No matter how you cut it, coding for multicore is really just parallel programming.Doug Eadline explains the differences between OpenMP and MPI, when it’s smart to use existing code and when it’s time to rewrite an application to scale better on multicore systems.
No matter how you cut it, coding for multicore is really just parallel programming. Once you’ve realized that, it’s time to look at the options, whether your existing codebase will scale, or if you need to rewrite your code and how.
As stated in The Multicore Programming Challenge, parallel programming can be difficult. It moves the programmer closer to the hardware and further from their application space or problem. Fortunately, people like rocket scientists have been writing parallel software for quite some time in the HPC (High Performance Computing) sector.
As any good programmer knows, an existing code base can be valuable to current programming projects. First, the possibility of re-using existing code is a major incentive. Also, learning how someone else attacked a similar problem is very valuable.
In the HPC sector, most parallel programs are written using Message Passing Interface (MPI). While MPI is normally used on large computing systems (clusters) it can be also be used on a multicore processor. The “MPI proposition” may seem counter to conventional wisdom as MPI was designed for distributed memory (i.e. each core/processor has it own private memory), whereas OpenMP was designed for shared memory.
The lazy assumption suggests that OpenMP is a better solution because it was designed for shared memory. However, the possibility of re-using an existing MPI code base is worth considering before you spend a month(s) re-inventing the software wheel. Ultimately, the question is really about efficiency. Namely, How does the performance of MPI compare to…
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