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Even without NVLink C2C, on a GPU with 16XPCIe 5.0 lanes to host, you have 128GB/sec in theory and 100+ GB/sec in practice bidirectional bandwidth (half that in each direction), so still come out ahead with pipelining.

Of course prefix sums are often used within a series of other operators, so if these are already computed on GPU, you come out further ahead still.

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Haha... GPUs are great. But do you mean to suggest we should swap a single ARM core for a top-line GPU with 10k+ cores and compare numbers on that basis? Surely not.

Let's consider this in terms of throughput-per-$ so we have a fungible measurement unit. I think we're all agreed that this problem's bottleneck is the host memory<->compute bus so the question is: for $1 which server architecture lets you pump more data from memory to a compute core?

It looks like you can get a H100 GPU with 16xPCIe 5.0 (128 GB/s theoretical, 100 GB/s realistic) for $1.99/hr from RunPod.

With an m8g.8xlarge instance (32 ARM CPU cores) you should get much-better RAM<->CPU throughput (175 GB/s realistic) for $1.44/hr from AWS.

GH200 is $1.5/hr at lambda and can do 450GB/s to the GPU. seems still cheaper?

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