Skip to main content

Getting back into parallel computing with Apache Spark

Returning to parallel computing with Apache Spark has been insightful, especially observing the increasing mainstream adoption of the McColl and Valiant BSP (Bulk Synchronous Parallel) model beyond GPUs. This structured approach to parallel computation, with its emphasis on synchronized supersteps, offers a practical framework for diverse parallel architectures.While setting up Spark on clusters can involve effort and introduce overhead, ongoing optimizations are expected to enhance its efficiency over time. Improvements in data handling, memory management, and query execution aim to streamline parallel processing.A GitHub repository for Spark snippets has been created as a resource for practical examples. As Apache Spark continues to evolve in parallel with the HDFS (Hadoop Distributed File System), this repository intends to showcase solutions leveraging their combined strengths for scalable data processing.



Labels:

Popular posts from this blog

Darrell Ulm Git Hub Profile Page

This is the software development profile page of Darrell Ulm for GitHub including projects and code for these languages C, C++, PHP, ASM, C#, Unity3d and others. Here is the link: https://github.com/drulm The content can be found at these other sites: Profile , Wordpress , and Tumblr . Certainly we're seeing more and more projects on Github or moving there and wondering how much of the software project domain they currently have percentage-wise.
Read more

Apache Spark Knapsack Approximation Algorithm in Python

The code shown below computes an approximation algorithm, greedy heuristic, for the 0-1 knapsack problem in Apache Spark. Having worked with parallel dynamic programming algorithms a good amount, wanted to see what this would look like in Spark. 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 ...
Read more