Update action

.github/workflows/documentation.yml

@@ -16,7 +16,7 @@ jobs:
     steps:
       # checkout repository
       - name: Checkout cppuddle
-        uses: actions/checkout@v2
+        uses: actions/checkout@v4
         with:
           path: cppuddle
       # install dependencies

README.md

@@ -18,6 +18,8 @@ In this use-case, allocating GPU buffers for all sub-grids in advance would have
 - Executor pools and various scheduling policies (round robin, priority queue, multi-gpu), which rely on reference counting to gauge the current load of a executor instead of querying the device itself. Tested with CUDA, HIP and Kokkos executors provided by HPX / HPX-Kokkos.
 - Special Executors/Allocators for on-the-fly work GPU aggregation (using HPX).
 
+The documentation is available [here](https://sc-sgs.github.io/CPPuddle/index.html). In particular, the public functionality for the memory recycling in available in the namespace [memory_recycling](https://sc-sgs.github.io/CPPuddle/namespacecppuddle_1_1memory__recycling.html), for the executor pools it is available in the namespace [executor_recycling](https://sc-sgs.github.io/CPPuddle/namespacecppuddle_1_1executor__recycling.html) and the work aggregation (kernel fusion) functionality is available in the namespace [work_aggregation](https://sc-sgs.github.io/CPPuddle/namespacecppuddle_1_1kernel__aggregation.html).
+
 #### Requirements
 
 - C++17