Description
Problem Description
Your task in this programming assignment is to implement a simple, parallelized application leveraging the
University of Melbourne HPC facility SPARTAN. Your application will search a large geocoded Twitter dataset to
identify Twitter usage around Melbourne and the most frequently occurring hashtags that are being sent from those
areas.
You should be able to log in to SPARTAN through running the following command:
ssh your-unimelb-username@spartan.hpc.unimelb.edu.au
with your University password. Thus I would log in as:
ssh rsinnott@spartan.hpc.unimelb.edu.au
If you are a Windows user then you may need to install an application like Putty.exe to run ssh.
The files to be used in this assignment are accessible at:
• /data/projects/COMP90024/bigTwitter.json
o this is the main 11Gb JSON file to use for your final analysis and report write up, i.e. do not use
the bigTwitter.json file for software development and testing.
• /data/projects/COMP90024/smallTwitter.json
o smallTwitter.json this a 25Mb JSON file should be used for testing;
• /data/projects/COMP90024/tinyTwitter.json
o tinyTwitter.json this a 3.6Mb JSON file should be used for testing
o You may also decide to use the smaller JSON files on your own PC/laptop to start with.
• /data/projects/COMP90024/melbGrid
o this is a small JSON-based Grid file for Melbourne.
You should make a symbolic link to these files, i.e. you should run the following commands at the Unix prompt from
your own user directory on SPARTAN:
ln –s /data/projects/COMP90024/bigTwitter.json
ln –s /data/projects/COMP90024/smallTwitter.json
ln –s /data/projects/COMP90024/tinyTwitter.json
ln –s /data/projects/COMP90024/melbGrid.json
Once done you should see something like the following in your home directory:
lrwxrwxrwx 1 rsinnott unimelb 40 Mar 22 15:06 bigTwitter.json -> /data/projects/COMP90024/bigTwitter.json
lrwxrwxrwx 1 rsinnott unimelb 39 Mar 22 15:06 smallTwitter.json -> /data/projects/COMP90024/smallTwitter.json
lrwxrwxrwx 1 rsinnott unimelb 38 Mar 22 15:06 tinyTwitter.json -> /data/projects/COMP90024/tinyTwitter.json
lrwxrwxrwx 1 rsinnott unimelb 41 Mar 22 15:06 melbGrid.json -> /data/projects/COMP90024/melbGrid.json
The melbGrid.json file includes the latitudes and longitudes of a range of gridded boxes as illustrated in the figure
below, i.e. the latitude and longitude of each of the corners of the boxes is given in the file.
Your assignment is to (eventually!) search the large Twitter data set (bigTwitter.json) to identify Twitter activity
around Melbourne and the most frequently used hashtags in each of the grid cells (A1…D5). Specifically, you
should:
• Order (rank) the Grid boxes based on the total number of Twitter posts made in each box and return the total
count of posts in each box, e.g.
o C3: 23,456 posts,
o B2: 22,345 posts,
o D1: 21,234 posts,
o …
o Down to the square with the least number of posts;
• Order (rank) the top 5 hashtags in each Grid cells based on the number of occurrences of those hashtags, e.g.
o C3: ((#maccas, 123),(#qanda, 121),(#trump,100),(#unimelb,66),(#dominos,41))
o B2: ((#maccas, 82),(#vicroads,81), etc etc….)
o …
o Down to the top 5 hashtags in the grid cell with the least number of posts;
(Obviously these numbers and hashtags are representative of the contents of each grid cell!)
An individual post can be considered to occur in the box if its geo-location information (the post latitude and
longitude given by the post coordinates) is within the box identified by the set of coordinates in melbGrid.json. It
should be noted that the file bigTwitter.json includes many posts that do not have geocodes or they are not in this
grid, e.g. they are from other parts of Victoria. You should filter/remove these posts since only the posts in the grid
boxes identified here are of interest. If a tweet occurs exactly on the boundary of two cells in the same row, e.g. A1
and A2 then assume that the tweet occurs in the left box (A1). If the boundary the tweet occurs exactly on the
boundary between two cells in the same column, e.g. A1 and B1, then assume that the tweet occurs in the higher box
(A1).
Your application should allow a given number of nodes and cores to be utilized. Specifically, your application
should be run once to search the bigTwitter.json file on each of the following resources:
• 1 node and 1 core;
• 1 node and 8 cores;
• 2 nodes and 8 cores (with 4 cores per node).
The resources should be set when submitting the search application with the appropriate SLURM options. Note that
you should run a single SLURM job three separate times on each of the resources given here, i.e. you should not need
to run the same job 3 times on 1 node 1 core for example to benchmark the application. (This is a shared facility and
this many COMP90024 students will consume a lot of resources!).
A1 A2 A3 A4
B1 B2 B3 B4
C1 C2 C3 C4 C5
D3 D4 D5
You can implement your search using any routines that you wish from existing libraries however it is strongly
recommended that you follow the guidelines provided on access and use of the SPARTAN cluster. Do not for
example think that the job scheduler/SPARTAN automatically parallelizes your code – it doesn’t! You may wish to
use the pre-existing MPI libraries that have been installed for C, C++ or Python. You should feel free to make use of
the Internet to identify which JSON processing libraries you might use.
Your application should return the final results and the time to run the job itself, i.e. the time for the first job starting
on a given SPARTAN node to the time the last job completes. You may ignore the queuing time. The focus of this
assignment is not to optimize the application to run faster, but to learn about HPC and how basic benchmarking of
applications on a HPC facility can be achieved and the lessons learned in doing this on a shared resource.
Final packaging and delivery
You should write a brief report on the application – no more than 4 pages!, outlining how it can be invoked, i.e. it
should include the scripts used for submitting the job to SPARTAN, the approach you took to parallelize your code,
and describe variations in its performance on different numbers of nodes and cores. Your report should also include a
single graph (e.g. a bar chart) showing the time for execution of your solution on 1 node with 1 core, on 1 node with
8 cores and on 2 nodes with 8 cores.
Deadline
The assignment should be submitted to the lecturer via LMS as a zip file. The zip file must be named with the first
named student in each team (as listed in googlesheets). This includes Forename-Surname-Student ID, e.g. <JoeSmith-0123456>.zip. Only one report is required per student pair.
The deadline for submitting the assignment is: Monday 15th April (by 12 noon!).
It is strongly recommended that you do not do this assignment at the last minute, as it may be the case that the
Spartan HPC facility is under heavy load when you need it and hence it may not be available! You have been
warned…!!!!
Marking
The marking process will be structured by evaluating whether the assignment (application + report) is compliant with
the specification given. This implies the following:
• A working demonstration – 60% marks
• Report and write up discussion – 40% marks
Timeliness in submitting the assignment in the proper format is important. A 10% deduction per day will be made
for late submissions.
You are free to develop your system where you are more comfortable with (at home, on your PC/laptop, in the labs,
on SPARTAN itself – but not on the bigTwitter.json file until you are ready!). Your code should of course work on
SPARTAN.
