workload

That being said, it really depends on the problem at hand and the overall workload of the database.

Because of the way flash memory is organized into pages and blocks, latency is very workload specific.

For a purely graphics workload the CPU will be doing relatively small amounts of work.

Making that process unavailable to copyright holders outside the US would make those protections unavailable, increasing the workload for these businesses.

Give that GPU a highly serialized workload and watch the actual performance take a nosedive.

This article is referring to this type of workload since this is Spark's bread/butter.

No one can run that workload as a volunteer for a decade+.

Adjusting the autovacuum settings by table will give you finer control over the postgresql.conf parameters to better match the workload for specific tables, as well.

For those unaware - this will improve workload handling even further.

That said, it is a pretty common FPU workload.

You can run a MBP with the lid closed, but it will get pretty toasty, and these aren't being used for a typical laptop workload one presumes.

Frankly, they're a rounding error with our current workload.

Depends on what you mean by a "real" workload.

That's not "prove to me you're 10x!", but my work ethic coming out and keeping me on task until my expected workload was actually complete.

But I can see how the default reality probably bends towards "Well, someone left and we need this job filled" -> "I'll just temporarily add that job to my workload" -> "It'd be more effort to move that job's responsibility to someone else, so I'll just keep doing it."