docs: update benchmarks in README

README.md

@@ -38,7 +38,7 @@ func main() {
 }
 ```
 
-## Benchmarks
+## Performance
 
 The existing Go library that has a comparable implementation of this is
 [`github.com/jmcvetta/randutil`][1], which optimizes for the single operation
@@ -48,31 +48,36 @@ faster, especially for large data sets.
 
 [1]: https://github.com/jmcvetta/randutil
 
-Comparison of this library versus `randutil.ChooseWeighted`. For repeated
-samplings from large collections, `weightedrand` will be much quicker.
-
-| Num choices | `randutil` | `weightedrand` |
-| ----------: | ------------: | -------------: |
-| 10 | 435 ns/op | 58 ns/op |
-| 100 | 511 ns/op | 84 ns/op |
-| 1,000 | 1297 ns/op | 112 ns/op |
-| 10,000 | 7952 ns/op | 137 ns/op |
-| 100,000 | 85142 ns/op | 173 ns/op |
-| 1,000,000 | 2082248 ns/op | 312 ns/op |
+Comparison of this library versus `randutil.ChooseWeighted` on my workstation.
+For repeated samplings from large collections, `weightedrand` will be much
+quicker:
+
+| Num choices | `randutil` | `weightedrand` | `weightedrand -cpu=8`* |
+| ----------: | -------------: | -------------: | ---------------------: |
+| 10 | 201 ns/op | 38 ns/op | 2.9 ns/op |
+| 100 | 267 ns/op | 51 ns/op | 4.1 ns/op |
+| 1,000 | 1012 ns/op | 67 ns/op | 5.4 ns/op |
+| 10,000 | 8683 ns/op | 83 ns/op | 6.9 ns/op |
+| 100,000 | 123500 ns/op | 105 ns/op | 12.0 ns/op |
+| 1,000,000 | 2399614 ns/op | 218 ns/op | 17.2 ns/op |
+| 10,000,000 | 26804440 ns/op | 432 ns/op | 35.1 ns/op |
+
+**: Since `v0.3.0` weightedrand can efficiently utilize a single Chooser across
+multiple CPU cores in parallel, making it even faster in overall throughput. See
+[PR#2](https://github.com/mroth/weightedrand/pull/2) for details. Informal
+benchmarks conducted on an Intel Xeon W-2140B CPU (8 core @ 3.2GHz,
+hyperthreading enabled).*
 
 Don't be mislead by these numbers into thinking `weightedrand` is always the
 right choice! If you are only picking from the same distribution once,
 `randutil` will be faster. `weightedrand` optimizes for repeated calls at the
-expense of some setup time and memory storage.
-
-*Update: Starting in `v0.3.0` weightedrand can now scale linearly to take
-advantage of multiple CPU cores in parallel, making it even faster. See
-[PR#2](https://github.com/mroth/weightedrand/pull/2) for details.*
+expense of some initialization time and memory storage.
 
 ## Caveats
 
-Note this uses `math/rand` instead of `crypto/rand`, as it is optimized for
-performance, not a cryptographically secure implementation.
+Note this library utilizes `math/rand` instead of `crypto/rand`, as it is
+optimized for performance, and is not intended to be used for cryptographically
+secure requirements.
 
 ## Credits