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In order to cope with this problem, this paper presents a high-level performance estimator based on a neural network, which easily adapts to the non-linear behaviour of the execution time in advanced architectures and presents a speed-up up to 190 times in comparison with cycle-accurate simulators, using the PowerPC 750 as target architecture.
As we have seen, the GPU 3D-DWT algorithm obtains good speed-ups, up to 16 in the GT540M platform and up to 39 in the GTX280.
In[15], authors present GPU implementations for the 2D-DWT obtaining speed-ups up to 20 when compared to the CPU sequential algorithm.
We observed speed-ups up to factor 10 on various benchmark data when using this option while the alignment quality was still reasonably high, in the sense that the average sum-of-pair score and average column score on our benchmarks deteroriated around 5%10%10% only.
Implementation of the structure factor evaluation in NVIDIA® CUDA so that the code can be run on GPUs leads to a speed up of up to two orders of magnitude.
The sparsification of Λ∗ alone is claimed to provide a speed up of up to a linear factor of the DP-folding of RNA secondary structures [ 29].
However, now the pace of the decline is speeding up up.
Compared to the commonly used van Ginneken style buffering, up to 49× speed up is obtained and often 10% buffer area is saved.
Performance test on IBM p690 architecture shows that the parallel scheme adds up 20-fold speed ups when using 36 processors.
"Now we live in such a fast-paced world, we can feel we need to speed up to keep up with everything".
The results indicate that for large problems a single GPU is up to 45 times faster than a CPU for this application, and the parallel implementation shows linear speed up on up to 4 GPUs.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com