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?

The Choice is Yours: CUDA in C++ or Ruby

See the output here: Ruby Query Output

See the output here: C++ Query Output

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:

http://www.gpucomputing.net/?q=node/5277

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 Computing with Ruby

Presented in RedDotRubyConf 2011 - PechaKucha Night Singapore.

GPU Computing with Ruby

View more presentations from myxman.

Using SGC-Ruby-CUDA on the Newly Launched Amazon EC2 Cluster GPU

Wonder if GPU works for you? No budget for a system with decent GPU? Installations and configurations are too much trouble for you? You can now try out SGC-Ruby-CUDA on Amazon EC2 with the system image, located at US East Virginia zone, called SGCRubyCUDA.1 which is available as a community AMI.

Compile for rubycu shared library and run tests.

[root@ip-10-17-130-174 sgc-ruby-cuda.git]# rake
(in /root/sgc-ruby-cuda.git)
checking for main() in -lcuda... yes
creating Makefile
g++44 -I. -I/usr/local/include/ruby-1.9.1/x86_64-linux -I/usr/local/include/ruby-1.9.1/ruby/backward -I/usr/local/include/ruby-1.9.1 -I.   -fPIC -O3 -ggdb -Wextra -Wno-unused-parameter -Wno-parentheses -Wpointer-arith -Wwrite-strings -Wno-missing-field-initializers -Wno-long-long   -o rubycu.o -c rubycu.cpp
g++44 -shared -o rubycu.so rubycu.o -L. -L/usr/local/lib -Wl,-R/usr/local/lib -L.  -rdynamic -Wl,-export-dynamic   -lcuda  -lpthread -lrt -ldl -lcrypt -lm   -lc

[root@ip-10-17-130-174 sgc-ruby-cuda.git]# rake test
(in /root/sgc-ruby-cuda.git)
/usr/local/bin/ruby -I"lib:lib" "/usr/local/lib/ruby/1.9.1/rake/rake_test_loader.rb" "test/test_rubycu.rb" 
Loaded suite /usr/local/lib/ruby/1.9.1/rake/rake_test_loader
Started
......................
Finished in 89.055900 seconds.

22 tests, 99 assertions, 0 failures, 0 errors, 0 skips

Test run options: --seed 25668

Compile for rubygems then install it and try some SGC-Ruby-CUDA APIs.

[root@ip-10-17-130-174 sgc-ruby-cuda.git]# rake gem
(in /root/sgc-ruby-cuda.git)
mkdir -p pkg
  Successfully built RubyGem
  Name: sgc-ruby-cuda
  Version: 0.0.1
  File: sgc-ruby-cuda-0.0.1-x86_64-linux.gem
mv sgc-ruby-cuda-0.0.1-x86_64-linux.gem pkg/sgc-ruby-cuda-0.0.1-x86_64-linux.gem

[root@ip-10-17-130-174 sgc-ruby-cuda.git]# cd pkg
[root@ip-10-17-130-174 pkg]# gem install sgc-ruby-cuda-0.0.1-x86_64-linux.gem 
Successfully installed sgc-ruby-cuda-0.0.1-x86_64-linux
1 gem installed
Installing ri documentation for sgc-ruby-cuda-0.0.1-x86_64-linux...
Installing RDoc documentation for sgc-ruby-cuda-0.0.1-x86_64-linux...

[root@ip-10-17-130-174 pkg]# gem list

*** LOCAL GEMS ***

minitest (1.6.0)
rake (0.8.7)
rdoc (2.5.8)
sgc-ruby-cuda (0.0.1 x86_64-linux)

[root@ip-10-17-130-174 pkg]# irb
irb(main):001:0> require 'rubycu'
=> true
irb(main):002:0> include SGC::CU
=> Object
irb(main):004:0> CUDevice.get_count
=> 2
irb(main):005:0> d = CUDevice.get(0)
=> #<SGC::CU::CUDevice:0x0000000908c920>
irb(main):006:0> c = CUContext.new.create(0, d)
=> #<SGC::CU::CUContext:0x0000000907af40>
irb(main):007:0> d.get_name
=> "Tesla M2050"
irb(main):009:0> d.compute_capability
=> {:major=>2, :minor=>0}
irb(main):010:0> d.total_mem
=> 2817982464

Note: Remember to select Cluster GPU, when launching the instance.

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