diff --git a/.gitea/workflows/julia-package-ci.yml b/.gitea/workflows/julia-package-ci.yml
index 10cf859..b7887e6 100644
--- a/.gitea/workflows/julia-package-ci.yml
+++ b/.gitea/workflows/julia-package-ci.yml
@@ -22,7 +22,9 @@ jobs:
           version: '1.9.2'
 
       - name: Instantiate
-        run: julia --project=./ -e 'using Pkg; Pkg.instantiate()'
+        run: |
+          julia --project=./ -e 'using Pkg; Pkg.instantiate()'
+          julia --project=./ -e 'using Pkg; Pkg.add(url="https://github.com/QEDjl-project/QEDprocesses.jl/")'
 
       - name: Format check
         run: |
@@ -32,7 +34,7 @@ jobs:
           if out == ""
             exit(0)
           else
-            @error "Some files have not been formatted !!!"
+            @error "Some files have not been formatted!!!"
             write(stdout, out)
             exit(1)
           end'
diff --git a/Project.toml b/Project.toml
index f1957b2..722043e 100644
--- a/Project.toml
+++ b/Project.toml
@@ -15,6 +15,7 @@ QEDbase = "10e22c08-3ccb-4172-bfcf-7d7aa3d04d93"
 QEDprocesses = "46de9c38-1bb3-4547-a1ec-da24d767fdad"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Roots = "f2b01f46-fcfa-551c-844a-d8ac1e96c665"
+StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
 
 [extras]
diff --git a/data/qed_ke-kke_reduction_optimizer.csv b/data/qed_ke-kke_reduction_optimizer.csv
new file mode 100644
index 0000000..794ca46
--- /dev/null
+++ b/data/qed_ke-kke_reduction_optimizer.csv
@@ -0,0 +1,16 @@
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diff --git a/data/qed_ke-kkke_greedy_optimizer_GlobalMetricEstimator().csv b/data/qed_ke-kkke_greedy_optimizer_GlobalMetricEstimator().csv
new file mode 100644
index 0000000..eb191bd
--- /dev/null
+++ b/data/qed_ke-kkke_greedy_optimizer_GlobalMetricEstimator().csv
@@ -0,0 +1,176 @@
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diff --git a/data/qed_ke-kkke_reduction_optimizer.csv b/data/qed_ke-kkke_reduction_optimizer.csv
new file mode 100644
index 0000000..bd41fbf
--- /dev/null
+++ b/data/qed_ke-kkke_reduction_optimizer.csv
@@ -0,0 +1,82 @@
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diff --git a/data/qed_ke-kkkkke_reduction_optimizer.csv b/data/qed_ke-kkkkke_reduction_optimizer.csv
new file mode 100644
index 0000000..9432121
--- /dev/null
+++ b/data/qed_ke-kkkkke_reduction_optimizer.csv
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diff --git a/examples/Project.toml b/examples/Project.toml
index ac0e043..0a5d779 100644
--- a/examples/Project.toml
+++ b/examples/Project.toml
@@ -1,3 +1,9 @@
 [deps]
 BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
+CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
+CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
+DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 MetagraphOptimization = "3e869610-d48d-4942-ba70-c1b702a33ca4"
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+QEDprocesses = "46de9c38-1bb3-4547-a1ec-da24d767fdad"
+StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd"
diff --git a/examples/qed_bench.jl b/examples/qed_bench.jl
new file mode 100644
index 0000000..67934fa
--- /dev/null
+++ b/examples/qed_bench.jl
@@ -0,0 +1,148 @@
+using MetagraphOptimization
+using LIKWID
+using CUDA
+using UUIDs
+
+function cpu_bench(compute_function, inputs)
+    compute_function.(inputs[begin:10]) # make sure it's compiled
+
+    time = @elapsed Threads.@threads for i in eachindex(inputs)
+        @invokelatest compute_function(inputs[i])
+    end
+    rate = length(inputs) / time
+    return (time, rate)
+end
+
+function gpu_bench(compute_function, inputs)
+    CUDA.@sync compute_function.(inputs[begin:10])  # make sure it's compiled
+
+    time = @elapsed CUDA.@sync compute_function.(inputs)
+    rate = length(inputs) / time
+
+    return (time, rate)
+end
+
+function bench_process(
+    process::MetagraphOptimization.AbstractProcessDescription,
+    func,
+    io::IO = stdout;
+    use_likwid = true,
+)
+    println(io, "\n--- Benchmarking $(process) ---")
+
+    NFLOPs = GraphProperties(graph).computeEffort
+    if use_likwid
+        input = gen_process_input(process)
+        func(input) # compile first
+        _, events = @perfmon "FLOPS_DP" func(input)
+        NFLOPs = first(events["FLOPS_DP"])["RETIRED_SSE_AVX_FLOPS_ALL"]
+    end
+
+    nInputs = 10000000  # ten million
+    println(io, "Generating $nInputs inputs with $(Threads.nthreads()) threads...")
