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Call for Participation: OFA Workshop in Austin

The Annual OpenFabrics Workshop Renews Emphasis on Collaboration within OpenFabrics Community

Update: Due to a high level of interest in presenting at the OpenFabrics Alliance (OFA) Workshop 2020, the OFA has extended the Call for Sessions submission deadline by one week. Proposals are now due by January 24, 2020, at 5:00 p.m. PST. The Technical Program Committee will respond to proposals and announce session topics by February 3, 2020.

The OpenFabrics Alliance has issued their Call for Participation for the 2019 OFA Workshop. The event takes place March 20-21 in Austin, Texas.

The annual OFA Workshop, returning this year to Austin, TX, is a premier means of fostering collaboration among those who develop fabrics, deploy fabrics, and create applications that rely on fabrics. It is the only event of its kind where fabric developers and users can discuss emerging fabric technologies, collaborate on future industry requirements, and address problems that exist today.

The 2019 OFA Workshop will focus on the following overarching themes:

Administration, Management and Deployment

  • Fabric technology
  • Traffic policy, engineering and control
  • Diverse fabrics

The OFA and the Open Community

  • OSs and I/O subsystem support
  • Security, namespaces
  • File systems, protocols, DAX

Interoperability

  • Goals
  • Objective
  • Methods

Remote Persistent Memory

  • Programming interfaces
  • Protocols
  • Devices

Submission Instructions

Submit your session proposal by January 24, 2020, at 5:00 p.m. PST.  via EasyChair

The OpenFabrics Alliance (OFA) is an open source-based organization that develops, tests, licenses, supports and distributes RDMA/Advanced Networks software and the OpenFabrics Enterprise Distribution of the RDMA/Advanced Networks software. The Alliance’s mission is to develop and promote software that enables maximum application efficiency by delivering wire-speed messaging, ultra-low latencies and maximum bandwidth directly to applications with minimal CPU overhead.

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