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nt(Pe.Layout,null,{})},enhanceApp({app:o,router:e,siteData:t}){Ya(o)}};export{Qa as R,bo as c,L as u}; diff --git a/previews/PR117/assets/reference.md.aVgNy7tN.js b/previews/PR117/assets/reference.md.BgXJBiXX.js similarity index 99% rename from previews/PR117/assets/reference.md.aVgNy7tN.js rename to previews/PR117/assets/reference.md.BgXJBiXX.js index 8512c339..e801ae42 100644 --- a/previews/PR117/assets/reference.md.aVgNy7tN.js +++ b/previews/PR117/assets/reference.md.BgXJBiXX.js @@ -9,7 +9,7 @@ import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.CJC1Ax_0.js";const g warmup ::Float64 # Whether this sample had a warmup run before it (1.0 = yes. 0.0 = no). checksum ::Float64 # A checksum based on the values returned by the benchmarked function ...more fields may be added... -end

A struct representing a single sample of a benchmark.

@b returns a composite sample formed by taking the field-wise minimum of the measured samples. More fields may be added in the future as more information becomes available.

source


# Chairmarks.Benchmark — Type.
julia
struct Benchmark
+end

A struct representing a single sample of a benchmark.

@b returns a composite sample formed by taking the field-wise minimum of the measured samples. More fields may be added in the future as more information becomes available.

source


# Chairmarks.Benchmark — Type.
julia
struct Benchmark
     samples::Vector{Sample}
     ...more fields may be added...
 end

A struct representing a complete benchmark result. Returned by @be.

More fields may be added in the future to represent non sample specific information.

The functions minimum and maximum are defined field wise on Benchmark objects and return Samples. On Julia 1.9 and above, the functions Statistics.median, Statistics.mean, and Statistics.quantile are also defined field wise on Benchmark objects and return Samples.

julia
julia> @be eval(:(for _ in 1:10; sqrt(rand()); end))
@@ -20,7 +20,7 @@ import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.CJC1Ax_0.js";const g
  max    12.021 ms (3608 allocs: 173.453 KiB, 95.03% compile time)
 
 julia> minimum(ans)
-4.307 ms (3608 allocs: 173.453 KiB, 92.21% compile time)

source


# Chairmarks.@b — Macro.
julia
@b [[init] setup] f [teardown] keywords...

Benchmark f and return the fastest Sample.

Use @be for full results.

@b args... is equivalent to minimum(@be args...). See the docstring for @be for more information.

Examples

julia
julia> @b rand(10000) # Benchmark a function
+4.307 ms (3608 allocs: 173.453 KiB, 92.21% compile time)

source


# Chairmarks.@b — Macro.
julia
@b [[init] setup] f [teardown] keywords...

Benchmark f and return the fastest Sample.

Use @be for full results.

@b args... is equivalent to minimum(@be args...). See the docstring for @be for more information.

Examples

julia
julia> @b rand(10000) # Benchmark a function
 5.833 μs (2 allocs: 78.172 KiB)
 
 julia> @b rand hash # How long does it take to hash a random Float64?
@@ -42,7 +42,7 @@ import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.CJC1Ax_0.js";const g
 183.871 ns
 
 julia> @b (x = 0; for _ in 1:5e8; x = hash(x); end; x) # This runs for a long time, so it is only run once (with no warmup)
-2.447 s (without a warmup)

source


# Chairmarks.@be — Macro.
julia
@be [[init] setup] f [teardown] keywords...

Benchmark f and return the results as a Benchmark.

Use @b for abbreviated results.

Positional argument pipeline syntax

The four positional arguments form a pipeline with the return value of each passed as an argument to the next. Consequently, the first expression in the pipeline must be a nullary function. If you use a symbol like rand, it will be interpreted as a function and called normally. If you use any other expression, it will be interpreted as the body of a nullary function. For example in @be rand(10) the function being benchmarked is () -> rand(10).

Later positions in the pipeline must be unary functions. As with the first function, you may provide either a function, or an expression. However, the rules are slightly different. If the expression you provide contains an _ as an rvalue (which would otherwise error), it is interpreted as a unary function and any such occurrences of _ are replaced with result from the previous function in the pipeline. For example, in @be rand(10) sort(_, rev=true) the setup function is () -> rand(10) and the primary function is x -> sort(x, rev=true). If the expression you provide does not contain an _ as an rvalue, it is assumed to produce a function and is called with the result from the previous function in the pipeline. For example, in @be rand(10) sort!∘shuffle!, the primary function is simply sort!∘shuffle! and receives no preprocessing. @macroexpand can help elucidate what is going on in specific cases.

Positional argument disambiguation

setup, teardown, and init are optional and are parsed with that precedence giving these possible forms:

@be f
+2.447 s (without a warmup)

source


# Chairmarks.@be — Macro.
julia
@be [[init] setup] f [teardown] keywords...

Benchmark f and return the results as a Benchmark.

Use @b for abbreviated results.