+
+    inputs = Vector{typeof(gen_process_input(process))}()
+    resize!(inputs, nInputs)
+    processes = Vector{typeof(process)}()
+    for i in 1:Threads.nthreads()
+        push!(processes, copy(process))
+    end
+
+    Threads.@threads for i in eachindex(inputs)
+        inputs[i] = gen_process_input(processes[Threads.nthreads()])
+    end
+
+    println(io, "Benchmarking CPU with $(Threads.nthreads()) threads...")
+    (time_cpu, rate_cpu) = cpu_bench(func, inputs)
+    flops_cpu = (rate_cpu * NFLOPs) / 1024^3
+
+    println(io, "Benchmarking GPU...")
+    cuInputs = CuArray(inputs)
+    (time_gpu, rate_gpu) = gpu_bench(func, cuInputs)
+    flops_gpu = (rate_gpu * NFLOPs) / 1024^3
+
+    println(io, "\nBenchmark Summary for $(process):")
+
+    if use_likwid
+        println(io, "Measured FLOPS by LIKWID: $NFLOPs")
+    else
+        println(io, "Total graph compute effort: $NFLOPs")
+    end
+    println(io, "Total input size: $(bytes_to_human_readable(Base.summarysize(inputs)))")
+    println(io, "CPU, $(Threads.nthreads()) threads")
+    println(io, "  Time:  $time_cpu")
+    println(io, "  Rate:  $rate_cpu")
+    println(io, "  GFLOPS: $flops_cpu")
+    println(io, "GPU, $(name(first(CUDA.devices())))")
+    println(io, "  Time:  $time_gpu")
+    println(io, "  Rate:  $rate_gpu")
+    return println(io, "  GFLOPS: $flops_gpu")
+end
+
+# use "mock" machine that only uses cpu
+machine = Machine(
+    [
+        MetagraphOptimization.NumaNode(
+            0,
+            1,
+            MetagraphOptimization.default_strategy(MetagraphOptimization.NumaNode),
+            -1.0,
+            UUIDs.uuid1(),
+        ),
+    ],
+    [-1.0;;],
+)
+optimizer = ReductionOptimizer()
+
+# sadly cannot put these in functions because the world age must increase after the function is created which happens only in the global scope
+
+# compton
+process = parse_process("ke->ke", QEDModel())
+graph = gen_graph(process)
+optimize_to_fixpoint!(optimizer, graph)
+compute_func = get_compute_function(graph, process, machine)
+bench_process(process, compute_func)
+
+# 2-photon compton
+process = parse_process("ke->kke", QEDModel())
+graph = gen_graph(process)
+optimize_to_fixpoint!(optimizer, graph)
+compute_func = get_compute_function(graph, process, machine)
+bench_process(process, compute_func)
+
+# 3-photon compton
+process = parse_process("ke->kkke", QEDModel())
+graph = gen_graph(process)
+optimize_to_fixpoint!(optimizer, graph)
+compute_func = get_compute_function(graph, process, machine)
+bench_process(process, compute_func)
+
+# AB->AB
+process = parse_process("AB->AB", ABCModel())
+graph = parse_dag("input/AB->AB.txt", ABCModel())
+optimize_to_fixpoint!(optimizer, graph)
+compute_func = get_compute_function(graph, process, machine)
+bench_process(process, compute_func)
+
+# AB->AB^3
+process = parse_process("AB->ABBB", ABCModel())
+graph = parse_dag("input/AB->ABBB.txt", ABCModel())
+optimize_to_fixpoint!(optimizer, graph)
+compute_func = get_compute_function(graph, process, machine)
+bench_process(process, compute_func)
+
+exit(0)
+
+# 4-photon compton
+process = parse_process("ke->kkkke", QEDModel())
+graph = gen_graph(process)
+optimize_to_fixpoint!(optimizer, graph)
+compute_func = get_compute_function(graph, process, machine)
+bench_process(process, compute_func)
+
+# AB->AB^5
+process = parse_process("AB->ABBBBB", ABCModel())
+graph = parse_dag("input/AB->ABBBBB.txt", ABCModel())
+optimize_to_fixpoint!(optimizer, graph)
+compute_func = get_compute_function(graph, process, machine)
