I guess it all depends on where you jump into the fray with this parallelism stuff. In the server world, most of the applications are concerned with transaction throughput and the number of simultaneous user requests that can be successfully processed. So various schemes are concocted to deal with the load of simultaneous user requests. So multicore configurations have an obvious application at the server level. In the application world, things are not always so clear, but requirements for concurrency pop up.

Applications developed using modern programming languages can become legacy code faster than expected. Multicore microprocessors have been found guilty of transforming outstanding serial code into legacy code in just a few years. However, there are many alternatives to translate multicore power into application performance making small changes to the legacy serial code.

Surely some vendor should be able to come along and make the number of cores or processors transparent to our application. After all, isn't one of the operating system's jobs to hide the details of the hardware from the programmer?

On the one hand, Garbage Collectors simplify developers' lives, but on the other hand, they can become the greatest enemies of a parallelized algorithm's performance. Finally, Java 7 and .Net 4 are going to offer new Garbage Collectors really targeted for multicore microprocessors with large memories.

When I first dipped my toes into parallel programming I hunted around for a legacy application to change from serial to parallel. Rather than work on one of my own apps, I looked for a serial application on the web that had lots of CPU activity. I ended up choosing the Dhrystone benchmark.

GPUs are dedicated processors that perform 3D graphics rendering. GPU manufactures are in a frenzy to deliver the latest technology that accelerates performance at a lower cost while still delivering high-end 3D graphics. Maybe this is something to think about. Maybe not. I do.

In this conversation with Timothy G. Mattson, Senior Research Scientist Intel, Computational Software Lab, we find close agreement on the best approach to applying parallelism to business and general application programming. But first we discuss quantum physics.

If you've been trying figure out what "cloud computing" is, you're not alone -- and coming to the rescue is the National Institute of Standards and Technology. But as nice as the NIST folks are, they didn't have you in mind when they decided to put together an understandable definition of cloud computing.

After a long silence since the last CTP (Community Technology Preview), Microsoft released the Visual Studio 2010 Beta 1 that comes with the new .Net Framework 4.0. This new release will bring new promising features that empower parallel programming using the supported languages.

I'm a sucker for a good pun. (Of course, some would say that there's no such thing as a "good" pun.) So when I came across a paper entitled FAWNdamentally Power-efficient Clusters, I had to read it. And you know what? Not only did I enjoy reading the paper, I actually learned a thing or two about a very important topic.

Are you still designing RIAs (Rich Internet Applications) using a serial programming approach? If that's the case, then you are losing a big opportunity to achieve a sustainable competitive advantage over competitors.

Management professionals love metrics. However, they don't like the idea to optimize applications that are currently running. Thus, what about showing them some metrics about the power that they are wasting using a free tool?

You can tell when technology has matured, in part by how much the user needs to be an expert in using it. A step in maturing for parallel programming will come as we see that the efficiency of a programmer is more important than the efficiency of the machine.

Many embedded system designers are still struggling to determine whether multicore technology really buys them anything in terms of performance. Resolving this quandary requires a thorough understanding of the target application, the amount of time that must be invested to make the transition, and the characteristics of multicore processors that could be used.

A couple of quick ideas for getting started with OpenMP.

1. Compile a cute example


2. Read a book