package bechamel

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bechamel
- Bechamel
  - Analyze
    
    OLS
    
    RANSAC
  - Benchmark
  - Linear_algebra
  - Measure
  - Measurement_raw
  - S
    
    FUNCTOR
    
    MEASURE
  - Staged
  - Test
    
    Elt
    
    Multiple
    
    Uniq
  - Time
  - Toolkit
    
    Compaction
    
    Extension
    
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    Major_allocated
    
    Major_collection
    
    Minor_allocated
    
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    Monotonic_clock
    
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bechamel.monotonic_clock
- Monotonic_clock

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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.

module OLS : sig ... end

module RANSAC : sig ... end

type 'a t

Type of analysis.

val 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.

val ransac : filter_outliers:bool -> predictor:string -> RANSAC.t t

val 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.

val 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.

val 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.