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I. Pipeline implementation: The pipeline is implemented with combination of shell, perl, python, and R scripts in a Linux environment.
The keystream pipeline implementation operated at frequency of 50 MHz and occupied 6864 slices.
The proposed pipeline implementation preserves convergence by simulating a pure Gauss Seidel style row-wise iteration.
In the experiments, we compare PARE against the original transaction pipeline implementation and a well-adopted popular CC mechanism MVCC.
The pipeline implementation was for instance facilitated using the PyWavelets [31] and the more general NumPy libraries.
We describe a scalable heterogeneous computing pipeline implementation, exploiting both the high computing density and FLOP-per-Watt ratio of modern GPUs.
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In both FPGAs, it can be seen that the throughput is almost similar for 2-stages and 3-stages pipeline implementations.
Ideally, researchers and clinicians should have little to no uncertainty about the correct pipeline parameterizations for each sequencing experiment, and hence little variability with respect to their pipeline implementations; however this is rarely, if ever, the case.
This corresponds to 3.5× higher throughput with 2.1× more slice compared to non-pipeline implementation.
Compared to a non-pipeline implementation, 2-stages pipeline utilizes roughly 22% more ALUT, with 59% higher throughput.
The maximum throughput using Virtex-5 FPGA is 1654 Mpixels/s with total slice of 3, 419 for 4-stages pipeline, which corresponds to 2.07× more slice and 2.16× higher throughput compared to non-pipeline implementation.
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CEO of Professional Science Editing for Scientists @ prosciediting.com