Thursday, July 4, 2013

[Resolved] Skype crashed on Fedora 19

Running Skype on Fedora 19 gets core dumped immediately.

By luck I found a workaround using mesa-libGL library from Fedora 17.

  1. Download the mesa-libGL-8.0.4-1.fc17.i686.rpm from Fedora, or get it here.
  2. Extract the rpm file.
    • $ rpm2cpio mesa-libGL-8.0.4-1.fc17.i686.rpm | cpio -idv
  3. Run skype with the library.
    • $ LD_LIBRARY_PATH=usr/lib /usr/bin/skype

That seems working fine for me.

Monday, June 18, 2012

Personal Supercomputing System with Quad GPUs

The secrets of the new Kepler GPUs have been revealed. The Kepler based graphics cards have been studied extensively for gaming performance. Most would suggest you don't need the upgrade. Furthermore, the supported PCI-E 3.0 is of little to no use.

Well, it's probably a different story for CUDA programs. Here's the setup I'm going to use for testing CUDA programs extensively.

  • Asus P8Z99-V Premium
    • Supports dual 16x PCI-E. Most other boards support only single 16x.
    • Built-in 32GB SSD storage.
    • At the time of writing, the system is not fully functional when the 2nd GTX 690 is installed. The BIOS probably needs update to fix the problems.
  • Intel Ivy Bridge Core i7 3770K 3.5GHz/8MB (3.9GHz at Turbo mode)
  • Corsair Vengeance 1600MHz 32GB
  • Asus GTX 690 4GB x 2
  • Kingston HyperX SSD 3K SATA3 240GB
  • Western Digital Black SATA3 2TB
  • Corsair AX1200 1200W
  • Antec Kuhler 920 (Liquid cooling)
    • The CPU is extremely hot 80+ C with stock fan at full load. Liquid cooling is highly recommended.
    • Unfortunately, the fan control software is rather limited. It controls the fan based on the liquid temperature which is very different from the CPU temperature. As a result, the fan does not automatically ramp up on high CPU temperature as the liquid temperature may be much lower.

Asus GTX 690 in a box

GTX 690 is much longer than old GTX 285. Prepare a long casing for the card.

Full system with dual GTX 690

A closer look at the LED illuminating GTX

Sunday, June 26, 2011

Being Nvidia CUDA Certified Programmer!

It takes some courage and effort to take the Nvidia CUDA Certification exam. You'll have to pay S$350 for that yet there is no guarantee of real use in business and career. The exam questions are perfect to squeeze out all your brain juice.

After much feedback and long awaiting, delayed plans, finally I received an email about being Nvidia CUDA certified programmer now. It's better arrived late than never. But what's next?

Let's call for all Nvidia CUDA Certified Programmer, starting from Singapore. Anyone else CUDA certified? What's your plan on CUDA?

Tuesday, May 3, 2011

Web Seminar: Programming GPUs Beyond CUDA

GPU/CUDA programming is easy if we ignore the performance, or even the correctness of the program. It becomes tough when the performance is critical, one has to optimize very hard on the specific hardware. Fortunately, GPU hardware performance improves drastically every 2 years. Unfortunately, the performance is not portable across different generations of GPUs.

Prof Chen from Tshing Hua University is proposing MapCG, a MapReduce framework as a resolution to the portability problem.

Check out the details of the seminar in the following link:

Saturday, April 30, 2011

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, SGC Ruby CUDA Doc.
Feel free to join the discussion group/mailing list at SGC Ruby CUDA Google Group.

Sunday, April 24, 2011