First Release of SGC Ruby CUDA - Beginning of a long way path

Today we decided to put up the first release of the SGC Ruby CUDA v0.1.0 as a mean to attract Rubyists to try out GPU programming as their new toy projects, and also to encourage HPC developers to evaluate if Ruby is good to use for their HPC applications.

When important software libraries are not available in Ruby, we certainly do not expect much Ruby usage in the area. As time is running short, more and more hardware is piling up underutilized, we are urged to take the first fundamental step moving Ruby programming towards HPC applications by making important SDK such as Nvidia CUDA SDK available to a Ruby program.

Rubyists who are new to GPU programming can now access CUDA GPUs easily to harness the massively parallel architecture of GPUs. On the other spectrum, HPC developers now have a choice to manage their complex applications by large portion in Ruby, while retaining only relatively small section of codes in C/C++/CUDA C etc.

We believe Ruby programming could improve productivity and maintainability tremendously since in many cases, heavy computation only happens in small section of codes, and Ruby programming simplify the software architecture and implementation significantly. Even when the performance is extremely critical that one must port everything back from Ruby to C/C++/CUDA C for highest performance, one has already saved tremendous effort in software architecture and design to achieve manageable design, extendable, ease of use, etc. The porting back to C/C++/CUDA C becomes much more straightforward as one has gained much knowledge about the domain.

Compared to developing a complex application from scratch in C/C++/CUDA C, one has to go through unforeseeable curvy path to achieve the same state which is bound to very high failure rate. Hence, we believe that this could set the start of Ruby programming towards HPC applications.

SGC Ruby CUDA has been updated significantly since the last post about it. As we have packaged it into a Ruby gem, you can now install it with

gem install sgc-ruby-cuda

The code repository is hosted at github, SGC Ruby CUDA.
The documentations are available at rubydoc.info, SGC Ruby CUDA Doc.
Feel free to join the discussion group/mailing list at SGC Ruby CUDA Google Group.

GPU Anywhere with Cloud Computing

Simulation taking months to run? Buying and maintaining new systems causing too much hassle? Perhaps Cluster GPU would be a good candidate to save time and trouble. Cloud solution is an excellent platform for proof of concept before committed to a large system in-house.

Paying $2.10 per hour (Amazon pricing as of 16 Nov 2010) for the spec of:

22 GB of memory
33.5 EC2 Compute Units (2 x Intel Xeon X5570, quad-core "Nehalem" architecture)
2 x NVIDIA Tesla "Fermi" M2050 GPUs
1690 GB of instance storage
64-bit platform
I/O Performance: Very High (10 Gigabit Ethernet)
API name: cg1.4xlarge

That may not sound inexpensive in long run, but you now have the option to pay little for great fun, which is cheaper than a movie ticket. Get the systems for a few hours solving great puzzles. That sounds like an interesting weekend activity.

High Performance Computing Using Amazon EC2

High Performance for All

Parallel programming is much more affordable now as multi-core CPU and programmable GPU become commodity products. Unlike a decade ago where a minimum dual socket system equipped with lower clocked CPU & RAM would relatively cost a fortune to a typical desktop user, but dual-core system is basically everywhere nowadays. The use of dual-core systems is not really because it's affordable, but simply the users have not given a choice for not going multi-core.

It was non-trivial to me a decade ago, why should I go with lower clocked CPU & RAM in order to go multi-processing? Isn't that will slow down all my applications that use only single core? Fortunately, this problem is now less severe with dynamic clock adjusting CPUs, so called turbo mode. We could enjoy the benefits of high clock speed and multiple cores for different applications.

Moving forward, does commodity products make HPC a commodity service? How is HPC doing in enterprise?

Checkout the report published by Freeform Dynamics: High Performance for All