Positional argument pipeline syntax

The four positional arguments form a pipeline with the return value of each passed as an argument to the next. Consequently, the first expression in the pipeline must be a nullary function. If you use a symbol like rand, it will be interpreted as a function and called normally. If you use any other expression, it will be interpreted as the body of a nullary function. For example in @be rand(10) the function being benchmarked is () -> rand(10).

Later positions in the pipeline must be unary functions. As with the first function, you may provide either a function, or an expression. However, the rules are slightly different. If the expression you provide contains an _ as an rvalue (which would otherwise error), it is interpreted as a unary function and any such occurrences of _ are replaced with result from the previous function in the pipeline. For example, in @be rand(10) sort(_, rev=true) the setup function is () -> rand(10) and the primary function is x -> sort(x, rev=true). If the expression you provide does not contain an _ as an rvalue, it is assumed to produce a function and is called with the result from the previous function in the pipeline. For example, in @be rand(10) sort!∘shuffle!, the primary function is simply sort!∘shuffle! and receives no preprocessing. @macroexpand can help elucidate what is going on in specific cases.

Positional argument disambiguation

setup, teardown, and init are optional and are parsed with that precedence giving these possible forms:

@be f
 @be setup f
 @be setup f teardown
 @be init setup f teardown

You may use an underscore _ to provide other combinations of arguments. For example, you may provide a teardown and no setup with

@be _ f teardown

Keyword arguments

Provide keyword arguments using name=value syntax similar to how you provide keyword arguments to ordinary functions. Keyword arguments to control executions are

  • evals::Integer How many function evaluations to perform in each sample. Defaults to automatic calibration.

  • samples::Integer Maximum number of samples to take. Defaults to unlimited and cannot be specified without also specifying evals. Specifying samples = 0 will cause @be to run the warmup sample only and return that sample.

  • seconds::Real Maximum amount of time to spend benchmarking. Defaults to 0.1 seconds unless samples is specified in which case it defaults to 1 second. Set to Inf to disable the time limit. Compile time is typically not counted against this limit. A reasonable effort is made to respect the time limit, but it is always exceeded by a small about (less than 1%) and can be significantly exceeded when benchmarking long running functions.

  • gc::Bool An experimental option to disable garbage collection during benchmarking. Defaults to true. Set to false to garbage collection during benchmarking. Disabling garbage collection may cause out of memory errors during a benchmark that requires garbage collection, but should not result in memory leaks that survive past the end of the benchmark. As an experimental option, this may be removed in the future or its semantics may change. This option also depends on Julia internals and so it may break in future versions of Julia.

Interpolation

You may use standard interpolation syntax within any of the positional arguments. This will cause the interpolated values to be evaluated only once upon execution of the benchmark and the runtime of that evlaution will not be included in reported results. For example,

x = [1,2,3]
@@ -112,4 +112,4 @@ import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.CJC1Ax_0.js";const g
 
 julia> @be (x = 0; for _ in 1:5e8; x = hash(x); end; x) # This runs for a long time, so it is only run once (with no warmup)
 Benchmark: 1 sample with 1 evaluation
-        2.488 s (without a warmup)

source


`,12)]))}const o=i(l,[["render",e]]);export{g as __pageData,o as default}; + 2.488 s (without a warmup)

source


`,12)]))}const o=i(l,[["render",e]]);export{g as __pageData,o as default}; diff --git a/previews/PR117/assets/reference.md.aVgNy7tN.lean.js b/previews/PR117/assets/reference.md.BgXJBiXX.lean.js similarity index 99% rename from previews/PR117/assets/reference.md.aVgNy7tN.lean.js rename to previews/PR117/assets/reference.md.BgXJBiXX.lean.js index 8512c339..e801ae42 100644 --- a/previews/PR117/assets/reference.md.aVgNy7tN.lean.js +++ b/previews/PR117/assets/reference.md.BgXJBiXX.lean.js @@ -9,7 +9,7 @@ import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.CJC1Ax_0.js";const g warmup ::Float64 # Whether this sample had a warmup run before it (1.0 = yes. 0.0 = no). checksum ::Float64 # A checksum based on the values returned by the benchmarked function ...more fields may be added... -end

A struct representing a single sample of a benchmark.

@b returns a composite sample formed by taking the field-wise minimum of the measured samples. More fields may be added in the future as more information becomes available.

source


# Chairmarks.Benchmark — Type.
julia
struct Benchmark
+end

A struct representing a single sample of a benchmark.

@b returns a composite sample formed by taking the field-wise minimum of the measured samples. More fields may be added in the future as more information becomes available.

source


# Chairmarks.Benchmark — Type.
julia
struct Benchmark
     samples::Vector{Sample}
     ...more fields may be added...
 end

A struct representing a complete benchmark result. Returned by @be.

More fields may be added in the future to represent non sample specific information.

The functions minimum and maximum are defined field wise on Benchmark objects and return Samples. On Julia 1.9 and above, the functions Statistics.median, Statistics.mean, and Statistics.quantile are also defined field wise on Benchmark objects and return Samples.

julia
julia> @be eval(:(for _ in 1:10; sqrt(rand()); end))
@@ -20,7 +20,7 @@ import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.CJC1Ax_0.js";const g
  max    12.021 ms (3608 allocs: 173.453 KiB, 95.03% compile time)
 
 julia> minimum(ans)
-4.307 ms (3608 allocs: 173.453 KiB, 92.21% compile time)

source


# Chairmarks.@b — Macro.
julia
@b [[init] setup] f [teardown] keywords...

Benchmark f and return the fastest Sample.

