package bechamel

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Yet Another Benchmark in OCaml

Install 

dune-project
 Dependency

github.com Readme Changelog MIT License Edit opam file Versions (5)

Authors

  1. R
    Romain Calascibetta <romain.calascibetta@gmail.com>

Maintainers

  1. R
    Romain Calascibetta <romain.calascibetta@gmail.com>

Sources

bechamel-0.3.0.tbz
sha256=d719040841a1a3be6f93699ae9bf1f8cb2c5d294f0218c0bc0a735386c2d71a0
sha512=dc1233d4dcf01a997a3fcbafc116df0aae22ea5a6c98c09e200e4aa984c558976c8290b3e14b1156519ad12a6cc4b1b9fa4adf3dc2458d373d77a07fb9f7acff

doc/bechamel/Bechamel/Analyze/index.html

Module Bechamel.AnalyzeSource

Analyze module.

Micro-benchmark usually uses a linear-regression to estimates the execution time of a code segments. For example, the following table might represent {!Measurement_raw.t} array collected by Benchmark.run:

+-----+------+
| run | time |
+-----+------+
| 1   | 19   |
| 2   | 25   |
| 3   | 37   |
| 4   | 47   |
| 5   | 56   |
+-----+------+

Bechamel records 3000 samples and the number of iterations can grows geometrically (see Benchmark.run). Then, Bechamel can use 2 algorithms:

  • Ordinary Least Square
  • RANdom SAmple Consensus

The user can choose one of it. Currently, OLS is the best to use. These algorithms will estimate the actual execution time of the code segment. Using OLS with the above data would yield an estimated execution time of 9.6 nanoseconds with a goodness of fit (r²) of 0.992.

More generally, Bechamel lets the user choose the predictors and responder. Indeed, the user can use others metrics (such as perf) and the API allows to analyze such metrics together.

Sourcemodule OLS : sig ... end
Sourcemodule RANSAC : sig ... end
Sourcetype 'a t

Type of analysis.

Sourceval ols : r_square:bool -> bootstrap:int -> predictors:string array -> OLS.t t

ols ~r_square ~bootstrap ~predictors is an Ordinary Least Square analysis on predictors. It calculates r² if r_square = true. bootstrap defines how many times Bechamel tries to resample measurements.

Sourceval ransac : filter_outliers:bool -> predictor:string -> RANSAC.t t
Sourceval one : 'a t -> Measure.witness -> Benchmark.t -> 'a

one analysis measure { Benchmark.stat; lr; kde; } estimates the actual given measure for one predictor. So, one analysis time { Benchmark.stat; lr; kde; } wants to estimate actual run-time (or execution time) value, where analysis is initialized with run predictor.

Sourceval all : 'a t -> Measure.witness -> (string, Benchmark.t) Hashtbl.t -> (string, 'a) Hashtbl.t

all analysis measure tbl is an application of one for all results from the given tbl.

Sourceval merge : 'a t -> Measure.witness list -> (string, 'a) Hashtbl.t list -> (string, (string, 'a) Hashtbl.t) Hashtbl.t

merge witnesses tbls returns a dictionary where the key is the label of a measure (from the given witnesses) and the value is the result of this specific measure.