+bench_process(process, compute_func)
diff --git a/examples/reduction.ipynb b/examples/reduction.ipynb
new file mode 100644
index 0000000..cfa8674
--- /dev/null
+++ b/examples/reduction.ipynb
@@ -0,0 +1,542 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Threads: 32\n"
+     ]
+    }
+   ],
+   "source": [
+    "#using Pkg\n",
+    "#Pkg.add(url=\"https://github.com/QEDjl-project/QEDprocesses.jl/\")\n",
+    "\n",
+    "using MetagraphOptimization\n",
+    "using CUDA\n",
+    "using UUIDs\n",
+    "using BenchmarkTools\n",
+    "\n",
+    "println(\"Threads: $(Threads.nthreads())\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "compute__5aa84716_9ba0_11ee_31df_554757739f76 (generic function with 1 method)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# preparation of graph\n",
+    "machine = Machine([MetagraphOptimization.NumaNode(0, 1, MetagraphOptimization.default_strategy(MetagraphOptimization.NumaNode), -1.0, UUIDs.uuid1())], [-1.0;;])\n",
+    "model = QEDModel()\n",
+    "process = parse_process(\"ke->kkkkke\", model)\n",
+    "graph = gen_graph(process)\n",
+    "n_inputs = 10_000\n",
+    "inputs = [gen_process_input(process) for _ in 1:n_inputs]\n",
+    "cu_inputs = CuArray(inputs)\n",
+    "optimizer = ReductionOptimizer()\n",
+    "\n",
+    "get_compute_function(graph, process, machine) # run once for compilation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "bench (generic function with 1 method)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "function bench(func, inputs, cu_inputs)\n",
+    "    compile_time = @elapsed func(inputs[1])\n",
+    "\n",
+    "    single_thread = @elapsed func.(inputs)\n",
+    "    multi_threaded = @elapsed Threads.@threads for i in eachindex(inputs)\n",
+    "        func(inputs[i]) \n",
+    "    end\n",
+    "    \n",
+    "    gpu_compile = 0 #@elapsed CUDA.@sync func.(cu_inputs[1:2])\n",
+    "    gpu = 0 #@elapsed CUDA.@sync func.(cu_inputs)\n",
+    "    return (cpu_compile_time = compile_time, gpu_compile_time = gpu_compile, cpu_single_thread_time = single_thread, cpu_multi_thread_time = multi_threaded, gpu_time = gpu)\n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# bench and produce data\n",
+    "using DataFrames\n",
+    "\n",
+    "STEPSIZE = 10\n",
+    "n = 0\n",
+    "\n",
+    "df = DataFrame(operations=Int[], graph_nodes=Int[], graph_edges=Int[], graph_ce=Float64[], graph_dt=Float64[], graph_ci=Float64[], gen_func_t=Float64[], cpu_compile_t=Float64[], cpu_st_t=Float64[], cpu_mt_t=Float64[], gpu_compile_t=Float64[], gpu_t=Float64[])\n",
+    "\n",
+    "while true\n",
+    "    func_gen_time = @elapsed func = get_compute_function(graph, process, machine)\n",
+    "    res = bench(func, inputs, cu_inputs)\n",
+    "\n",
+    "    graph_properties = get_properties(graph)\n",
+    "    push!(df, (\n",
+    "        n,\n",
+    "        graph_properties.noNodes,\n",
+    "        graph_properties.noEdges,\n",
+    "        graph_properties.computeEffort,\n",
+    "        graph_properties.data,\n",
+    "        graph_properties.computeIntensity,\n",
+    "        func_gen_time,\n",
+    "        res.cpu_compile_time,\n",
+    "        res.cpu_single_thread_time,\n",
+    "        res.cpu_multi_thread_time,\n",
+    "        res.gpu_compile_time,\n",
+    "        res.gpu_time\n",
+    "    ))\n",
+    "\n",
+    "    if fixpoint_reached(optimizer, graph)\n",
+    "        break\n",
+    "    end\n",
+    "\n",
+    "    optimize!(optimizer, graph, STEPSIZE)\n",
+    "    n += STEPSIZE\n",
