Hi all,
as it just came up on the ML, I want to bring this up again for general
discussion. I think we should try to get at least one or two students
for this year's GSOC. If you have never heard of GSOC, look here:
http://write.flossmanuals.net/gsoc-mentoring/what-is-gsoc/ and here:
https://developers.google.com/open-source/gsoc/
Applications for organizations open on January 19th and it is a great
way of introducing new people to the SystemML development and get more
contributors.
To apply, we need to propose projects for a 4-month period in which a
student works on them full time (May - August). Each proposed project
needs one community member to mentor it - in the end Google decides how
many students each project gets, depending of the quality of the
proposed ideas.
To successfully apply we need (1) good ideas for projects and (2) people
willing to mentor those ideas.
For an initial brainstorming I suggest that we first figure out if we
want to participate (which mainly means we need to find people willing
to mentor projects) and then start collecting ideas. Ideas can be
anything from infrastructure, to core development or implementation of
new algorithms.
Here is a quick example of how a project proposal could look like:
Title: Performance Benchmarks and Experiments
Description: To make decisions about new features and the evaluation of
old assumptions we need up-to-date performance statistics on multiple
levels of the systems and on different architectures (local,
distributed, GPU). The systematic evaluation of performance can be
measured with performance tests and micro-benchmarks. In this way,
changes to the project or alternative implementations (i.g. for
low-level linear algebra backends) can be systematically evaluated and
compared. (Semi-) Automated benchmarks can help make these decisions and
challenge assumptions that were made during earlier development. In the
course of this project, the student should build a benchmark
infrastructure and conduct experiments, that compare different choices
in critical parts (sparsity thresholds, BLAS backends, optimization
decisions, etc.).
Expected Outcome: A benchmark suite than can be used to detect
regressions or improvements in critical components of the system.
Skills required: Java/Scala, some knowledge of benchmarking; preferred:
knowledge about high-performance-computing and/or distributed systems.
Possible Mentors: Matthias, Niketan, Nakul, Felix
Let's decide on if we want to apply as an organization!
- Felix
