Added small amount of documentation to vectorized_convenience_functions.

Author: chriselrod

docs/src/getting_started.md

@@ -42,7 +42,6 @@ Aside from loops, `LoopVectorization.jl` also supports broadcasting.
 
 ```julia
 julia> using LoopVectorization, BenchmarkTools
-[ Info: Precompiling LoopVectorization [bdcacae8-1622-11e9-2a5c-532679323890]
 
 julia> M, K, N = 47, 73, 7;
 

docs/src/vectorized_convenience_functions.md

@@ -0,0 +1,53 @@
+# Convenient Vectorized Functions
+
+## vmap
+
+
+
+
+## vfilter
+
+This function requires LLVM 7 or greater, and is only likly to give better performance if your CPU has AVX512. This is because it uses the compressed store intrinsic, which was added in LLVM 7. AVX512 provides a corresponding instruction, making the operation fast, while other instruction sets must emulate it, and thus are likely to get similar performance with `LoopVectorization.vfilter` as they do from `Base.filter`.
+
+```julia
+julia> using LoopVectorization, BenchmarkTools
+
+julia> x = rand(997);
+
+julia> y1 = filter(a -> a > 0.7, x);
+
+julia> y2 = vfilter(a -> a > 0.7, x);
+
+julia> y1 == y2
+true
+
+julia> @benchmark filter(a -> a > 0.7, $x)
+BenchmarkTools.Trial:
+  memory estimate:  7.94 KiB
+  allocs estimate:  1
+  --------------
+  minimum time:     955.389 ns (0.00% GC)
+  median time:      1.050 μs (0.00% GC)
+  mean time:        1.191 μs (9.72% GC)
+  maximum time:     82.799 μs (94.92% GC)
+  --------------
+  samples:          10000
+  evals/sample:     18
+
+julia> @benchmark vfilter(a -> a > 0.7, $x)
+BenchmarkTools.Trial:
+  memory estimate:  7.94 KiB
+  allocs estimate:  1
+  --------------
+  minimum time:     477.487 ns (0.00% GC)
+  median time:      575.166 ns (0.00% GC)
+  mean time:        711.526 ns (17.87% GC)
+  maximum time:     9.257 μs (79.17% GC)
+  --------------
+  samples:          10000
+  evals/sample:     193
+```
+
+
+
+