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.

Dynamic Parallel Execution: Losing control at the high end

In a past column, I talked about programming large numbers of cluster nodes. By large, I mean somewhere around 10,000. If you have been following along, at the end of the article I had promised to mention some real alternatives to MPI and even suggest some wild ideas. I plan to keep my promise, however, I wanted to take a slight detour this month and develop the solution a bit further. One point of note before we begin. To keep things simple, I will refer to cluster nodes as if they were a single processing unit.

But it does not stop me from asking

Fifteen years ago I wrote a short article in a now defunct parallel computing magazine (Parallelogram) entitled "How Will You Program 1000 Processors?" Back then it was a good question that had no easy answer. Today, it is still a good question that still has no easy answer. Except now it seems a bit more urgent as we step into the "mulit-core" era. Indeed, when I originally wrote the article, using 1000 processors was a far off, but real possibility. Today, 1000 processors are a reality for many practitioners of HPC. As dual cores hit the server rooms, effectively doubling the processor counts, many more people will be joining the 1000P club very soon.

Static or Dynamic? It is a matter of balance

Now that we know how to identify parallel parts of our program, the question is now what to do with this knowledge. Or, how do you write a parallel program. To answer this question, we will discuss what the structure of a parallel program may look like. Programs can be organized in different ways. We already discussed SPMD (Single Program Multiple Data) and MPMD (Multiple Programs Multiple Data) models. SPMD and MPMD represents the way a program looks from the point of view of the cluster. Note, that using a MPMD model with MPI an "app" or "procgroup" file will be needed to start different programs on cluster nodes. Let's see what the programs look from the implementation standpoint.

It All Depends on the Dependencies

In this article, we continue our series on writing parallel programs with a discussion on how to determine if a program has concurrent sections by looking at the code. The formal way to do this is to determine flow dependence. In fact, conditions for concurrency are basically the same as for reordering instructions. When instructions can be reordered, they can be executed concurrently. Here is simple example of two Fortran statements that are concurrent.

More parallel secrets; speedup, efficiency and your code

In the previous column, we found that parallel programs for clusters have very subtle differences and their efficiency requires careful examination of the code. In this article, we will see what a typical parallel program looks like and how it is executed on a cluster. Be warned, however, there is a bit of gentle mathematics in this column. It will not hurt, we promise.


Login And Newsletter

Create an account to access exclusive content, comment on articles, and receive our newsletters.


Share The Bananas

Creative Commons License
©2005-2016 Copyright Seagrove LLC, Some rights reserved. Except where otherwise noted, this site is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License. The Cluster Monkey Logo and Monkey Character are Trademarks of Seagrove LLC.