The MPI is a flexible framework that supports a wide scalability and various types of parallel computing, e.g., single processor with multiple cores, multiple-processor clusters and hybrid models (Gropp et al. 1999).
Better computational efficiency was clearly achieved by simultaneously exploiting the multiple processor cores, the SIMD capability, and pipelining available in modern CPU processors.
With multiple processors it is relatively easy to process multiple framesets simultaneous, and some aspects of the reconstruction are implemented to take advantage of multiple processor environments and graphics card processing.
Multiple processors and cores as available from a compute cluster are employed to perform the genome searching, reducing the real time taken to test the probe designs in silico.
Those sub problems can be handled in parallel by different workers; for example, threads in a processor core, cores in a multi-core processor, multiple processors in a machine, or many machines in a cluster.
However, as reported by the authors, the execution presented in the article was performed in desktop machines with multiple processors instead of using HPC environments such as grids or clusters.
RAMP: Research Accelerator for Multiple Processors.
Computers with multiple processors inside are also distributed systems.
This takes multiple processors running complex algorithms simultaneously.
Our algorithm performs better than the standard scheme, especially at low temperatures and with multiple processors.
To increase performance today, we must program multiple processors on multicore chips and exploit parallelism.
