BEAST & The GPU Cluster - Computerphile | Summary and Q&A

November 28, 2018
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BEAST & The GPU Cluster - Computerphile


Researchers at a school have developed a multi-generational GPU computing cluster named Beast and its siblings to meet the growing demand for computing power in deep learning research. They have implemented a centralized storage system using ZFS and achieved stable performance through various configuration adjustments.

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Key Insights

  • ✊ GPU computing clusters are essential in deep learning research, providing the necessary computing power to train complex neural networks.
  • ♿ Centralized storage systems can simplify data management in GPU clusters by eliminating data duplication and streamlining access.
  • 🧚 Scheduling systems like Slurm help allocate resources efficiently, ensuring fair usage and maximizing cluster utilization.
  • ✋ The ZFS file system offers advanced features such as caching and data protection, making it suitable for high-performance computing environments.
  • ❓ Configuration adjustments are necessary to overcome challenges and ensure stable performance in GPU computing clusters.
  • 💦 Collaboration and shared resources in GPU clusters increase efficiency and enable researchers to work on complex deep learning projects.
  • 😮 The demand for computing power in deep learning research continues to rise, leading to the development of multi-generational GPU clusters.


there's normally an alarm turned on here but things have been in and out so much left so down here are all the cvl gpu well it's quite noisy in here should we go somewhere else yes joe tell me what it is you've been doing on this particular project so i'm a technician here my official job title is it systems engineer but i do a lot of different thi... Read More

Questions & Answers

Q: How did the researchers come up with the names for the GPU computing machines?

The names for the machines, such as Beast and Rogue, were chosen through voting or by selecting the shortest names from the X-Men character list on Wikipedia.

Q: Why was it necessary to centralize the storage in the GPU computing cluster?

Centralized storage eliminates the need to duplicate data across multiple machines and ensures that researchers can access their data quickly and efficiently, without worrying about which machine to use.

Q: How does the ZFS file system handle caching for efficient data access?

ZFS automatically handles caching using an adaptive replacement cache, which prioritizes recently accessed and frequently accessed data. This caching mechanism improves performance by reducing the need to access slower platter-based storage.

Q: What is the process for submitting jobs and accessing the GPU computing cluster?

Users submit their jobs through the command line using the "sbatch" command and specify requirements such as the number of GPUs, memory, and CPU cores. The system automatically assigns the job to the appropriate compute node based on availability and requirements.

Summary & Key Takeaways

  • The school's IT systems engineer, Joe, is responsible for maintaining the GPU computing cluster and networking infrastructure for deep learning research.

  • The cluster consists of multiple generations of machines named Beast and Rogue, with increased demand for computing power due to the popularity of deep learning in research.

  • To efficiently manage the cluster, a scheduling system called Slurm is used, and centralized storage using ZFS has been implemented to eliminate data duplication and facilitate training processes.

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