+    "end\n",
+    ";"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# plot data\n",
+    "using Plots\n",
+    "using StatsPlots\n",
+    "\n",
+    "img = @df df scatter(\n",
+    "    :operations, \n",
+    "    [:gen_func_t, :cpu_st_t, :cpu_mt_t], \n",
+    "    label=[\"Function generation (s)\" \"Single threaded execution (s)\" \"$(Threads.nthreads())-threaded execution (s)\"], \n",
+    "    title=\"$process using $optimizer ($(n_inputs) inputs)\",\n",
+    "    linewidth=2,\n",
+    "    xlabel=\"optimizer steps\",\n",
+    "    ylabel=\"time (s)\",\n",
+    "    yscale=:log10,\n",
+    "    minorgrid=true,\n",
+    "    size=(800, 600),\n",
+    "    fmt=:pdf\n",
+    ")\n",
+    "\n",
+    "savefig(img, \"../images/$(String(process))_exec_$(n_inputs)_inputs.pdf\")\n",
+    "\n",
+    "img"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "img = @df df scatter(\n",
+    "    :operations,\n",
+    "    [:graph_nodes, :graph_edges],\n",
+    "    label=[\"Graph Nodes (#)\" \"Graph Edges (#)\"],\n",
+    "    title=\"$process using $optimizer\",\n",
+    "    linewidth=2,\n",
+    "    xlabel=\"optimizer steps\",\n",
+    "    ylims=(0.0, 1.05 * maximum(df.graph_edges)),\n",
+    "    fmt=:pdf,\n",
+    "    size=(800, 600)\n",
+    ")\n",
+    "\n",
+    "savefig(img, \"../images/$(String(process))_graph_properties.pdf\")\n",
+    "\n",
+    "img"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Julia 1.9.4 (32 Threads)",
+   "language": "julia",
+   "name": "julia-1.9"
+  },
+  "language_info": {
+   "file_extension": ".jl",
+   "mimetype": "application/julia",
+   "name": "julia",
+   "version": "1.9.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/images/qed_ke-kke_exec_1000000_inputs.pdf b/images/qed_ke-kke_exec_1000000_inputs.pdf
new file mode 100644
index 0000000..c6d5b3b
Binary files /dev/null and b/images/qed_ke-kke_exec_1000000_inputs.pdf differ
diff --git a/images/qed_ke-kke_graph_properties.pdf b/images/qed_ke-kke_graph_properties.pdf
new file mode 100644
index 0000000..83fecf2
Binary files /dev/null and b/images/qed_ke-kke_graph_properties.pdf differ
diff --git a/images/qed_ke-kkke_exec_1000000_inputs.pdf b/images/qed_ke-kkke_exec_1000000_inputs.pdf
new file mode 100644
index 0000000..3ee95a4
Binary files /dev/null and b/images/qed_ke-kkke_exec_1000000_inputs.pdf differ
diff --git a/images/qed_ke-kkke_graph_properties.pdf b/images/qed_ke-kkke_graph_properties.pdf
new file mode 100644
index 0000000..fce3bff
Binary files /dev/null and b/images/qed_ke-kkke_graph_properties.pdf differ
diff --git a/images/qed_ke-kkke_greedy_optimizer_GlobalMetricEstimator()_exec_1000000_inputs.pdf b/images/qed_ke-kkke_greedy_optimizer_GlobalMetricEstimator()_exec_1000000_inputs.pdf
new file mode 100644
index 0000000..a617cb8
Binary files /dev/null and b/images/qed_ke-kkke_greedy_optimizer_GlobalMetricEstimator()_exec_1000000_inputs.pdf differ
diff --git a/images/qed_ke-kkke_greedy_optimizer_GlobalMetricEstimator()_graph_properties.pdf b/images/qed_ke-kkke_greedy_optimizer_GlobalMetricEstimator()_graph_properties.pdf
new file mode 100644
index 0000000..2602ec9
Binary files /dev/null and b/images/qed_ke-kkke_greedy_optimizer_GlobalMetricEstimator()_graph_properties.pdf differ
diff --git a/images/qed_ke-kkkke_exec_100000_inputs.pdf b/images/qed_ke-kkkke_exec_100000_inputs.pdf
new file mode 100644
index 0000000..095401c
Binary files /dev/null and b/images/qed_ke-kkkke_exec_100000_inputs.pdf differ
diff --git a/images/qed_ke-kkkke_graph_properties.pdf b/images/qed_ke-kkkke_graph_properties.pdf
