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Google Summer of Code 2018 Ideas
Ideas for Google Summer of Code 2018.
Feel free to reach us by joining #sciruby on chat.freenode.net or via our mailing list.
IMPORTANT NOTICE: SciRuby encourages diversity. Scientific progress in general benefits from diversity and software development for science is no exception. We are really happy that the number of people from Asia, Africa and South America applying for GSoC projects is increasing. Our org admin this year is from India, our previous org admin was from Brazil. We have had students from Japan, India, Sri Lanka, Russia, etc. We have women software developers in our programme. We are happy to hear from you all!
We strongly recommend that you pick one of the ideas listed below. We value contributions in advance of GSoC, even if they're just little ones. Go pick out something in one of our trackers and work on it, talk to folks on the listserv, and get an idea for what features are needed.
You don't need to know a lot about Ruby to work on a project: depending on how much you already know, it'll be pretty easy to learn enough to be able to contribute. However, you may need some familiarity with scientific computation. If you don't have any, take a look at "Numerical Recipes in C", which you'll probably find in your university's library.
In any case, if you feel your skills aren't enough for some project, please ask us on our IRC channel (see contact section above) or our Google Group (see sciruby.com to #) and we can help you.
See also:
Most of the main SciRuby’s landing page on Github holds the stable version of SciRuby gems but developers and contributors should work on the very latest (bleeding edge) repositories in order to make sure that changes can be committed without conflict arising.
Try reading Finding The SciRuby Development Repositories on Github if you would like a brief introduction on finding the latest development gems to work on from Github. Also go through the coding guidelines before sending your first patch.
Here's a great tutorial: http://www.thinkful.com/learn/github-pull-request-tutorial/
Have a look and feel free to ask if you have any questions.
Guidelines for mentors to submit projects:
- Specify the name of your project as a heading.
- Write a paragraph or two with further details.
- Write a small 'Skills' section detailing the skills that the student must possess to complete the project.
- Write down your own GitHub handle and contact details in a 'Mentor Details' section over which the student can contact you.
- If anyone else wants to co-mentor a project, please specify your details along with the mentor's details.
Similar to native CUDA kernel support in Julia, we should have support in Ruby.
Since it is tough to augment the Ruby VM to support this, it can be done in an easier way using Rubex and RbCUDA. For example, a sample Rubex method that would compile to a native CUDA kernel can be defined with cudadef and written like this:
require 'rbcuda_native'
require 'rbcuda'
include RbCUDA::Driver
cudadef kernel_vadd(a, b, c)
i = threadIdx().x
c[i] = a[i] + b[i]
return
end
# generate some data
len = 512
a = rand(len).to_i
b = rand(len).to_i
# allocate & upload to the GPU
d_a = GPU_Array.new(a)
d_b = GPU_Array.new(b)
d_c = GPU_Array.new(d_a)
# execute and fetch results.
kernel_vadd(d_a, d_b, d_c) with cuda(1,len)
c = d_c.to_cpu_array- Skills: C/C++, CUDA, Ruby, Ruby C API, parallel programming, familiarity with design of compilers.
- Mentor: Sameer Deshmukh @v0dro, Prasun Anand @prasunanand
- Difficulty: Moderate.
NMatrix is SciRuby's numerical matrix core, implementing dense matrices as well as two types of sparse (linked-list-based and Yale/CSR). NMatrix is a fairly well-established project which has received Summer-of-Code-like grants from both Brighter Planet and the Ruby Association (in other words, from Matz, who created Ruby). Those who contribute to NMatrix will likely eventually become authors of a jointly-published peer-reviewed science article on the library. Additionally, NMatrix is a good place to gain practical C and C++ experience, while also working to improve Ruby.
NMatrix currently relies on ATLAS/CBLAS/CLAPACK and standard LAPACK for several of its linear algebra operations. In some cases, native versions of the functions are implemented, so that the libraries are not required. There are quite a number of areas for growth in terms of the capabilities of NMatrix here.
- Mentors: John Woods (@mohawkjohn) , Prasun Anand(@prasunanand)
- Per this discussion, constraints of the Ruby language currently slow down element-wise addition and subtraction for NMatrix objects. There are possibly some work-arounds, described in that email thread. A successful proposal would involve some preliminary research and design work on how to speed up element-wise operations.
- Recommended skills: Some C/C++ would be beneficial, as you'll need to be working under the hood on NMatrix.