Use @be for full results.

@b args... is equivalent to minimum(@be args...). See the docstring for @be for more information.

Examples

julia
julia> @b rand(10000) # Benchmark a function
+4.307 ms (3608 allocs: 173.453 KiB, 92.21% compile time)

source


# Chairmarks.@b — Macro.
julia
@b [[init] setup] f [teardown] keywords...

Benchmark f and return the fastest Sample.

Use @be for full results.

@b args... is equivalent to minimum(@be args...). See the docstring for @be for more information.

Examples

julia
julia> @b rand(10000) # Benchmark a function
 5.833 μs (2 allocs: 78.172 KiB)
 
 julia> @b rand hash # How long does it take to hash a random Float64?
@@ -42,7 +42,7 @@ import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.CJC1Ax_0.js";const g
 183.871 ns
 
 julia> @b (x = 0; for _ in 1:5e8; x = hash(x); end; x) # This runs for a long time, so it is only run once (with no warmup)
-2.447 s (without a warmup)

source


# Chairmarks.@be — Macro.
julia
@be [[init] setup] f [teardown] keywords...

Benchmark f and return the results as a Benchmark.

Use @b for abbreviated results.

Positional argument pipeline syntax

The four positional arguments form a pipeline with the return value of each passed as an argument to the next. Consequently, the first expression in the pipeline must be a nullary function. If you use a symbol like rand, it will be interpreted as a function and called normally. If you use any other expression, it will be interpreted as the body of a nullary function. For example in @be rand(10) the function being benchmarked is () -> rand(10).

Later positions in the pipeline must be unary functions. As with the first function, you may provide either a function, or an expression. However, the rules are slightly different. If the expression you provide contains an _ as an rvalue (which would otherwise error), it is interpreted as a unary function and any such occurrences of _ are replaced with result from the previous function in the pipeline. For example, in @be rand(10) sort(_, rev=true) the setup function is () -> rand(10) and the primary function is x -> sort(x, rev=true). If the expression you provide does not contain an _ as an rvalue, it is assumed to produce a function and is called with the result from the previous function in the pipeline. For example, in @be rand(10) sort!∘shuffle!, the primary function is simply sort!∘shuffle! and receives no preprocessing. @macroexpand can help elucidate what is going on in specific cases.

Positional argument disambiguation

setup, teardown, and init are optional and are parsed with that precedence giving these possible forms:

@be f
+2.447 s (without a warmup)

source


# Chairmarks.@be — Macro.
julia
@be [[init] setup] f [teardown] keywords...

Benchmark f and return the results as a Benchmark.

Use @b for abbreviated results.

Positional argument pipeline syntax

The four positional arguments form a pipeline with the return value of each passed as an argument to the next. Consequently, the first expression in the pipeline must be a nullary function. If you use a symbol like rand, it will be interpreted as a function and called normally. If you use any other expression, it will be interpreted as the body of a nullary function. For example in @be rand(10) the function being benchmarked is () -> rand(10).

Later positions in the pipeline must be unary functions. As with the first function, you may provide either a function, or an expression. However, the rules are slightly different. If the expression you provide contains an _ as an rvalue (which would otherwise error), it is interpreted as a unary function and any such occurrences of _ are replaced with result from the previous function in the pipeline. For example, in @be rand(10) sort(_, rev=true) the setup function is () -> rand(10) and the primary function is x -> sort(x, rev=true). If the expression you provide does not contain an _ as an rvalue, it is assumed to produce a function and is called with the result from the previous function in the pipeline. For example, in @be rand(10) sort!∘shuffle!, the primary function is simply sort!∘shuffle! and receives no preprocessing. @macroexpand can help elucidate what is going on in specific cases.

Positional argument disambiguation

setup, teardown, and init are optional and are parsed with that precedence giving these possible forms:

@be f
 @be setup f
 @be setup f teardown
 @be init setup f teardown

You may use an underscore _ to provide other combinations of arguments. For example, you may provide a teardown and no setup with

@be _ f teardown

Keyword arguments

Provide keyword arguments using name=value syntax similar to how you provide keyword arguments to ordinary functions. Keyword arguments to control executions are

  • evals::Integer How many function evaluations to perform in each sample. Defaults to automatic calibration.

  • samples::Integer Maximum number of samples to take. Defaults to unlimited and cannot be specified without also specifying evals. Specifying samples = 0 will cause @be to run the warmup sample only and return that sample.

  • seconds::Real Maximum amount of time to spend benchmarking. Defaults to 0.1 seconds unless samples is specified in which case it defaults to 1 second. Set to Inf to disable the time limit. Compile time is typically not counted against this limit. A reasonable effort is made to respect the time limit, but it is always exceeded by a small about (less than 1%) and can be significantly exceeded when benchmarking long running functions.

