what is a typical workload that you speak of, where CPUs are better?
We've been implementing GPU support in Presto/Velox for analytical workloads and I'm yet to see a use case where we wouldn't pull ahead.
The DRAM-VRAM memory bottleneck isn't really a bottleneck on GH/GB platforms (you can pull 400+GB/s across the C2C NVLink), and on NVL8 systems like the typical A100/H100 deployments out there, doing real workloads, where the data is coming over the network links, you're toast without using GPUDirect RDMA.
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.
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.
By typical I imagined adoption within commonly-deployed TSDBs like Prometheus, InfluxDB, etc.
GB/GH are actually ideal targets for my code: both architectures integrate Neoverse V2 cores, the same core I developed for. They are superchips with 144/72 CPU cores respectively.
The perf numbers I shared are for one core, so multiply the numbers I gave by 144/72 to get expected throughput on GB/GH. As you (apparently?) have access to this hardware I'd sincerely appreciate if you could benchmark my code there and share the results.
We've been implementing GPU support in Presto/Velox for analytical workloads and I'm yet to see a use case where we wouldn't pull ahead.
The DRAM-VRAM memory bottleneck isn't really a bottleneck on GH/GB platforms (you can pull 400+GB/s across the C2C NVLink), and on NVL8 systems like the typical A100/H100 deployments out there, doing real workloads, where the data is coming over the network links, you're toast without using GPUDirect RDMA.