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Team Render Benders

The importance of rendering three-dimensional objects cannot be understated. Displaying an animated version of a dataset can open a door to the public to better comprehend the research being done, and data visualization has often been the difference between research teams which have received funding and which ones have not. Our team has chosen to direct our efforts towards rendering carbon nanotubes, which have been highlighted as a nanomaterial structure due to their high tensile strength and flexibility. Furthermore, in recent years, there have been multiple research advances highlighting the use of oriented carbon nanotubes for water filtration and oil adsorption from water. This is where Team Render Benders comes into action. Our team aims to provide accurate renders of the carbon nanotubes, first of one, and then of an orientation similar to those within the research advances mentioned above.


The data file which our team is looking at is linked in the website here:

Download and Installation

To begin using this template, choose one of the following options to get started:

Use CloudyCluster in the Project

CloudyCluster supports the dynamic provisioning and de-provisioning of HPC environment within commercial clouds. The provisioned HPC cluster can include shared filesystems, NAT instance, compute nodes, parallel filesystem, login node, and schedulers, and can solve storage issues for Big Data and Data Intensive applications. To improve the scalability of our RenderBender system, we deployed our system in CloudyCluster. Below is the overview of RenderBender.

Fig. 1: The overview of RenderBender

Figure 1 shows the overview of RenderBender. The Utility layer shows the control node, login instance, scheduler instance and the GCSBucket (Google Cloud Storage bucket). The orangefs layer shows the OrangeFS instance. Torque and Slurm are job schedulers used to start, hold, monitor and cancel jobs submitted via the CloudyCluster interface. CloudyCluster Queue (CCQ) is a meta-scheduler provided with CloudyCluster that handles the instance selection and scaling the passes off the jobs to the configured scheduler (e.g., Torque or Slurm). A script is a set of instructions that specify how a job should be executed. RenderBender uses the script (e.g., Torque script, Slurm script) to specify how a job should be executed (including the number of requested nodes, the number of tasks on each node, etc.).

Fig. 2: An example of the script for specifying how a job should be executed

Figure 2 shows one of the configurations (an example of the script) for specifying how a job should be executed. RenderBender uses Torque/Maui HPC Scheduler and requests two nodes from the system (each node is assigned two tasks) by uncommenting “##PBS -l nodes=2:ppn=2”; RenderBender uses Slurm HPC Scheduler and requests two nodes from the system (each node is assigned two tasks) by uncommenting “##SBATCH -N 2” and “##SBATCH --ntasks-per-node=2”. RenderBender uses the openMPI by uncommenting “#module add openmpi/3.0.0”.

In RenderBender, when users submit their jobs (e.g., animating the render), the jobs are delivered to the scheduler. The scheduler will split the jobs into tasks and distribute the tasks to the requested nodes from the system. Then the nodes run the tasks assigned to them and generate the result. The result will be saved in the location specified in the above script. Figure 3 shows the framework of job scheduling in RenderBender.

Fig. 3: The framework of job scheduling in RenderBender

Experimental Results

Fig. 4: Carbon nanotubes

Fig. 5: Carbon nanotube

Fig. 6: Render the Blender files

Fig. 7: Render the Blender files

Fig. 8: Render the Blender files

Fig. 9: Render the Blender files

Here is our final rendering: Final_Render_v=Cs8UVRE7dgE.mp4 or see it at

Research Findings

The articles depicting the different research findings are linked here:,

We used Blender to design and render the carbon nanotubes. The rendering was done on Cloudycluster with a 16 CPU virtual machine.


This is a GitHub repository to display our renders and code for the Render Benders' Hackathon project.






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