Parallel Programming

We are going to be honest here. Writing parallel codes is not simple. You can learn the mechanics of writing MPI codes from Jeff Squyres MPI Monkey column,but what about the computer science? Or more specifically, how are you going to make sure your code runs faster on multiple processors? Join Pavel Telegin and Douglas Eadline as they explain the issues and the answers.

Want to parallelize your code? Before you dive in, you might want to test the waters.

It can be said be said that writing parallel code is easy. It can also be said that getting your code to run fast and produce correct answers is a bit more difficult. With parallel computing, we don't have the luxury of a compiler optimizing for us, so we have to do the work. In this column we are going to look at some of the fundamentals and hopefully get you thinking about some of the issues that are critical for your success.

Clusters are organized in such a way that there is a set of independent computers connected by a communication network. The difference between clusters and multiprocessor computers (SMP systems) is the memory structure. Multiprocessors contain several CPUs which are connected in someway to memory. They can be connected by a bus or crossbar, but the most important thing is that all processors are connected to all the memory. This configuration is called shared memory. With a cluster, each node has its own memory that is local to cluster node. Other nodes can only access this memory through a communication network. This configuration is called distributed memory and is what makes a programming a cluster different from shared memory computer. Accessing the memory of other nodes results in substantial time delay. And because of this delay, a program that runs well on a multiprocessor will not necessarily run well on a cluster. Of course, each node can be and often is a multiprocessor, but the key to cluster programming is programming distributed memory systems because the limiting factor is the communication speed.

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