update docs

Project.toml

@@ -1,7 +1,7 @@
 name = "GCMAES"
 uuid = "4aa9d100-eb0f-11e8-15f1-25748831eb3b"
 authors = ["Yao Lu <[email protected]>"]
-version = "0.1.5"
+version = "0.1.6"
 
 [deps]
 BSON = "fbb218c0-5317-5bc6-957e-2ee96dd4b1f0"

README.md

@@ -7,6 +7,13 @@ using Pkg
 pkg"add GCMAES"
 ```
 
+## Features
+
+- use low level BLAS operations to ensure performance
+- use `Elemental` to do distributed eigendecomposition, which is crutial for high dimensional (>10000) problem
+- compatible with `julia`'s native parallelism
+- compatible with `MPI.jl`, therefore suitable to be run on clusters without good TCP connections
+
 ## Usage
 
 ```julia
@@ -35,4 +42,23 @@ using ForwardDiff
 GCMAES.minimize((rastrigin, ∇rastrigin), x0, σ0, lo, hi, maxiter = 200)
 ```
 
-A checkpoint file named `CMAES.bson` will be created in the current working directory during optimization, which will be loaded back to initilize `CMAESOpt` if dimensions are equal.
+You can also enable `autodiff` and then `GCMAES` will internally use `Zygote` to do the gradient calculation
+
+```julia
+using Zygote
+GCMAES.minimize((rastrigin, ∇rastrigin), x0, σ0, lo, hi, maxiter = 200, autodiff = true)
+```
+
+A checkpoint file named `CMAES.bson` will be created in the current working directory during optimization, which will be loaded back to initilize `CMAESOpt` if dimensions are equal.
+
+## Parallel Usage
+
+Just simply add `@mpirun` before `GCMAES.minimize`
+
+```
+# ....
+@mpirun GCMAES.minimize(...)
+# ....
+```
+
+Then you can use `mpirun -n N julia ...` or `julia -p N ...` to start your job.

src/util.jl

@@ -80,6 +80,7 @@ macro mpirun(ex)
         MPI.Barrier(MPI.COMM_WORLD)
         $(esc(ex))
         MPI.Barrier(MPI.COMM_WORLD)
+        atexit(MPI.Finalize)
     end
 end