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This parallelism between rule applications within one membrane is modeled with multithreading.
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MsrB1 structure was modeled with Modeler 8.2.
This is the same issue as with multithreading with a shared level 1 cache.
The SMP and VN modes were used to examine differences in mpAD4 scaling and performance using MPI with multithreading SMP OMP = 4) or MPI alone VN OMP = 1).
The main issue with multithreading in real-time systems arises when the execution time of one thread depends on the state of a different thread.
Even on a single, multi-core machine, Halvade attains a significant speedup compared with running the individual tools with multithreading.
To exploit parallelism in a workstation with one or more multi-core CPUs, one can either run a single instance of a tool with multithreading on all available CPU cores, or run multiple instances of that tool, each instance using only a fraction of the CPU cores.
The underlying data structure is a memory efficient de-Bruijn graph (Compeau et al., 2011), and the general parallelism model is multithreading.
Using MPI with OpenMP multithreading, mpAD4 scales with near linearity on the multiprocessor systems tested.
However, the opposite is true as library size shrinks in comparison to system size, as demonstrated in the scaling of the 9 K ZINC library where node utilization inefficiencies are apparent in both VN and SMP mode, though SMP mode is less effected (Figure 3b). Figure 3 Scaling of MPI alone versus MPI with OpenMP multithreading.
In the bootstrap analyses, multithreading with 16 CPUs were utilized to reduce the computational time.
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