750 points by mlwhiz 6 months ago flag hide 22 comments
gnarlyhacker 6 months ago next
Great post! I've been working with large datasets lately and k-means clustering has been a lifesaver. Any tips on how to parallelize the process?
techlead 6 months ago next
Definitely! I recommend using a GPU-accelerated implementation of k-means. It significantly speeds up the clustering process for large datasets.
quantprogrammer 6 months ago prev next
I've tried using a GPU-accelerated k-means algorithm on a K80 Tesla and it was night and day compared to a regular CPU-based implementation.
neuralnetexpert 6 months ago prev next
GPU-accelerated k-means is definitely the way to go for large datasets. Here are some libraries to check out: cuML, NVIDIA RAPIDS, and PyTorchCluster.
deeplearningdude 6 months ago next
Thanks for the recommendations! I've heard good things about cuML. How does it compare to traditional scikit-learn for k-means?
gonumwiz 6 months ago next
cuML is built on top of scikit-learn, so it has the same API. However, it's optimized for GPUs. This means that you can perform k-means clustering on larger datasets much faster.
algoenthusiast 6 months ago prev next
What about initializing the centroids in k-means? I'm wondering if there's a GPU-accelerated way of doing this.
cythoncoder 6 months ago next
Yes, there is! You can use a GPU-accelerated random number generator to initialize the centroids. Here's a link to a library that has an implementation: [https://github.com/ddemidov/cuRandXY](https://github.com/ddemidov/cuRandXY)
googlegolfer 6 months ago prev next
I'm curious if anyone has experience running GPU-accelerated k-means on a cloud GPU instance. Is it worth it?
awswhiz 6 months ago next
Yes, I've tried it on AWS and it's definitely worth it when dealing with really large datasets. It can be expensive, but the speed improvement is significant.
azureace 6 months ago prev next
I've also tried using Azure's GPU instances for k-means and it saved me a lot of time. Make sure to choose a GPU that's optimized for MLA.
mlnerd 6 months ago prev next
One thing to keep in mind when using GPU-accelerated k-means is that you'll need to transfer the data to the GPU. This can take some time, so make sure to use an efficient transfer method.
bigdatacoordinator 6 months ago prev next
@mlnerd thanks for the tip. Do you have any recommendations for efficient data transfer libraries that are GPU-compatible?
gpucoder 6 months ago next
@bigdatacoordinator Yes, I recommend checking out CuPy for efficient data transfer between the CPU and GPU. Here's a link: [https://cupy.dev/](https://cupy.dev/)
mladventurer 6 months ago prev next
Another thing to consider is the number of iterations needed for k-means convergence. GPU-acceleration can help speed up the process, but it still depends on the quality of the data and the number of iterations needed.
parallelprof 6 months ago next
@mladventurer That's a great point. You can also consider using mini-batch k-means, which updates the centroids with a subset of the data at each iteration. This can help reduce the number of iterations needed for convergence.
numbaprogrammer 6 months ago prev next
An alternative to GPU-accelerated k-means is using a distributed system with multiple CPUs. This may not be as fast as using a GPU, but it can still handle really large datasets.
memorymeister 6 months ago next
Yes, distributed k-means is definitely worth considering. You can use libraries like Dask or Spark to parallelize the process across multiple CPUs. However, keep in mind that distributed k-means requires more memory and data transfer between nodes.
bigdatalearner 6 months ago prev next
GPU-accelerated k-means is a powerful tool, but it's important to consider your use case. If you're working with really large datasets that need frequent clustering, it may be worth investing in a GPU. Otherwise, the cost may not outweigh the benefits.
fpgapoweruser 6 months ago prev next
Just a thought - what about using FPGAs for accelerating k-means clustering? It's an alternative to GPUs and might be worth considering for some use cases.
hpcnut 6 months ago next
FPGAs are indeed a compelling option, especially for high-frequency operations. However, they can be challenging to program and may not have the same level of support for machine learning libraries as CPUs and GPUs.
datamaven 6 months ago prev next
Awesome discussion! I'm definitely intrigued by the potential of GPU-accelerated k-means and the different libraries and tools available. Thanks for sharing your experiences and tips!