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Numerical experiments will be done in Matlab 2008a, based on Intel core i5-2400 [email protected].
The experiments of the aforementioned implementations are conducted on Intel core i5 2410M machine with 4 GB of RAM.
The proposed algorithms are implemented in MATLAB 2014a on Intel core i3 processor at a speed of 3.5 GHz on windows 7 operating systems.
This paper presents a study that shows the L2 DPL (data prefetch logic) in processors based on Intel Core microarchitecture can be a cause to this problem as well.
The hosting PC runs on Intel core i7-2600 CPU @3.4 GHz with 8 GB system memory, the operating system is Debian with Linux kernel the GPU driver version is 325.15.
Using NVIDIA TESLA K40C GPU for a case of asynchronous I/O transfer, our GPU optimization efforts using fine-grained NEDI can achieve a speedup of 99.09-fold 99.09-folddering the I/O transfer time, compared when the original single-threaded consideringwithethe -O2 compI/Ong optransferon on Intimecompared-920.
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All the experiments in this paper are based on Intel CORE-i7 (2.9 GHz) machine with 8 GB RAM.
Runs on an Intel Core 2 Duo @ 2.6GHz.
Therefore, we require 79.952 μ s to encode any image for the aforementioned resolution, which is 12 times faster than the sequential algorithm on an Intel Core 2 CPU at 1.8 GHz with 5 GBytes RAM.
On the other hand, the algorithm requires 66,240 cycles to perform the decoding process for an image resolution of 512 × 512 pixels, so 662 μ s are needed to decode any image for that resolution, being 70 times faster than the sequential decoding algorithm on an Intel Core 2 CPU at 1.8 GHz with 5 GBytes RAM. Figure 11 shows the maximum decoding frame rate achievable for the proposed architecture.
The entire simulation took ≈3 minutes to complete on an intel core i7-820m processor based laptop computer with 12 Giga bytes of RAM.
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