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Run times may not be comparable between tools/runs if we don't ensure that the underlying computational conditions were comparable. For that, the evaluation would probably have to be executed on a dedicated server with a task having exclusive access to that machine and input/output files being placed on local storage (e.g. using nextflow's scratch true
).
- Cluster execution could be acceptable if we can ensure
- homogeneity of the nodes (explicit partition spec?)
- exclusive use of nodes
- use of local scratch space
- Cloud (
awsbatch
) execution could be acceptable if we can ensure
- homogeneity of the nodes
- exclusive use of nodes
- use of local scratch space
In addition, we must capture more of the task information via
trace.fields = 'task_id,name,status,exit,realtime,%cpu,rss'
- which should include requested resources
cpus,memory,time
- more here
The cpu details can be easily picked-up in the mapping process
e.g. beforeScript 'cat /proc/cpuinfo > cpuinfo'
which can be parsed downstream.
It is of limited value on its own for serious speed benchmarking,
but may be useful for the indicative use of speed in reports.