  • gc::Bool An experimental option to disable garbage collection during benchmarking. Defaults to true. Set to false to garbage collection during benchmarking. Disabling garbage collection may cause out of memory errors during a benchmark that requires garbage collection, but should not result in memory leaks that survive past the end of the benchmark. As an experimental option, this may be removed in the future or its semantics may change. This option also depends on Julia internals and so it may break in future versions of Julia.

Interpolation

You may use standard interpolation syntax within any of the positional arguments. This will cause the interpolated values to be evaluated only once upon execution of the benchmark and the runtime of that evlaution will not be included in reported results. For example,

x = [1,2,3]
@@ -112,4 +112,4 @@ import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.CJC1Ax_0.js";const g
 
 julia> @be (x = 0; for _ in 1:5e8; x = hash(x); end; x) # This runs for a long time, so it is only run once (with no warmup)
 Benchmark: 1 sample with 1 evaluation
-        2.488 s (without a warmup)

source


`,12)]))}const o=i(l,[["render",e]]);export{g as __pageData,o as default}; + 2.488 s (without a warmup)

source


`,12)]))}const o=i(l,[["render",e]]);export{g as __pageData,o as default}; diff --git a/previews/PR117/assets/why.md.CMh4TIgK.js b/previews/PR117/assets/why.md.BGSZYcVH.js similarity index 89% rename from previews/PR117/assets/why.md.CMh4TIgK.js rename to previews/PR117/assets/why.md.BGSZYcVH.js index d4af8510..82a87d28 100644 --- a/previews/PR117/assets/why.md.CMh4TIgK.js +++ b/previews/PR117/assets/why.md.BGSZYcVH.js @@ -1,4 +1,4 @@ -import{_ as i,c as a,a5 as t,o as n}from"./chunks/framework.CJC1Ax_0.js";const g=JSON.parse('{"title":"","description":"","frontmatter":{},"headers":[],"relativePath":"why.md","filePath":"why.md","lastUpdated":null}'),h={name:"why.md"};function l(e,s,k,p,r,d){return n(),a("div",null,s[0]||(s[0]=[t(`

Precise ​

Capable of detecting 1% difference in runtime in ideal conditions

julia
julia> f(n) = sum(rand() for _ in 1:n)
+import{_ as i,c as a,a5 as t,o as n}from"./chunks/framework.CJC1Ax_0.js";const g=JSON.parse('{"title":"","description":"","frontmatter":{},"headers":[],"relativePath":"why.md","filePath":"why.md","lastUpdated":null}'),h={name:"why.md"};function e(l,s,k,p,r,d){return n(),a("div",null,s[0]||(s[0]=[t(`
Precise ​
Capable of detecting 1% difference in runtime in ideal conditions
julia
julia> f(n) = sum(rand() for _ in 1:n)
 f (generic function with 1 method)
 
 julia> @b f(1000)
@@ -27,11 +27,8 @@ import{_ as i,c as a,a5 as t,o as n}from"./chunks/framework.CJC1Ax_0.js";const g
 [...]
 
 julia> @b rand(100) sort(_, by=x -> exp(-x)) issorted(_, rev=true) || error()
-5.358 μs (2 allocs: 1.750 KiB)
See @b for more info
Truthful ​
Chairmarks automatically computes a checksum based on the results of the provided computations, and returns that checksum to the user along with benchmark results. This makes it impossible for the compiler to elide any part of the computation that has an impact on its return value.
While the checksums are fast, one negative side effect of this is that they add a bit of overhead to the measured runtime, and that overhead can vary depending on the function being benchmarked. These checksums are performed by computing a map over the returned values and a reduction over those mapped values. You can disable this by passing the checksum=false keyword argument, possibly in combination with a custom teardown function that verifies computation results. Be aware that as the compiler improves, it may become better at eliding benchmarks whose results are not saved.
julia
julia> @b 1
-0.713 ns
+5.358 μs (2 allocs: 1.750 KiB)
See @be for more info
Truthful ​
On versions of Julia prior to 1.8, Chairmarks automatically computes a checksum based on the results of the provided computations and returns that checksum to the user along with benchmark results. This makes it impossible for the compiler to elide any part of the computation that has an impact on its return value.
While the checksums are fast, one negative side effect of this is that they add a bit of overhead to the measured runtime, and that overhead can vary depending on the function being benchmarked. In versions of Julia 1.8 and later, these checksums are emulated using the function Base.donotdelete which is designed and documented to ensure that necessary computation is not elided without adding extra overhead. You can disable all of this on all versions of Julia by passing the checksum=false keyword argument, possibly in combination with a custom teardown function that verifies computation results. Be aware that as the compiler improves, it may become better at eliding benchmarks whose results are not saved.
julia
julia> @b rand hash
+2.276 ns
 
-julia> @b 1.0
-1.135 ns
-
-julia> @b 1.0 checksum=false
-0 ns
You may experiment with custom reductions using the internal _map and _reduction keyword arguments. The default maps and reductions (Chairmarks.default_map and Chairmarks.default_reduction) are internal and subject to change and/or removal in the future.
Innate qualities ​
Chairmarks is inherently narrower than BenchmarkTools by construction. It also has more reliable back support. Back support is a defining feature of chairs while benches are known to sometimes lack back support.
`,23)]))}const y=i(h,[["render",l]]);export{g as __pageData,y as default};
+julia> @b rand hash checksum=false
+0 ns

You may experiment with custom reductions using the internal _map and _reduction keyword arguments. The default maps and reductions (Chairmarks.default_map and Chairmarks.default_reduction) are internal and subject to change and/or removal in the future.