new file mode 100644
index 0000000..fabbf22
Binary files /dev/null and b/images/qed_ke-kkkke_graph_properties.pdf differ
diff --git a/images/qed_ke-kkkkke_exec_10000_inputs.pdf b/images/qed_ke-kkkkke_exec_10000_inputs.pdf
new file mode 100644
index 0000000..4ee404f
Binary files /dev/null and b/images/qed_ke-kkkkke_exec_10000_inputs.pdf differ
diff --git a/images/qed_ke-kkkkke_graph_properties.pdf b/images/qed_ke-kkkkke_graph_properties.pdf
new file mode 100644
index 0000000..0e7639a
Binary files /dev/null and b/images/qed_ke-kkkkke_graph_properties.pdf differ
diff --git a/notebooks/abc_model_showcase.ipynb b/notebooks/abc_model_showcase.ipynb
index 771c26b..865c4a3 100644
--- a/notebooks/abc_model_showcase.ipynb
+++ b/notebooks/abc_model_showcase.ipynb
@@ -391,7 +391,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Julia 1.9.3",
+   "display_name": "Julia 1.9.4",
    "language": "julia",
    "name": "julia-1.9"
   },
@@ -399,7 +399,7 @@
    "file_extension": ".jl",
    "mimetype": "application/julia",
    "name": "julia",
-   "version": "1.9.3"
+   "version": "1.9.4"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/large_compton.ipynb b/notebooks/large_compton.ipynb
index 7845ece..f5dc873 100644
--- a/notebooks/large_compton.ipynb
+++ b/notebooks/large_compton.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -31,13 +31,13 @@
      "data": {
       "text/plain": [
        "Graph:\n",
-       "  Nodes: Total: 131069, DataTask: 65539, ComputeTaskQED_Sum: 1, \n",
-       "         ComputeTaskQED_V: 35280, ComputeTaskQED_S2: 5040, ComputeTaskQED_U: 9, \n",
-       "         ComputeTaskQED_S1: 25200\n",
-       "  Edges: 176419\n",
-       "  Total Compute Effort: 549370.0\n",
-       "  Total Data Transfer: 1.0645344e7\n",
-       "  Total Compute Intensity: 0.05160659909158408\n"
+       "  Nodes: Total: 15866, DataTask: 7937, ComputeTaskQED_S2: 720, \n",
+       "         ComputeTaskQED_Sum: 1, ComputeTaskQED_V: 4320, ComputeTaskQED_S1: 2880, \n",
+       "         ComputeTaskQED_U: 8\n",
+       "  Edges: 21617\n",
+       "  Total Compute Effort: 66249.0\n",
+       "  Total Data Transfer: 1.314048e6\n",
+       "  Total Compute Intensity: 0.050415966540035065\n"
       ]
      },
      "metadata": {},
@@ -47,7 +47,7 @@
    "source": [
     "machine = get_machine_info()\n",
     "model = QEDModel()\n",
-    "process = parse_process(\"ke->kkkkkke\", model)\n",
+    "process = parse_process(\"ke->kkkkke\", model)\n",
     "\n",
     "inputs = [gen_process_input(process) for _ in 1:1e3];\n",
     "graph = gen_graph(process)"
@@ -62,13 +62,13 @@
      "data": {
       "text/plain": [
        "Graph:\n",
-       "  Nodes: Total: 14783, DataTask: 7396, ComputeTaskQED_Sum: 1, \n",
-       "         ComputeTaskQED_V: 1819, ComputeTaskQED_S2: 5040, ComputeTaskQED_U: 9, \n",
-       "         ComputeTaskQED_S1: 518\n",
-       "  Edges: 26672\n",
-       "  Total Compute Effort: 77102.0\n",
-       "  Total Data Transfer: 5.063616e6\n",
-       "  Total Compute Intensity: 0.015226668056977465\n"
+       "  Nodes: Total: 2234, DataTask: 1121, ComputeTaskQED_S2: 720, \n",
+       "         ComputeTaskQED_Sum: 1, ComputeTaskQED_V: 312, ComputeTaskQED_S1: 72, \n",
+       "         ComputeTaskQED_U: 8\n",
+       "  Edges: 3977\n",
+       "  Total Compute Effort: 11313.0\n",
+       "  Total Data Transfer: 659712.0\n",
+       "  Total Compute Intensity: 0.017148392025611175\n"
       ]
      },
      "metadata": {},
@@ -78,6 +78,7 @@
    "source": [
