package bechamel

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bechamel
- Bechamel
  - Analyze
    
    OLS
    
    RANSAC
  - Benchmark
  - Linear_algebra
  - Measure
  - Measurement_raw
  - S
    
    FUNCTOR
    
    MEASURE
  - Staged
  - Test
    
    Elt
  - Time
  - Toolkit
    
    Compaction
    
    Extension
    
    Instance
    
    Major_allocated
    
    Major_collection
    
    Minor_allocated
    
    Minor_collection
    
    Monotonic_clock
    
    One
    
    Promoted
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 to choose predictors and the responder. Indeed, the user can use others metrics (such as perf) and the API allows to analyze such metrics each other.

module OLS : sig ... end

module RANSAC : sig ... end

type 'a t

Type of analyze.

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

ols ~r_square ~bootstrap ~predictors is an Ordinary Least Square analyze on predictors. It calculate r² if r_square = true. bootstrap is the number of how many times Bechamel try to resample measurements.

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

val one : 'a t -> Measure.witness -> Benchmark.t -> 'a

one analyze measure (stat, samples) estimates the actual given measures for one predictors. So, one analyze time (stat, samples) where analyze is initialized with run predictor wants to estimate actual run-time (or execution time) value.

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

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