Question for someone with gpu programming experience: is it feasible to analyze time series data (eg.: run a trading algorithm through a series of stock prices) in parallel (with the algorithms receiving different parameters in each core)?

If not then what is the limiting factor?

This depends on the algorithm. If it has lots of branching and control flow, then it's unlikely to run well. If it's based on something like FFT or any sort of linear algebra really, then it should run really well.

WebGPU is accessible enough maybe you should try it! You'll learn a lot either way, and it can be fun.

Since you mentioned FFT, I wondered whether GPU-based computation would be useful for audio DSP. But then, lots of audio DSP operations run perfectly well even on a low-end CPU. Also, as one tries to reduce latency, the buffer size decreases, thus reducing the potential to exploit parallelism, and I'm guessing that going to the GPU and back adds latency. Still, I guess GPU-based audio DSP could be useful for batch processing.

Yes. Probably the single most effective use of the GPU for audio is convolutional reverb, for which a number of plugins exist. However, the problem is that GPUs are optimized for throughput rather than latency, and it gets worse when multiple applications (including the display compositor) are contending for the same GPU resource - it's not uncommon for dispatches to have to wait multiple milliseconds just to be scheduled.

I think there's potential for interesting things in the future. There's nothing inherently preventing a more latency-optimized GPU implementation, and I personally would love to see that for a number of reasons. That would unlock vastly more computational power for audio applications.

There's also machine learning, of course. You'll definitely be seeing (and hearing) more of that in the future.