Our mission is to build a platform that allows data scientist to explore data, train machine learning algorithms, and build applications while primarily staying on the GPU
There are many ways for a project, group, individual, or company to join the GPU Open Analytics Initiative (GoAi). Below are details. If you have any questions, please reach out.
Adopters are projects, products or groups that are currently using or planning to use a GoAi project. For example, a database adding support for the GPU Data Frame can be a GoAi adopter.
Contributors are any projects, groups, or individuals that would like to help develop projects. Since the GoAi is based around open projects, clone a repository on our GitHub channel and make a pull request. To help faciliate the process, let us know of your plans on our public Google Groups.
A member is responsible for stewardship of the GoAi organization, including decision-making about expanding GoAi to future projects, planning for events and outreach, and generally promoting the GoAi mission. Members will meet periodically to discuss and vote on GoAi management issues. However, technical decisions are handled by the open source developer communities around individual GoAi projects and membership is not a requirement for technical contributions. An organization can request membership from the existing members.
If the criteria is applicable, apply for membership on our public Google Groups. Existing members will vote on the admission of new members based on the above criteria. Note that adopters do not need to satisfy the above requirements.
Members are expected to follow the below criteria:
Below are our current members:
Our projects are based around an open platform, which is the foundation of the GPU analytics ecosystem. Members will help guide the development of projects, but anyone can be a contributor. And because its open, anyone can freely use the projects.
Currently, GoAi is supporting the GPU Data Frame project (GDF), which will enable tabular data to be directly exchanged between libraries and applications on the GPU. We expect other projects to be added in the future, such as a graph standard for the GDF.
The basic approach for the GPU Data Frame (GDF) is pretty simple: if applications and libraries agree on an in-memory data format for tabular data and associate metadata, then just a device pointer to the data structure need be exchanged. Additionally, the IPC mechanism built into the CUDA driver allows device pointers to be moved between processes.
The GDF uses the Apache Arrow columnar format to represent data on the GPU. Some Arrow features are not yet supported.
In the planning stages.