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path: root/src/benchmarks/fd.py
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* make the whole project more python idiomaticFlorian Fischer2020-06-021-93/+0
| | | | | | | | | * rename src directory to allocbench * make global variable names UPPERCASE * format a lot of code using yapf * use lowercase ld_preload and ld_library_path as Allocator members * name expected Errors 'err' and don't raise a new Exception * disable some pylint messages
* unify plotting codeFlorian Fischer2020-04-071-10/+16
| | | | | Now there is only a single plot function which takes a plot type as well as some plot and figure options.
* improve matplotlib codeFlorian Fischer2020-03-091-2/+2
| | | | | | add new helper functions to get all y_values for a plot use seperate figures for each plot s/filepostfix/file_postfix/ to be consistent
* cleanup plot codeFlorian Fischer2020-02-111-4/+4
| | | | | | | * use str.format instead of eval for plot labels * use meaningful variable names * add license header * add some doc strings
* move plotting code from src.benchmark to src.plotsFlorian Fischer2020-02-111-10/+13
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* remove unused importsFlorian Fischer2019-12-131-2/+0
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* fmt benchmark definitions with yapfFlorian Fischer2019-12-111-8/+9
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* fix fd.prepare()Florian Fischer2019-11-221-3/+4
| | | | always call provide of the linux sources to know where they were provided to
* keep not modified artifact data in cacheFlorian Fischer2019-11-211-2/+2
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* fix y-label for fd plotsFlorian Fischer2019-10-211-1/+1
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* don't prepare fd if it was already preparedFlorian Fischer2019-09-231-0/+3
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* use ArchiveArtifacts for all benchmark resourcesFlorian Fischer2019-09-231-41/+20
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* add artifactsFlorian Fischer2019-09-211-9/+7
| | | | | | | | | Artifacts are downloadable external ressources. They provide their ressource (vcs commit, extracted archive) using Artifact.provide. GitArtifact download a bare git repository and provides specific commits using worktrees. Use GitArtifact for the linux kernel source
* add fd benchmarkFlorian Fischer2019-09-191-0/+104
benchmark inspired by this blog post. https://dev.to/sharkdp/an-unexpected-performance-regression-11ai