Closed grantmcdermott closed 9 months ago
That is why test scripts include call to setDTthreads() at start to male it consistent
Hmm, this is a good point. Globally searching for setDTthreads()
shows that the function is only called in datatable runs.
Many other R solutions use ncores = parallel::detectCores()
.
Still working on publishing the results, it will most likely come with a blog post explaining the delay (and other updates).
As a preview, we will move to a click-bench model where anyone can request the results be updated if they can show faster execution times (with different settings/new solution version) on the same machine. After verification, the results will be updated. This means that if R-collapse or data.table used an incorrect number of threads, the benchmark can be updated within a week.
setDTthreads is only used in datatable runs because it controls number of threads only for datatable.
setDTthreads is only used in datatable runs because it controls number of threads only for datatable.
It's also used in the proposed r-collapse run. At least, getDTthteads is, without the corresponding/corrective set call earlier afaict. I'll flag in that PR and then close this issue.
Probably a question for @jangorecki, since this applies to data.table's thread retrieval function. But it will effect other R implementations relying on
getDThreads
, e.g. the newly proposed r-collapse PR (#33).Here's an example from my own EC2 server.
First, basic machine characteristics.
The Python call gets the 96 CPU number right.
But data.table only recognises half of the available cores (physical only?).