Innate qualities ​

Chairmarks is inherently narrower than BenchmarkTools by construction. It also has more reliable back support. Back support is a defining feature of chairs while benches are known to sometimes lack back support.

`,23)]))}const y=i(h,[["render",e]]);export{g as __pageData,y as default}; diff --git a/previews/PR117/assets/why.md.CMh4TIgK.lean.js b/previews/PR117/assets/why.md.BGSZYcVH.lean.js similarity index 89% rename from previews/PR117/assets/why.md.CMh4TIgK.lean.js rename to previews/PR117/assets/why.md.BGSZYcVH.lean.js index d4af8510..82a87d28 100644 --- a/previews/PR117/assets/why.md.CMh4TIgK.lean.js +++ b/previews/PR117/assets/why.md.BGSZYcVH.lean.js @@ -1,4 +1,4 @@ -import{_ as i,c as a,a5 as t,o as n}from"./chunks/framework.CJC1Ax_0.js";const g=JSON.parse('{"title":"","description":"","frontmatter":{},"headers":[],"relativePath":"why.md","filePath":"why.md","lastUpdated":null}'),h={name:"why.md"};function l(e,s,k,p,r,d){return n(),a("div",null,s[0]||(s[0]=[t(`

Precise ​

Capable of detecting 1% difference in runtime in ideal conditions

julia
julia> f(n) = sum(rand() for _ in 1:n)
+import{_ as i,c as a,a5 as t,o as n}from"./chunks/framework.CJC1Ax_0.js";const g=JSON.parse('{"title":"","description":"","frontmatter":{},"headers":[],"relativePath":"why.md","filePath":"why.md","lastUpdated":null}'),h={name:"why.md"};function e(l,s,k,p,r,d){return n(),a("div",null,s[0]||(s[0]=[t(`
Precise ​
Capable of detecting 1% difference in runtime in ideal conditions
julia
julia> f(n) = sum(rand() for _ in 1:n)
 f (generic function with 1 method)
 
 julia> @b f(1000)
@@ -27,11 +27,8 @@ import{_ as i,c as a,a5 as t,o as n}from"./chunks/framework.CJC1Ax_0.js";const g
 [...]
 
 julia> @b rand(100) sort(_, by=x -> exp(-x)) issorted(_, rev=true) || error()
-5.358 μs (2 allocs: 1.750 KiB)
See @b for more info
Truthful ​
Chairmarks automatically computes a checksum based on the results of the provided computations, and returns that checksum to the user along with benchmark results. This makes it impossible for the compiler to elide any part of the computation that has an impact on its return value.
While the checksums are fast, one negative side effect of this is that they add a bit of overhead to the measured runtime, and that overhead can vary depending on the function being benchmarked. These checksums are performed by computing a map over the returned values and a reduction over those mapped values. You can disable this by passing the checksum=false keyword argument, possibly in combination with a custom teardown function that verifies computation results. Be aware that as the compiler improves, it may become better at eliding benchmarks whose results are not saved.
julia
julia> @b 1
-0.713 ns
+5.358 μs (2 allocs: 1.750 KiB)
See @be for more info
Truthful ​
On versions of Julia prior to 1.8, Chairmarks automatically computes a checksum based on the results of the provided computations and returns that checksum to the user along with benchmark results. This makes it impossible for the compiler to elide any part of the computation that has an impact on its return value.
While the checksums are fast, one negative side effect of this is that they add a bit of overhead to the measured runtime, and that overhead can vary depending on the function being benchmarked. In versions of Julia 1.8 and later, these checksums are emulated using the function Base.donotdelete which is designed and documented to ensure that necessary computation is not elided without adding extra overhead. You can disable all of this on all versions of Julia by passing the checksum=false keyword argument, possibly in combination with a custom teardown function that verifies computation results. Be aware that as the compiler improves, it may become better at eliding benchmarks whose results are not saved.
julia
julia> @b rand hash
+2.276 ns
 
-julia> @b 1.0
-1.135 ns
-
-julia> @b 1.0 checksum=false
-0 ns
You may experiment with custom reductions using the internal _map and _reduction keyword arguments. The default maps and reductions (Chairmarks.default_map and Chairmarks.default_reduction) are internal and subject to change and/or removal in the future.
Innate qualities ​
Chairmarks is inherently narrower than BenchmarkTools by construction. It also has more reliable back support. Back support is a defining feature of chairs while benches are known to sometimes lack back support.
`,23)]))}const y=i(h,[["render",l]]);export{g as __pageData,y as default};
+julia> @b rand hash checksum=false
+0 ns

You may experiment with custom reductions using the internal _map and _reduction keyword arguments. The default maps and reductions (Chairmarks.default_map and Chairmarks.default_reduction) are internal and subject to change and/or removal in the future.

