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.
Cluster Programming: Explicit Implications of Cluster Computing
- Published on Tuesday, 05 December 2006 03:47
- Written by Douglas Eadline
- Hits: 9057
A hard to swallow conclusion from the table of cluster
A confession is in order. The last two (1, 2) installments of this column have been a sales pitch of sorts. If you believe some of the things I talked about, large clusters will break, applications will need to tolerate failure and be easy to write, then you may agree that dynamic programming algorithms are one method to solve these problem. The next question is, how do implement these ideas?
The answer is the part many may find distasteful. If you are one of the brave few who take pause to think about how we are going achieve pervasive cluster (parallel) computing, then take a bite of this column. The rest of you weenies should at least nibble at the edges.
Read more: Cluster Programming: Explicit Implications of Cluster Computing
Cluster Programming: The Ignorance is Bliss Approach
- Published on Tuesday, 10 October 2006 21:00
- Written by Douglas Eadline
- Hits: 7796
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.
Read more: Cluster Programming: The Ignorance is Bliss Approach
Cluster Programming: You Can't Always Get What You Want
- Published on Tuesday, 19 September 2006 04:27
- Written by Douglas Eadline
- Hits: 8437
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.
Read more: Cluster Programming: You Can't Always Get What You Want
Parallel Programming: A Balancing Act
- Published on Saturday, 15 July 2006 11:47
- Written by Pavel Telegin
- Hits: 6296
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.
Is Your Code Concurrent?
- Published on Wednesday, 26 April 2006 21:00
- Written by Pavel Telegin
- Hits: 5711
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.
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