IEEE Xplore Digital Library Search on author Darrell Ulm

A search for IEEE Xplore entries of research papers in parallel computing for Darrell Ulm.

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IEEE Xplore Digital Library Search on author Darrell Ulm

A search for IEEE Xplore entries of research papers in parallel computing for Darrell Ulm.

Tumblr, Wordpress

Once the IDs are added, a DataFrame join will merge all the columns into one Dataframe.

# For two Dataframes that have the same number of rows, merge all columns, row by row.

# Get the function monotonically_increasing_id so we can assign ids to each row, when the # Dataframes have the same number of rows. from pyspark.sql.functions import monotonically_increasing_id

#Create some test data with 3 and 4 columns. df1 = sqlContext.createDataFrame([("foo", "bar","too","aaa"), ("bar", "bar","aa…

The Github code repo. for the Knapsack approximation algorithms is here, and it includes a Scala solution. The work on a Java version is in progress at time of this writing.

Below we have the code that computes the solution that fits within the knapsack W for a set of items each with it's own weight and profit value. We look to maximize the final sum of selected items profits while not exceeding the total possible weight, W.

First we import some spark libraries into Python.

# Knapsack 0-1 function weights, values and size-capacity. from pyspark.sql import SparkSession from pyspark.sql.functions import lit from pyspark.sql.functions import col from pyspark.sql.functions import sum

Now define the function, which will take a Spark Dataframe w…

I ran this on a local setup, so it may require modification if you are using something like a Databricks environment. Also you will likely need to setup your Scala environment.

All the code for this is at GitHub

First, let's import all the libraries we need.

Again, we'll define the knapsack approximation algorithm, expecting a dataframe with the profits and weights, as well as W, a total weight.

Calculate t…