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In this arrangement, every microprocessor or CPU has equal access to the entire physical memory, and communication among them uses the shared memory model.
The Parallella's implementation uses the shared memory space to store the packets.
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While Kepler's new read-only data cache brings enough benefits for one kernel function, performance of the other kernel function is further enhanceable through using the shared memory and so-called halo threads.
Additionally, only low scalability is achieved on multi-computers and multi-processors when employing the message passing paradigm, whereas much better behaviour is observed on multi-processors using the shared memory programming model.
In Figure11, we evaluate the new algorithm for computing the wavelet transform using the shared memory.
Theoretically, the approach of using the shared memory should be faster.
Those corrections can be done by using the shared memory of the host with the function thrust::for_each.
MNS3 has the same parameters as MNS2, but MNS3 was implemented using the shared memory approach (see subsection 3.2).
Threads can communicate with the other threads in the same block by using the shared memory efficiently.
We assume robots know the graph, and can communicate with each other their current vertex positions using the shared memory.
Of course, in the context of a high-performance application where a slave should perform one task only, using the shared memory would be a better solution.
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