Citing (#152)

* citation.bib

* cleanup

Author: CarloLucibello

README.md

@@ -28,6 +28,20 @@ pkg> add GraphNeuralNetworks
 
 Usage examples can be found in the [examples](https://github.com/CarloLucibello/GraphNeuralNetworks.jl/tree/master/examples) folder. Also, make sure to read the [documentation](https://CarloLucibello.github.io/GraphNeuralNetworks.jl/dev) for a comprehensive introduction to the library.
 
+
+## Citing
+
+If you use GraphNeuralNetworks.jl in a scientific publication, we would appreciate the following reference:
+
+```
+@misc{Lucibello2021GNN,
+  author       = {Carlo Lucibello and other contributors},
+  title        = {GraphNeuralNetworks.jl: a geometric deep learning library for the Julia programming language},
+  year         = 2021,
+  url          = {https://github.com/CarloLucibello/GraphNeuralNetworks.jl}
+}
+```
+
 ## Acknowledgments
 
 GraphNeuralNetworks.jl is largely inspired by [PyTorch Geometric](https://pytorch-geometric.readthedocs.io/en/latest/), [Deep Graph Library](https://docs.dgl.ai/),

bench.jl

@@ -0,0 +1,37 @@
+##
+using GraphNeuralNetworks
+using CUDA
+using Flux
+
+N = 10000
+M = 1
+I = 10
+
+function test_mem(n, make_data)
+    for i in 1:I
+        g = make_data()
+        x = n(g |> gpu)
+        # b_g = Flux.batch([g for i in 1:M]) |> gpu
+        # x = n(b_g)
+        CUDA.memory_status()
+    end
+end
+
+GC.gc(); CUDA.reclaim(); GC.gc();
+CUDA.memory_status()
+
+println("GNN:")
+make_data() = GNNGraph(collect(1:N-1), collect(2:N), num_nodes = N, ndata = rand(1, N))
+n = GNNChain(Dense(1, 1000), Dense(1000, 1)) |> gpu
+# n = GCNConv(1 => 1000) |> gpu
+
+CUDA.@time test_mem(n, make_data)
+
+
+# GC.gc(); CUDA.reclaim(); GC.gc();
+# println("\n\nNN:")
+# make_data() = rand(3*N,1)
+# n = Chain(Dense(3*N, 1000), Dense(1000, 1)) |> gpu
+# CUDA.@time test_mem(n, make_data)
+
+println("################################")