[libc] update benchmark distributions

All distributions (expect D) have been updated using 7 days worth of data.
Distributions are smoother.
This patch also moves data from header file to individual csv file. It
helps the editor and allows easier export/plotting of the data.

Differential Revision: https://reviews.llvm.org/D105766
This commit is contained in:
Guillaume Chatelet 2021-07-13 09:59:11 +00:00
parent d991b7212b
commit 8724a7ec11
30 changed files with 176 additions and 44 deletions

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0,0.00315457,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.675079,0,0.116719,0.00315457,0,0,0.00630915,0.00315457,0,0,0.00315457,0,0,0,0.0126183,0,0,0,0,0,0,0,0,0.00315457,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.022082,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.00315457,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0378549,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0126183,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0157729,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0315457,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.00315457,0.00315457,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.00315457,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.00315457,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.022082,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.00315457,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0126183
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# Size distributions for memory functions under specific workloads
This folder contains a set of files that are included from `libc/benchmarks/MemorySizeDistributions.cpp`.
Offloading this data to individual files helps
- C++ editors (large arrays are usually not well handled by editors),
- and allows processing data by other tools to perform analysis or graph rendering.
## Format
Most filenames are of the form `{MemoryFunctionName}{WorkloadID}.csv`. They contain a single line of comma separated real values representing the probability that a particular size occurs. e.g.
- `"0,1"` indicates that only the size `1` occurs,
- `"0.5,0.5"` indicates sizes `0` and `1` occur with the same frequency.
These files usually contains sizes from `0` to `4096` inclusive. To save on space trailing zeros are discarded.
## Workloads
As identified in the [automemcpy](https://research.google/pubs/pub50338/) paper:
- `GoogleA` <-> `service 4`
- `GoogleB` <-> `database 1`
- `GoogleD` <-> `storage`
- `GoogleL` <-> `logging`
- `GoogleM` <-> `service 2`
- `GoogleQ` <-> `database 2`
- `GoogleS` <-> `database 3`
- `GoogleU` <-> `service 3`
- `GoogleW` <-> `service 1`
`Uniform384To4096` is an additional synthetic workload that simply returns a uniform repartition of the sizes from `384` to `4096` inclusive.
## Note
Except for `GoogleD`, all distributions are gathered over one week worth of data.

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