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Grizzly demo workloads

This directory contains example workloads that make use of Grizzly and a Weld-ified NumPy. This README file assumes for convenience that WELD_HOME is set to the root weld/ directory.

$ export WELD_HOME=/path/to/weld/root/directory

Acquire Data for Demo Workloads

To get data for data_cleaning and other related workloads, run:

$ cd $WELD_HOME/examples/python/grizzly
$ mkdir -p data
$ wget
$ mv 311-service-requests.csv data/311-service-requests-raw.csv
$ scripts/prune-csv -i data/311-service-requests-raw.csv -l "Incident Zip"
$ scripts/replicate-csv -i data/311-service-requests-raw-pruned.csv -o data/311-service-requests.csv -r 30

To get data for get_population_stats and other related workloads, run:

$ cd $WELD_HOME/examples/python/grizzly
$ mkdir -p data
$ wget
$ mv us_cities_states_counties.csv data/us_cities_states_counties_raw.csv
$ scripts/transform-population-csv -i data/us_cities_states_counties_raw.csv -o data/us_cities_states_counties.csv -r 30

Running the Demo Workloads

The demo workloads are in $WELD_HOME/examples/python/grizzly.

Each workload has a corresponding Grizzly version. For example, the native Pandas/NumPy data cleaning workload is, while the corresponding Grizzly workload is

As an example, to compare performance between the native Pandas data cleaning workload and the Weld-ified Pandas data cleaning workload, run:

$ python                                         # Native
$ WELD_NUM_THREADS=<num_threads> python  # Grizzly

By default, will run with 1 thread.

These scripts print out timing information.

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