BoF: Analyzing Parallel I/O

Parallel I/O performance can be a critical bottleneck for applications, yet users are often ill-equipped for identifying and diagnosing I/O performance issues. Increasingly complex hierarchies of storage hardware and software deployed on many systems only compound this problem. Tools that can effectively capture, analyze, and tune I/O behavior for these systems empower users to realize performance gains for many applications.

In this BoF, we form a community around best practices in analyzing parallel I/O and cover recent advances to help address the problem presented above, drawing on the expertise of users, I/O researchers, and administrators in attendance.

The primary objectives of this BoF are to: 1) highlight recent advances in tools and techniques for monitoring I/O activity in data centers, 2) to discuss experiences and limitations of current approaches, 3) to discuss and derive a roadmap for future I/O tools with the goal to capture, assess, predict and optimize I/O.

The BoF is held in conjunction with the Supercomputing conference. The official schedule is listed here.

Date Wednesday, November 20th, 2019
Time 5:15pm - 6:45pm
Venue Room 220, Denver, USA

The BoF is powered by the Virtual Institute for I/O and ESiWACE 1).

The BoF is organized by

The agenda is currently in preparation. We have a series of talks followed by a longer discussion:

  • Introduction
  • Dean Hildebrand (Google)
  • Eugen Betke (DKRZ)
  • Kevin Huck (University of Oregon)
  • Panel and discussion

Julian Kunkel – Dr. Kunkel is a Lecturer at the Computer Science Department at the University of Reading. He manages several research projects revolving around High-Performance Computing and particularly high-performance storage. Besides his main goal to provide efficient and performance-portable I/O, his HPC-related interests are: data reduction techniques, performance analysis of parallel applications and parallel I/O, management of cluster systems, cost-efficiency considerations, and software engineering of scientific software.

1)
ESiWACE has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No 675191