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We decomposed the CPU stall cycles into three components: latency due to row access, latency due to the pipeline of memory transactions, and burst transfer time.
The profiler shows that this approach leads to a reduction in both memory transactions and arithmetic operations giving significant runtime gains.
The very constrained memory space, the lack of interruptions queue, the absence of ordering for memory transactions and the lack of software components made the Parallella a fascinating device to experiment with, and from which we gained a better understanding of what issues one could have to tackle to trace a heterogeneous embedded system properly.
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We simulated various values for data bus width, page size, and row-access time of eDRAM, pipeline delay of a memory transaction, and data cache line size.
In this way, multiple processing units connected through the AAPs can make memory transactions at their slower frequencies and the memory access scheduler can serve all these transactions at the same time by taking full advantage of the memory bandwidth.
Such a strategy makes use of grid regularity of FGFEA to reduce drastically both the memory required by the implementation and the memory transactions to perform the operations with the common elemental stiffness matrix.
The following stack changes fit into the enforced stack window and no memory transactions are necessary.
Another advantage for processing data directly on the GPU is that the reconstructed OCT images can be rendered and displayed without any additional memory transactions.
In the typical use case interest focuses around a limited number of values, these are primarily performance factors (cpu, memory, transactions, requests etc), application behaviours (error rates, logs, status codes etc) and user behaviour (click through rates, time per page etc).
Due to the regular shape of the numerical stencil induced by the hexahedral regime, and since we use matrix-free formulations of all multigrid steps, computations and data layout can be restructured to avoid execution divergence of parallel running threads and to enable coalescing of memory accesses into single memory transactions.
Table 1 Methods for decomposition of the convolution problem and their requirements Method Number of operations Number of memory transactions DIF c(M f + M g ) log(M f + M g ) + (M f + M g ) 3(M f + M g ) DIT c(M f + M g ) log(M f + M g ) + 2(M f + M g ) ( 2 + P ) ( M f + M g ) Tilling c ( M f + P M g ) log ( M f P + M g ) + ( M f + 2 P M g ) 2 M f + P + 1 M g.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com