Update README.md

README.md

@@ -6,14 +6,14 @@
 
 This (unregistered) Julia package exposes a function `corkendall` that is a candidate to replace the function of the same name in the StatsBase package.
 
-The package also contains a function `speedtest` that prints a comparison of the execution speed of two (or more) implementations of Kendall Tau. `speedtest` demonstrates that the new version of `corkendall` is about ~~five~~ ~~six~~ seven times faster than the existing StatsBase version. See [# 634](https://github.com/JuliaStats/StatsBase.jl/issues/634).
+The package also contains a function `speedtest` that prints a comparison of the execution speed of two (or more) implementations of Kendall Tau. `speedtest` demonstrates that the new version of `corkendall` is about seven times faster than the existing StatsBase version. See [# 634](https://github.com/JuliaStats/StatsBase.jl/issues/634).
 
 ## Update February 2024
 The code of `corkendall` from this package was incorporated in StatsBase on 8 February 2021 (see [this](https://github.com/JuliaStats/StatsBase.jl/commit/11ac5b596405367b3217d3d962e22523fef9bb0d) commit).
 
 More recently I have made further changes:
 
-1) The function is now multi-threaded. On a PC with 12 cores and 20 logical processors this gives an approximate 12 times speed-up relative to `StatsBase.corkendall`
+1) The function is now multi-threaded. On a PC with 12 cores and 20 logical processors this gives an approximate 9 times speed-up relative to `StatsBase.corkendall`
 2) `KendallTau.corkendall` now has a `skipmissings` keyword argument, to control the treatment of missing values.
 3) A new function `corkendall_fromfile` takes arguments as names of csv files containing the input and output data.
 
@@ -40,54 +40,27 @@ help?> KendallTau.corkendall
 ## Performance
 In the REPL output below, note the large reduction in number and size of allocations. This was key to obtaining the full benefit of multi-threading.
 ```julia
-julia> using StatsBase,KendallTau,Random #StatsBase v0.33.21
+julia> using StatsBase,KendallTau,Random #StatsBase v0.34.2
 
 julia> x = rand(1000,10);StatsBase.corkendall(x)==KendallTau.corkendall(x)#compile
 true
 
 julia> x = rand(1000,1000);
 
 julia> @time res_sb = StatsBase.corkendall(x);
- 21.393938 seconds (3.00 M allocations: 17.082 GiB, 5.36% gc time)
+ 17.309843 seconds (3.00 M allocations: 17.090 GiB, 4.80% gc time)
 
 julia> @time res_kt = KendallTau.corkendall(x);
-  1.780313 seconds (2.28 k allocations: 8.876 MiB, 0.14% gc time)
+  1.850909 seconds (1.26 k allocations: 16.528 MiB)
 
-julia> 21.393938/1.780313
-12.016953198679108
+julia> 17.309843/1.850909
+9.352076736349545
 
-julia> res_sb == res_kt
+julia> res_sb==res_kt
 true
 
 julia> Threads.nthreads()#12 cores, 20 logical processors
 20
-```
-
-### Performance against size of `x`
-<img width="800" alt="image" src="plots/KendallTau vs StatsBase corkendall speed on 12 core 20 thread.svg">
-
-### Performance for very large `x`
-I wish to compute Kendall Tau for a set of 32,000 time series, each having observations every weekday over a four year period. Such a calculation takes some 42 minutes on my PC (Windows 11, 12th Gen Intel(R) Core(TM) i7-12700, 2100 Mhz, 12 Core(s), 20 Logical Processors), with Julia 1.8.5.
-
-```julia
-julia> Threads.nthreads()
-20
-
-julia> x = rand(1040,32000);
-
-julia> @time KendallTau.corkendall(x);
-2524.754279 seconds (64.28 k allocations: 7.633 GiB, 0.00% gc time)
-```
-
-**Update** 23 Jan 2024.
-
-On Julia 1.10, performance seems to have improved by about 10%:
-
-```julia
-julia> x = rand(1040,32000);
-
-julia> @time KendallTau.corkendall(x);
-2283.363978 seconds (32.26 k allocations: 7.882 GiB, 0.00% gc time)
 
 julia> versioninfo()
 Julia Version 1.10.0
@@ -103,10 +76,13 @@ Platform Info:
   Threads: 29 on 20 virtual cores
 Environment:
   JULIA_NUM_THREADS = 20
+  JULIA_PKG_DEVDIR = C:\Projects
+  JULIA_EDITOR = code
 ```
 
-
+### Performance against size of `x`
+<img width="800" alt="image" src="plots/KendallTau vs StatsBase corkendall speed on 12 core 20 thread.svg">
 
 
 Philip Swannell
-20 February 2023
+8 February 2024