Innate qualities ​

Chairmarks is inherently narrower than BenchmarkTools by construction. It also has more reliable back support. Back support is a defining feature of chairs while benches are known to sometimes lack back support.

`,23)]))}const y=i(h,[["render",e]]);export{g as __pageData,y as default}; diff --git a/previews/PR117/autoload.html b/previews/PR117/autoload.html index 3b526f8b..a41d5967 100644 --- a/previews/PR117/autoload.html +++ b/previews/PR117/autoload.html @@ -8,9 +8,9 @@ - + - + @@ -23,7 +23,7 @@ ["@b", "@be"] => :(using Chairmarks), ]) end - + \ No newline at end of file diff --git a/previews/PR117/explanations.html b/previews/PR117/explanations.html index 0a9f7b68..95fcdd56 100644 --- a/previews/PR117/explanations.html +++ b/previews/PR117/explanations.html @@ -8,9 +8,9 @@ - + - + @@ -46,7 +46,7 @@ julia> @btime sleep(1) 1.002 s (4 allocations: 112 bytes)

It would be a loss restrict ourselves to only runtime and allocations, it would be distracting to include "0% compilation time" in outputs which have zero compile time, and it would be inconsistent to make some fields (e.g. allocation count and amount) always display while others are only displayed when non-zero. Sparse display is the compromise I've chosen to get the best of both worlds.

- + \ No newline at end of file diff --git a/previews/PR117/hashmap.json b/previews/PR117/hashmap.json index 49e9866c..0e68cdfe 100644 --- a/previews/PR117/hashmap.json +++ b/previews/PR117/hashmap.json @@ -1 +1 @@ -{"autoload.md":"D1G-8Iua","explanations.md":"CcfdxnSq","index.md":"DQL1Mz9j","migration.md":"C6xB4EcO","reference.md":"aVgNy7tN","regressions.md":"DmkSa00d","tutorial.md":"CBMMzJfV","why.md":"CMh4TIgK"} +{"autoload.md":"D1G-8Iua","explanations.md":"CcfdxnSq","index.md":"DQL1Mz9j","migration.md":"C6xB4EcO","reference.md":"BgXJBiXX","regressions.md":"DmkSa00d","tutorial.md":"CBMMzJfV","why.md":"BGSZYcVH"} diff --git a/previews/PR117/index.html b/previews/PR117/index.html index 71d01759..9b4d74da 100644 --- a/previews/PR117/index.html +++ b/previews/PR117/index.html @@ -8,9 +8,9 @@ - + - + @@ -27,7 +27,7 @@ julia> @b rand(1000) _.*5 # How long does it take to multiply it by 5 element wise? 172.970 ns (3 allocs: 7.875 KiB) - + \ No newline at end of file diff --git a/previews/PR117/migration.html b/previews/PR117/migration.html index fe72d961..b396e94e 100644 --- a/previews/PR117/migration.html +++ b/previews/PR117/migration.html @@ -8,9 +8,9 @@ - + - + @@ -43,7 +43,7 @@ julia> @b x rand # put the access in the setup phase (most concise in simple cases) 15.507 ns (2 allocs: 112 bytes) - + \ No newline at end of file diff --git a/previews/PR117/reference.html b/previews/PR117/reference.html index 1114b4c2..6f100b15 100644 --- a/previews/PR117/reference.html +++ b/previews/PR117/reference.html @@ -8,11 +8,11 @@ - + - + - + @@ -28,7 +28,7 @@ warmup ::Float64 # Whether this sample had a warmup run before it (1.0 = yes. 0.0 = no). checksum ::Float64 # A checksum based on the values returned by the benchmarked function ...more fields may be added... -end

A struct representing a single sample of a benchmark.

@b returns a composite sample formed by taking the field-wise minimum of the measured samples. More fields may be added in the future as more information becomes available.

source


# Chairmarks.Benchmark — Type.
julia
struct Benchmark
+end

A struct representing a single sample of a benchmark.

@b returns a composite sample formed by taking the field-wise minimum of the measured samples. More fields may be added in the future as more information becomes available.

source


# Chairmarks.Benchmark — Type.
julia
struct Benchmark
     samples::Vector{Sample}
     ...more fields may be added...
 end

A struct representing a complete benchmark result. Returned by @be.

More fields may be added in the future to represent non sample specific information.

The functions minimum and maximum are defined field wise on Benchmark objects and return Samples. On Julia 1.9 and above, the functions Statistics.median, Statistics.mean, and Statistics.quantile are also defined field wise on Benchmark objects and return Samples.

julia
julia> @be eval(:(for _ in 1:10; sqrt(rand()); end))
@@ -39,7 +39,7 @@
  max    12.021 ms (3608 allocs: 173.453 KiB, 95.03% compile time)
 
 julia> minimum(ans)
-4.307 ms (3608 allocs: 173.453 KiB, 92.21% compile time)

source


# Chairmarks.@b — Macro.
julia
@b [[init] setup] f [teardown] keywords...

Benchmark f and return the fastest Sample.

