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[2022] COMP5426 Parallel and Distributed Computing - Final Exam Q4 LU Decomposition
Parallel and Distributed ComputingThe University of Sydney
This question is part of the COMP5426 Parallel and Distributed Computing - Final Exam Semester 1 2022, the University of Sydney
[2022] COMP5426 Parallel and Distributed Computing - Final Exam Q5 Parallel Reduction Operation
Parallel and Distributed ComputingThe University of Sydney
This question is part of the COMP5426 Parallel and Distributed Computing - Final Exam Semester 1 2022, the University of Sydney
DTS202TC Foundation of Parallel Computing - Group Assessment 1: Convert a RBG image to a grey-scale image
DTS202TCFoundation of Parallel ComputingConvert a RBG image to a grey-scale imageCParallel Algorithm
The purpose of this assignment is to gain experience in C programming and parallel algorithm design. You are expected to write a C serial program to convert a RBG image to a grey-scale image (no parallelism), and provide a parallel algorithm design that may speed up the serial implementation
STAT605/STAT405 Data Science Computing Project - Homework 3: Distributed Computing via Slurm and the Statistics High Performance Computing (HPC) Cluster
WISCUniversity of Wisconsin-MadisonStatistics 405Statistics 605Data Science Computing ProjectR
Use lunchbox only for editing and running Slurm commands that launch and manage jobs. (Do not run computations on lunchbox, as it cannot handle computations from many people.)
STAT605/STAT405 Data Science Computing Project - Homework 4: Large-scale distributed computing at CHTC
WISCUniversity of Wisconsin-MadisonStatistics 405Statistics 605Data Science Computing ProjectR
This exercise, an expanded version of Homework 2, is a full-scale search using the CHTC (via learn.chtc.wisc.edu) for an undiscovered, gravitationally lensed, high-redshift Lyman-break galaxy.
ST446 Assessment 2: LDA Clustering
Page RankLDAClusteringTopic ModellingData AnalysisST446
In this exercise, the task is to perform topic modelling based on Wikipedia data. You can use the seminar materials as reference and apply any topic modelling models available in PySpark.
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