It appears as if the latest way to find more processing power is heading into the mainstream. Both Nivida and AMD have GPU cards dedicated to processing instead of graphics. The popular book series GPU Gems has even released a third generation of the series that contains quite a bit of information and examples using the CUDA architecture. However, if you’ve looked at CUDA you may have had a Bud Light commercial moment like I did and said, “Dude…”. The years of not writing code may be skewing my view on it, but to me this is not a simple API. It seems that without easier to use higher level tools, GPU processing might be restricted to the uber-brains of software engineering that do matrix math in their head for fun. Now I don’t want to single out CUDA, because Brook doesn’t seem likely to win any Easy Button awards either.

What is clearly needed is an abstract API on top of both vendor cards, that lets C++ developers write code without a huge learning curve. That’s an easy statement to make, but a lot of complexities arise. I have been asking some of our customers about what they think an API like this should look like, what level of complexity to hide and not hide, etc. While there is some consensus, there is also just as much differing opinion. The only obvious answer seems to be that people want to take advantage of GPUs, and they’re looking for a way to make it easier for them to do so. If you have strong feelings on what you would like to see, please let them be known.

It was our second year participating in the OOP conference in Munich last week.  This conference focuses on aspects of modern software engineering, so as you can imagine, it is heavily infused with SOA technologies.  I gave two talks, one about modernizing C++ applications, and the other on creating a service grid.  In my talks I focused on the challenges of maintaining performance characteristics when moving from a monolithic application to an SOA, while also dealing with distributed computing issues that may not have been previously relevant.  Both drew a good number of people and several of them came by our booth later to discuss their situation and how it would fit in with what I discussed. 

I was a bit surprised that there were a good number of people that said they have problems dealing with large XML files.  We call this VLM (Very Large Messaging) and we are very familiar with the issues surrounding it.  The most common issue has to do with memory consumption.  Typical DOM based parsers use about 6 times the size of the original XML file in memory.  When you start dealing with large files, that quickly becomes a problem.  The other main issue people are having is rooted in performance.  Parsing large files is time consuming, especially when unexpected things like garbage collection kick in.  We have been solving this problem for a while now with our HydraSDO for XML product.  It’s very fast being written in C++, provides a native Java API as well that utilizes shared memory so as not to impact performance, and uses a non-extractive, indexed parsing model that gives a 1.5 times original XML document size memory footprint. 

I’m excited for the opportunity to help these people with the large XML file problems they’re having.  If you’re having similar issues, let me know and we can get you some quick help.

Two New York University professors, Dr. Robert Dewar and Dr. Edmond Schonberg, have written an article in the Journal of Defense Software Engineering about computer science courses neglecting basic skills, in particular in the areas of programming and formal methods.  The professors’ opinions have attracted discussion in places such as Slashdot and other popular Web sites since they have taken particular exception to the recent emphasis on Java in computer science courses.

The professors provide brief overviews of the educational benefits of various programming languages, including C and C++:

Why C Matters
C is the low-level language that everyone must know. It can be seen as a portable assembly language, and as such it exposes the underlying machine and forces the student to understand clearly the relationship between software and hardware. Performance analysis is more straightforward, because the cost of every software statement is clear. Finally, compilers (GCC for example) make it easy to examine the generated assembly code, which is an excellent tool for understanding machine language and architecture.

Why C++ Matters
C++ brings to C the fundamental concepts of modern software engineering: encapsulation with classes and namespaces, information hiding through protected and private data and operations, programming by extension through virtual methods and derived classes, etc. C++ also pushes storage management as far as it can go without full-blown garbage collection, with constructors and destructors.

What perhaps makes the professors’ opinions more credible is that their concerns are also commercial since they develop software commercially:

As founders of a company that specializes in Ada programming tools for mission-critical systems, we find it harder to recruit qualified applicants who have the right foundational skills. We want to advocate a more rigorous formation, in which formal methods are introduced early on, and programming languages play a central role in CS education

As the head of Rogue Wave Software’s professional services group, I’ve noticed that many of the younger engineers working in some of our customers are less skilled in C++ than their older colleagues.  While we’re happy to help our customers by filling in the skills gap on projects, it’s still a longer term worry because every programming language needs well educated and skilled developers. Teaching C++ in universities is a topic that I’ve been thinking about for some time. I’m particularly interested in what Rogue Wave can do to help with fundamental C++ skills, like promoting the use of the C++ Standard Library in universities and perhaps looking at the practicalities of making Rogue Wave’s products available for use in computer science courses.