Use @be for full results.

@b args... is equivalent to minimum(@be args...). See the docstring for @be for more information.

Examples

julia
julia> @b rand(10000) # Benchmark a function
+4.307 ms (3608 allocs: 173.453 KiB, 92.21% compile time)

source


# Chairmarks.@b — Macro.
julia
@b [[init] setup] f [teardown] keywords...

Benchmark f and return the fastest Sample.

Use @be for full results.

@b args... is equivalent to minimum(@be args...). See the docstring for @be for more information.

Examples

julia
julia> @b rand(10000) # Benchmark a function
 5.833 μs (2 allocs: 78.172 KiB)
 
 julia> @b rand hash # How long does it take to hash a random Float64?
@@ -61,7 +61,7 @@
 183.871 ns
 
 julia> @b (x = 0; for _ in 1:5e8; x = hash(x); end; x) # This runs for a long time, so it is only run once (with no warmup)
-2.447 s (without a warmup)

source


# Chairmarks.@be — Macro.
julia
@be [[init] setup] f [teardown] keywords...

Benchmark f and return the results as a Benchmark.

Use @b for abbreviated results.

Positional argument pipeline syntax

The four positional arguments form a pipeline with the return value of each passed as an argument to the next. Consequently, the first expression in the pipeline must be a nullary function. If you use a symbol like rand, it will be interpreted as a function and called normally. If you use any other expression, it will be interpreted as the body of a nullary function. For example in @be rand(10) the function being benchmarked is () -> rand(10).

Later positions in the pipeline must be unary functions. As with the first function, you may provide either a function, or an expression. However, the rules are slightly different. If the expression you provide contains an _ as an rvalue (which would otherwise error), it is interpreted as a unary function and any such occurrences of _ are replaced with result from the previous function in the pipeline. For example, in @be rand(10) sort(_, rev=true) the setup function is () -> rand(10) and the primary function is x -> sort(x, rev=true). If the expression you provide does not contain an _ as an rvalue, it is assumed to produce a function and is called with the result from the previous function in the pipeline. For example, in @be rand(10) sort!∘shuffle!, the primary function is simply sort!∘shuffle! and receives no preprocessing. @macroexpand can help elucidate what is going on in specific cases.

Positional argument disambiguation

setup, teardown, and init are optional and are parsed with that precedence giving these possible forms:

@be f
+2.447 s (without a warmup)

source


# Chairmarks.@be — Macro.
julia
@be [[init] setup] f [teardown] keywords...

Benchmark f and return the results as a Benchmark.

Use @b for abbreviated results.

Positional argument pipeline syntax

The four positional arguments form a pipeline with the return value of each passed as an argument to the next. Consequently, the first expression in the pipeline must be a nullary function. If you use a symbol like rand, it will be interpreted as a function and called normally. If you use any other expression, it will be interpreted as the body of a nullary function. For example in @be rand(10) the function being benchmarked is () -> rand(10).

Later positions in the pipeline must be unary functions. As with the first function, you may provide either a function, or an expression. However, the rules are slightly different. If the expression you provide contains an _ as an rvalue (which would otherwise error), it is interpreted as a unary function and any such occurrences of _ are replaced with result from the previous function in the pipeline. For example, in @be rand(10) sort(_, rev=true) the setup function is () -> rand(10) and the primary function is x -> sort(x, rev=true). If the expression you provide does not contain an _ as an rvalue, it is assumed to produce a function and is called with the result from the previous function in the pipeline. For example, in @be rand(10) sort!∘shuffle!, the primary function is simply sort!∘shuffle! and receives no preprocessing. @macroexpand can help elucidate what is going on in specific cases.

Positional argument disambiguation

setup, teardown, and init are optional and are parsed with that precedence giving these possible forms:

@be f
 @be setup f
 @be setup f teardown
 @be init setup f teardown

You may use an underscore _ to provide other combinations of arguments. For example, you may provide a teardown and no setup with

@be _ f teardown

Keyword arguments

Provide keyword arguments using name=value syntax similar to how you provide keyword arguments to ordinary functions. Keyword arguments to control executions are

  • evals::Integer How many function evaluations to perform in each sample. Defaults to automatic calibration.

  • samples::Integer Maximum number of samples to take. Defaults to unlimited and cannot be specified without also specifying evals. Specifying samples = 0 will cause @be to run the warmup sample only and return that sample.

  • seconds::Real Maximum amount of time to spend benchmarking. Defaults to 0.1 seconds unless samples is specified in which case it defaults to 1 second. Set to Inf to disable the time limit. Compile time is typically not counted against this limit. A reasonable effort is made to respect the time limit, but it is always exceeded by a small about (less than 1%) and can be significantly exceeded when benchmarking long running functions.

  • gc::Bool An experimental option to disable garbage collection during benchmarking. Defaults to true. Set to false to garbage collection during benchmarking. Disabling garbage collection may cause out of memory errors during a benchmark that requires garbage collection, but should not result in memory leaks that survive past the end of the benchmark. As an experimental option, this may be removed in the future or its semantics may change. This option also depends on Julia internals and so it may break in future versions of Julia.

