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Exploring new parallel algorithms for training large neural networks(ai-research.org)

80 points by ai_researcher 1 year ago | flag | hide | 18 comments

  • deeplearningfan 1 year ago | next

    This is such an interesting topic! I've been exploring parallel algorithms for training large neural networks lately, and I can't wait to see what new approaches people have come up with!

    • hpcguy 1 year ago | next

      I agree! I've been working on a project where we're exploring data parallelism and model parallelism, and we're seeing some promising results.

    • parallelenthusiast 1 year ago | prev | next

      Have any of you seen any interesting research on tensor parallelism? I feel like that approach could be really powerful for training huge neural networks.

      • deeplearningfan 1 year ago | next

        That sounds fascinating! Could you share a link to the paper?

      • hpcguy 1 year ago | prev | next

        I'm curious, have any of you experimented with GPU-accelerated interconnects, like NVLink or InfiniBand, to improve communication bandwidth between nodes? I'm wondering if they could make a difference in parallelizing large neural network training.

        • parallelenthusiast 1 year ago | next

          I haven't tried that specific configuration, but I have seen some experiments using NVLink and similar technologies. They seem to provide a noticeable improvement in communication bandwidth, but I think the real key is to optimize your algorithms to minimize the amount of communication needed.

          • deeplearningfan 1 year ago | next

            Another important factor to consider is reducing the amount of synchronization necessary during training. As we all know, synchronization can be a major bottleneck in parallel computations.

            • parallelenthusiast 1 year ago | next

              I'm interested in learning more about that. Do you have any resources you could recommend for getting started with asynchronous SGD and similar optimization algorithms?

              • deeplearningfan 1 year ago | next

                Another book that I would recommend is 'Paralle...'

                • tensorwiz 1 year ago | next

                  That's an excellent suggestion, thank you! I'll update my implementation to use AdamW and see if it improves the convergence rate.

        • tensorwiz 1 year ago | prev | next

          I agree with ParallelEnthusiast. It's important to optimize your algorithms for the parallel architecture you're using, otherwise you won't see the full benefits of the hardware.

          • hpcguy 1 year ago | next

            Absolutely. We've been experimenting with asynchronous SGD and various other optimization algorithms to reduce the need for synchronization without sacrificing convergence.

            • tensorwiz 1 year ago | next

              There are some great resources out there on this topic. One that I would recommend is the book 'Large-scale Machine Learning' by Peter Pawlowski, Martin P.W. Zinkevich, and Michael I. Jordan. It covers various optimization algorithms in the context of large-scale machine learning, and many of the techniques can be applied to parallel neural network training as well.

              • parallelenthusiast 1 year ago | next

                Thank you for the recommendation! I'll definitely check out that book.

  • tensorwiz 1 year ago | prev | next

    Absolutely! Just last week I was reading a paper on a new algorithm for tensor parallelism that leverages mixed-precision arithmetic to improve training time.

    • tensorwiz 1 year ago | next

      Yes, of course! Here it is: [link to paper]