Update on 13.2.1 Grib Decoder Threads

Since the last update, which involved testing only the ingest and decoding of CONDUIT 0.5/2.5 degree GFS, I've opened up the NGRID and NEXRAD3 feeds, as well as text and satellite products from the WMO and NIMAGE feeds, respectively.

 The goal is to compare the speed of the grid decoder on high-resolution CONDUIT GFS runs alone versus running in parallel with the full nationwide NEXRAD3 feed and other products.

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Testing 13.2.1 Unified Grib Decoder on CONDUIT GFS

Last month we received the first version of AWIPS II which included the new unified grib decoder (13.1.2). The install procedure for 13.1.2 was more complicated than usual - we needed the full 13.1.1 installation plus a 13.1.2 "update" - so around 8 GB of RPMs to manage. 

If you're unfamiliar with what the unified grib decoder is, here's a quick rundown: before 13.1.2, the D2D perspective (for WFOs) and the National Centers Perspective (for NCEP centers) required separate data decoders and database tables for grib messages. D2D used a decoder called grib, while NCP used a decoder called ncgrib. If you didn't want to bog down your system, you could only run one at a time, meaning: depending on your server configuration, gridded model data would only be visible in one perspective, not both.
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Introducing IDV 5.0 - Lynx!

Introducing IDV 5.0 - code name Lynx!

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Chunking Data: Choosing Shapes

In part 1, we explained what data chunking is about in the context of scientific data access libraries such as netCDF-4 and HDF5, presented a 38 GB 3-dimensional dataset as a motivating example, discussed benefits of chunking, and showed with some benchmarks what a huge difference chunk shapes can make in balancing read times for data that will be accessed in multiple ways.

In this post, I'll continue looking at that example dataset to see how we can derive good chunk shapes, generalize to other datasets, look at how long it can take to rechunk a multidimensional dataset, and look at the use of Solid State Disk (SSD) for both accessing and rechunking data.

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Chunking Data: Why it Matters

What is data chunking? How can chunking help to organize large multidimensional datasets for both fast and flexible data access?  How should chunk shapes and sizes be chosen?  Can software such as netCDF-4 or HDF5 provide better defaults for chunking? If you're interested in those questions and some of the issues they raise, read on ...

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