Interpolation

You may use standard interpolation syntax within any of the positional arguments. This will cause the interpolated values to be evaluated only once upon execution of the benchmark and the runtime of that evlaution will not be included in reported results. For example,

x = [1,2,3]
@@ -131,8 +131,8 @@
 
 julia> @be (x = 0; for _ in 1:5e8; x = hash(x); end; x) # This runs for a long time, so it is only run once (with no warmup)
 Benchmark: 1 sample with 1 evaluation
-        2.488 s (without a warmup)

source


- + 2.488 s (without a warmup)

source


+ \ No newline at end of file diff --git a/previews/PR117/regressions.html b/previews/PR117/regressions.html index fcf126eb..2f334ed6 100644 --- a/previews/PR117/regressions.html +++ b/previews/PR117/regressions.html @@ -8,9 +8,9 @@ - + - + @@ -26,7 +26,7 @@ @testset "Regression tests" begin RegressionTests.test(skip_unsupported_platforms=true) end

See the RegressionTests.jl documentation for more information.

- + \ No newline at end of file diff --git a/previews/PR117/tutorial.html b/previews/PR117/tutorial.html index 961954e2..ec4e4b71 100644 --- a/previews/PR117/tutorial.html +++ b/previews/PR117/tutorial.html @@ -8,9 +8,9 @@ - + - + @@ -78,7 +78,7 @@ 129.294 ns (3 allocs: 7.875 KiB) 129.471 ns (3 allocs: 7.875 KiB) 130.570 ns (3 allocs: 7.875 KiB)

Setting the seconds parameter too low can cause benchmarks to be noisy. It's good practice to run a benchmark at least a couple of times no matter what the configuration is to make sure it's reasonably stable.

Advanced usage ​

It is possible to manually specify the number of evaluations, samples, and/or seconds to run benchmarking for. It is also possible to pass a teardown function or an initialization function that runs only once. See the docstring of @be for more information on these additional arguments.

  1. note that the samples are aggregated element wise, so the max field reports the maximum runtime and the maximum proportion of runtime spent in garbage collection (gc). Thus it is possible that the trial which had a 19.748 μs runtime was not the same trial that spent 96.95% of its time in garbage collection. This is in order to make the results more consistent. If half the trials spend 10% of their time in gc amd runtime varies based on other factors, it would be unfortunate to report maximum gc time as either 10% or 0% at random depending on whether the longest running trial happened to trigger gc. ↩︎

- + \ No newline at end of file diff --git a/previews/PR117/why.html b/previews/PR117/why.html index e62c5477..5785a37e 100644 --- a/previews/PR117/why.html +++ b/previews/PR117/why.html @@ -8,11 +8,11 @@ - + - + - + @@ -46,15 +46,12 @@ [...] julia> @b rand(100) sort(_, by=x -> exp(-x)) issorted(_, rev=true) || error() -5.358 μs (2 allocs: 1.750 KiB)

See @b for more info

Truthful ​

Chairmarks automatically computes a checksum based on the results of the provided computations, and returns that checksum to the user along with benchmark results. This makes it impossible for the compiler to elide any part of the computation that has an impact on its return value.

While the checksums are fast, one negative side effect of this is that they add a bit of overhead to the measured runtime, and that overhead can vary depending on the function being benchmarked. These checksums are performed by computing a map over the returned values and a reduction over those mapped values. You can disable this by passing the checksum=false keyword argument, possibly in combination with a custom teardown function that verifies computation results. Be aware that as the compiler improves, it may become better at eliding benchmarks whose results are not saved.

julia
julia> @b 1
-0.713 ns
+5.358 μs (2 allocs: 1.750 KiB)

See @be for more info

Truthful ​

On versions of Julia prior to 1.8, Chairmarks automatically computes a checksum based on the results of the provided computations and returns that checksum to the user along with benchmark results. This makes it impossible for the compiler to elide any part of the computation that has an impact on its return value.

While the checksums are fast, one negative side effect of this is that they add a bit of overhead to the measured runtime, and that overhead can vary depending on the function being benchmarked. In versions of Julia 1.8 and later, these checksums are emulated using the function Base.donotdelete which is designed and documented to ensure that necessary computation is not elided without adding extra overhead. You can disable all of this on all versions of Julia by passing the checksum=false keyword argument, possibly in combination with a custom teardown function that verifies computation results. Be aware that as the compiler improves, it may become better at eliding benchmarks whose results are not saved.

julia
julia> @b rand hash
+2.276 ns
 
-julia> @b 1.0
-1.135 ns
-
-julia> @b 1.0 checksum=false
+julia> @b rand hash checksum=false
 0 ns

You may experiment with custom reductions using the internal _map and _reduction keyword arguments. The default maps and reductions (Chairmarks.default_map and Chairmarks.default_reduction) are internal and subject to change and/or removal in the future.

Innate qualities ​

Chairmarks is inherently narrower than BenchmarkTools by construction. It also has more reliable back support. Back support is a defining feature of chairs while benches are known to sometimes lack back support.

- + \ No newline at end of file