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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.
Our implementation running on an SGI Indigo workstation renders a 256^3 voxel medical data set in one second.
The automatically generated code running on PtidyOS is compared with a manual C implementation running on bare silicon.
The performance is compared to a CPU implementation running on Intel Xeon E5-2603 at 1.8 GHz.
The HW/SW Co-Design achieves a speedup of approximately 9× compared to an original HOG implementation running on the ARM processor.
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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.
Proposed implementation runs on cost-efficient configurations of networks on workstations in LAN and Internet environment.
The implementation runs on Apache Spark.
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.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com