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The first implementation runs on a shared memory computer, the second one on a distributed memory computer, and the last one is a hybrid implementation executed on a distributed system in which each node is a shared memory multiprocessor.
The implementation runs on Apache Spark.
Proposed implementation runs on cost-efficient configurations of networks on workstations in LAN and Internet environment.
The implementation runs on a 802.11a network as a user space daemon, that is, it sends and receives raw 802.11 frames.
Our current implementation runs on a single processor and it would need days or months to run a larger set of proteins on a larger set of genomes.
How fast is Algorithm 2? Figures 7 and 8 show that the Perl implementation runs on an old desktop PC at the speed of roughly 1000 basepairs per second.
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Then, the decoding speed of the algorithm is compared between the parallel implementation running on GPU and the traditional one running on CPU.
The blue dash line represents the serial implementation running on CPU, and the red solid line is our proposed one running in parallel on GPU.
The performance is compared to a CPU implementation running on Intel Xeon E5-2603 at 1.8 GHz.
For each task there can be up to three different implementations: Hardware implementations (HW) placed in the FPGA, Software implementations running on the master processor (MS), and another Software implementation running on the slave processor (SL).
Regardless of the test cases, the GPU implementations offer consistently faster computations than the CPU implementation running on up to 16 cores.
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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