AWS doesn't make sense for scientific computing

• Hail

  5 934 9.8 Python

  Cloud-native genomic dataframes and batch computing

  

I think this post is identifying scientific computing with simulation studies and legacy workflows, to a fault. Scientific computing includes those things, but it also includes interactive analysis of very large datasets as well as workflows designed around cloud computing.

Interactive analysis of large datasets (e.g. genome & exome sequencing studies with 100s of 1000s of samples) is well suited to low-latency, server-less, & horizontally scalable systems (like Dremel/BigQuery, or Hail [1], which we build and is inspired by Dremel, among other systems). The load profile is unpredictable because after a scientist runs an analysis they need an unpredictable amount of time to think about their next step.

As for productionized workflows, if we redesign the tools used within these workflows to directly read and write data to cloud storage as well as to tolerate VM-preemption, then we can exploit the ~1/5 cost of preemptible/spot instances.

One last point: for the subset of scientific computing I highlighted above, speed is key. I want the scientist to stay in a flow state, receiving feedback from their experiments as fast as possible, ideally within 300 ms. The only way to achieve that on huge datasets is through rapid and substantial scale-out followed by equally rapid and substantial scale-in (to control cost).

[1] https://hail.is

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