Parallel NetCDF Library Development

Formerly “Sensor Cloud Integration”

Kelsey Weingartner

Overview Background information

Purpose

Project artifacts

Final product

Results

Takeaways

NetCDF and MASS

NetCDF Machine-

independent format for representing scientific data

Files stores data arranged in variables

Each variable holds an array of data

MASS Library for running a

simulation in parallel

Eases the complexity of creating and running 2D and 3D spatial simulations

A simulation is a grid of “Places” that may or may not have “Agents” on them

Purpose

Within MASS• Make NetCDF file use simple

and feasible for MASS• Maintain the benefits of a

distributed environment running in parallel.

Real-World Applications• Climate change analysis

Artifacts Summer 2012• Sequential write with NetCDF• Worst-case parallel performance

Fall 2013• Best-case parallel performance• File creator• File creator with parallel write• File creator with parallel write & read

Winter 2013• Single instance per processor file creator

and parallel writer• Final product

Sequential Each save

requires the file to only be opened once

callAll() gathers agent information from each Place

Master node then handles writing to the NetCDF file

Parallel - Worst-Case

Each save, the file is opened by every Place object

Master triggers save with callAll()

Place gathers its Agents’ information and writes

JavaMPI Parallel Best-Case

Drag picture to placeholder or click icon to add Select a NetCDF file to

copy

Master node creates a new file with same dimensions

Send an equal portion of data from the chosen file to each node

Each node writes their received array to the newly created NetCDF file

Final ProductSingle Instance per processor file creator and

parallel reader/writer

Extends MASS Place

Creates a file for the simulation if none exists

Stores file contents in a buffer to increase read/write speed

Each processor holds the portion of the file relevant to them

A file is only opened by the first writer Place in each partition

Results Sequential write (1 processor): • 100x100, 1,000 agents, 1,000 cycles = 225,712.8 msec

Worst-case parallel write (1 processor): • 50x50, 500 agents, 100 cycles 957,590.5 msec

MPInetCDF results on a 50x50 file:• On 4 processors: 22,114.4 msec / 246,444 bytes = 0.0897

B/msec

• On 6 processors: 16,470.2 msec / 246,444 bytes = 0.0668 B/msec

RandomWalk using parallel NetCDF (1 processor):• 100x100, 1,000 agents, 1,000 cycles = 204,997.2 msec /

472,484 bytes = 0.434 B/msec

Final Product Results

RandomWalk• 100 x 100 grid, 1000 agents,

100 cycles: 7,843.7 msec

RandomWalk with NetCDF• 100 x 100 grid, 1000 agents,

100 cycles, writing to file every 20 cycles: 204,997.2 msec

• Previous settings, but writing to file only once: 69,480.7

Wave2DMASS• 100 x 100 grid, 1000 cycles:

16,913.5 msec

Wave2DMASS with NetCDF• 100 x 100 grid, 1,000 cycles,

writing to file every 50 cycles: 50,422.6 msec

• Previous settings, but writing to file only once: 22,923.9

Future WorkOn Parallel_NetCDF

D0 array support Object datatype support Allow a whole variable to be

read/written Smaller buffer

After Parallel_NetCDF Conference paper for IEEE PacRim

Conference

Key Lessons

Working with external libraries

Working with limited documentation

Creating and meeting deadlines

Experience with parallel and distributed systems

Questions?

Intermediate Products

File Creators

FileCreator• Create uniform 2D or 3D grids

• Can create NetCDF files with an unlimited dimension.

FileManipulator 1.0• Create uniform 2D or 3D grids

• Write 1D or 2D arrays of integer

FileManipulator 2.0• Create uniform 2D or 3D grids

• Read or write whole variable or single value

• 8 datatypes supported

Single Instance Iterations

Single instance per processor reader• Create uniform 2D or 3D

grids

• Read or write whole variables

• 8 datatypes supported

Tools Used Java

Eclipse IDE

JavaMPI

MASS Library

NetCDF Library