data placement in embedded many-core NUMA architectures
Proceedings of the 10th Workshop on Optimizations for DSP and Embedded Systems
Proceedings of the 2013 International Conference on Compilers, Architectures and Synthesis for Embedded Systems
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Reducing the effects of off-chip memory access latency is a key factor in exploiting efficiently embedded multicore platforms. We consider architectures that admit a multi-core computation fabric, having its own fast and small memory to which the data blocks to be processed are fetched from external memory using a DMA (direct memory access) engine, employing a double- or multiple-buffering scheme to avoid processor idling. In this paper we focus on application programs that process two dimensional data arrays and we determine automatically the size and shape of the portions of the data array which are subject to a single DMA call, based on hardware and applications parameters. When the computation on different array elements are completely independent, the asymmetry of memory structure leads always to prefer one-dimensional horizontal pieces of memory, while when the computation of a data element shares some data with its neighbors, there is a pressure for more "square" shapes to reduce the amount of redundant data transfers. We provide an analytic model for this optimization problem and validate our results by running a mean filter application on the CELL simulator.