xxHash VS Pytorch

> GPU Hash Table?

How bad would performance have suffered if you sha256'd the lines to build the map? I'm going to guess "badly"?

Maybe something like this in CUDA: https://github.com/Cyan4973/xxHash ?

Examples of fast, simple hashes that are independent enough includes murmur, xxHash, Fowler–Noll–Vo hash function and many others

If you're only using the hash for non-cryptographic applications, there are much faster hashes: https://github.com/Cyan4973/xxHash

I found the benchmark result of AMD ryzen 5950X

But what's the catch, you're thinking ? Well, it is a bit slower than its counterparts when it comes to deserializing (and marginally faster for serializing). To achieve smaller footprint, it uses a few tricks and notably a custom hash table to deduplicate strings. This comes at a cost of course (even when featuring xxHash to speed things up), but keeps the slowdown reasonable (I think).

Non cryptographic hashes has collisions, for example, assume you having content like "abcdefg" which hashed value is "123", in case of weak hash algorithm some other content like "abcdefZ" can also have a hash "123" which basically means such hash function is failed to be unique fingerprint of particular content. BLAKE3 for example can do 6-7Gb/s which make it pretty fast and secure. If your requirement accepts collision with defined error rate, I would advise you to take a look at XXH3 if you need very snappy hash algorithm, which can run at pace or RAM access (30GB/s+), but again, run tests at particular equipment you targeting, may be AES hardware accelerated MeowHash will serve you better.

PyTorch (https://pytorch.org/)

torch is a package to manage tensors and dynamic neural networks in python (GitHub)

PyTorch also has some support for them, but it's quite incomplete and has many issues so that it is basically unusable. And its future development is also unclear. https://github.com/pytorch/pytorch/issues/60832

TensorFlow

TensorFlow, developed by Google, and PyTorch, developed by Facebook, are two of the most popular frameworks for building and training complex machine learning models. TensorFlow is known for its flexibility and robust scalability, making it suitable for both research prototypes and production deployments. PyTorch is praised for its ease of use, simplicity, and dynamic computational graph that allows for more intuitive coding of complex AI models. Both frameworks support a wide range of AI models, from simple linear regression to complex deep neural networks.

> does PyTorch have a similar concept

of course https://github.com/pytorch/pytorch/blob/main/torch/utils/_py...

fyi should work on most 40xx[1]

[1] https://github.com/pytorch/pytorch/issues/119638#issuecommen...

Sure, right here: https://github.com/pytorch/pytorch/blob/main/torch/autograd/...

Here's the documentation: https://pytorch.org/tutorials/intermediate/forward_ad_usage....

> When an input, which we call “primal”, is associated with a “direction” tensor, which we call “tangent”, the resultant new tensor object is called a “dual tensor” for its connection to dual numbers[0].

*My post explains Dot, Matrix and Element-wise multiplication in PyTorch.

In PyTorch with @, dot() or matmul():