- Information for Students
- Adding a Proposal
- Previous year's Project Ideas
- Project Ideas
Information for Students
These ideas were contributed by our developers and users. They are sometimes vague or incomplete. If you wish to submit a proposal based on these ideas, you may wish to contact the developers and find out more about the particular suggestion you're looking at.
Being accepted as a Google Summer of Code student is quite competitive. Accepted students typically have thoroughly researched the technologies of their proposed project and have been in frequent contact with potential mentors. Simply copying and pasting an idea here will not work. On the other hand, creating a completely new idea without first consulting potential mentors is unlikely to work out.
If there is no specific contact given you can ask questions on the general Clojure Google Group - http://groups.google.com/group/clojure
Adding a Proposal
Please follow this template. If you don't have Confluence access please submit your idea, following this template to the clojure mailing list with the subject prefix
[GSoC Idea]. Also, feel free to add new categories.
If you are not a developer but have a good idea for a proposal, get in contact with relevant developers first.
Previous year's Project Ideas
Please organise your project idea into a category below. Feel free to create new categories if needed.
Lean JVM Runtime
Brief explanation: The current Clojure/JVM runtime is fully-featured, and can become even more fully-featured with projects like core.typed. However, sometimes it is important to have a leaner runtime that is less resource-intensive. This is ideal for instances where Clojure serves primarily as a library or in lean runtime environments like Google App Engine or Android where a quick startup is desirable. The goal of this project would be to create an alternative lean Clojure runtime and associated compiler. The compiler may be based either of the standard compiler or the ClojureScript compiler. Clojure vars would compile to static fields or methods. It will be necessary to find out which subset of the language makes sense (for example, vars would probably not really exist anymore).
Expected results: A lean Clojure runtime and compiler (well, some good progress to this end)
Knowledge prerequisites: A thorough understanding of Clojure and some familiarity with byte code generation for the JVM.
Mentor: Daniel Solano Gómez
Many of us are working to make Clojure into a great platform for Data Science, Big Data and numerical computing. See the Numerical Clojure Group for ideas and discussions. There are many opportunities to take on exciting projects in this space - a few ideas are listed below:
Linear Algebra for Clojure
Brief explanation: core.matrix provides a powerful and flexible API for array programming and numerical computing in Clojure. To take this to the next level, it is important to implement a suite of linear algebra algorithms - including various matrix decompositions (SVD, QR, Cholesky etc.....), linear system solvers etc. These need to be added both to the core.matrix API, and to one or more core.matrix implementations. A good option would be to add these algorithms to vectorz-clj, which is a well-maintained core.matrix implementation that runs 100% on the JVM. It would be possible to port or adapt existing Java algorithms (e.g. from EJML). Our goal would be to match / exceed the performance of other Java-based libraries - see the Java Matrix Benchmark
Expected results: A clean, well-defined API for linear algebra in core.matrix. Fast working implementations of the API added to vectorz-clj, with performance comparable to the best libraries in the Java Matrix Benchmark
Knowledge prerequisites: Mathematics / linear algebra, Java, Clojure
Skill Level: Hard
Mentor: Mike Anderson
Brief explanation: core.matrix provides a powerful and flexible API for array programming and numerical computing in Clojure. It should be possible to create an implementation for core.matrix that performs computations on the GPU. Ideally, matrices would be retained in GPU memory as much as possible, to avoid unnecessary copying to/from main memory.
Expected results: A full core.matrix implementation with GPU-based array storage and GPU-accelerated operations. Benchmarks to demonstrate clear performance gains over non-GPU approaches for key operations (e.g. matrix multiplication)
Knowledge prerequisites: Mathematics / linear algebra, Clojure, GPU programming
Skill Level: Hard
Mentor: Mike Anderson (core.matrix aspects) - would need another mentor with GPU expertise
Incanter + core.matrix integration
Brief explanation: core.matrix provides a powerful and flexible API for array programming and numerical computing in Clojure. Incanter is a platform for statistical computing and graphics. Bringing the two together would create a powerful combination.
Expected results: Incanter modified to take full advantage of core.matrix.
Knowledge prerequisites: Statistics, Mathematics, Clojure
Skill Level: Hard
Quil on ClojureScript
Expected results: quil clojurescript library capable of running regular quil sketches, documentation how to use it in clojurescript project.
Skill Level: Medium
Mentor: Nikita Beloglazov (email@example.com)
Dynalint: Improved Core Clojure errors and warnings
Brief explanation: Error reporting has been identified as a common complaint of Clojure in the latest Clojure Survey. In many cases, addressing these concerns directly in the core Clojure implementation would degrade performance, while an explicit "strict" or debug mode for Clojure would require extensive changes to Clojure core. Dynalint takes another approach: it is an external tool that can "instrument" core Clojure functionality to emit warnings and better errors. This approach has some limitations, but it can quickly make headway in an area that Clojure struggles with.
Expected results: A comprehensive suite of useful warnings and errors for the (near) latest version/s of Clojure.
Knowledge prerequisites: None
Mentor: Ambrose Bonnaire-Sergeant