     "optimizer = ReductionOptimizer()\n",
     "\n",
+    "compute_compton = get_compute_function(graph, process, machine)\n",
     "optimize_to_fixpoint!(optimizer, graph)\n",
     "graph"
    ]
@@ -91,7 +92,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Calculated 15537.0 results/s, 1295.0 results/s per thread for QED Process: 'ke->kkkkkke' (12 threads)\n"
+      "Calculated 133942.0 results/s, 11162.0 results/s per thread for QED Process: 'ke->kkkkke' (12 threads)\n"
      ]
     }
    ],
@@ -109,6 +110,31 @@
     "rate_per_thread = rate / Threads.nthreads()\n",
     "println(\"Calculated $(round(rate)) results/s, $(round(rate_per_thread)) results/s per thread for $(process) ($(Threads.nthreads()) threads)\")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculated 17124.0 results/s, 1427.0 results/s per thread for QED Process: 'ke->kkkkke' (12 threads)\n"
+     ]
+    }
+   ],
+   "source": [
+    "bench_result = @benchmark begin\n",
+    "    Threads.@threads :static for i in eachindex(inputs)\n",
+    "        outputs[i] = compute_compton(inputs[i])\n",
+    "    end\n",
+    "end\n",
+    "\n",
+    "rate = length(inputs) / (mean(bench_result.times) / 1.0e9)\n",
+    "rate_per_thread = rate / Threads.nthreads()\n",
+    "println(\"Calculated $(round(rate)) results/s, $(round(rate_per_thread)) results/s per thread for $(process) ($(Threads.nthreads()) threads)\")"
+   ]
   }
  ],
  "metadata": {
diff --git a/results/FWK8999_QED.txt b/results/FWK8999_QED.txt
new file mode 100644
index 0000000..deaebe9
--- /dev/null
+++ b/results/FWK8999_QED.txt
@@ -0,0 +1,62 @@
+CPU: AMD EPYC 7452 32 Cores, 64 Threads | 122.8 GFLOPS (?, source: https://www.cpubenchmark.net/cpu.php?cpu=AMD+EPYC+7452)
+GPU: A30 24GB                           | 5.161 TFLOPS (source: https://www.techpowerup.com/gpu-specs/a30-pcie.c3792)
+
+Benchmark Summary for QED Process: 'ke->ke':
+Measured FLOPS by LIKWID: 5657.0
+Total input size: 1.394 GiB
+CPU, 64 threads
+  Time:  0.594810558
+  Rate:  1.681207548437632e7
+  GFLOPS: 88.57428190774921
+GPU, NVIDIA A30
+  Time:  1.547648257
+  Rate:  6.461416510353748e6
+  GFLOPS: 34.041919930904314
+
+Benchmark Summary for QED Process: 'ke->kke':
+Measured FLOPS by LIKWID: 16256.0
+Total input size: 1.768 GiB
+CPU, 64 threads
+  Time:  1.294064702
+  Rate:  7.7275888790914565e6
+  GFLOPS: 116.99244828756034
+GPU, NVIDIA A30
+  Time:  4.973188906
+  Rate:  2.0107822544072892e6
+  GFLOPS: 30.442398346629826
+
+Benchmark Summary for QED Process: 'ke->kkke':
+Measured FLOPS by LIKWID: 43433.0
+Total input size: 2.632 GiB
+CPU, 64 threads
+  Time:  3.232029091
+  Rate:  3.094031556784648e6
+  GFLOPS: 125.15398916399816
+GPU, NVIDIA A30
+  Time:  14.597070187
+  Rate:  685068.9810963502
+  GFLOPS: 27.711131662091034
+
+Benchmark Summary for ABC Process: 'AB->AB':
+Measured FLOPS by LIKWID: 41.0
+Total input size: 2.201 GiB
+CPU, 64 threads
+  Time:  0.688079611
+  Rate:  1.453320203089116e7
+  GFLOPS: 0.5549390644454747
+GPU, NVIDIA A30
+  Time:  0.013803574
+  Rate:  7.244500590933913e8
+  GFLOPS: 27.662564462822903
+
+Benchmark Summary for ABC Process: 'AB->ABBB':
+Measured FLOPS by LIKWID: 899.0
+Total input size: 3.079 GiB
+CPU, 64 threads
+  Time:  0.855687624
+  Rate:  1.1686507692204276e7
+  GFLOPS: 9.784633680518386
+GPU, NVIDIA A30
+  Time:  0.014804518
+  Rate:  6.754694749265056e8
+  GFLOPS: 565.542893445984
diff --git a/src/code_gen/main.jl b/src/code_gen/main.jl
index 3b8b24b..94ba03a 100644
--- a/src/code_gen/main.jl
+++ b/src/code_gen/main.jl
@@ -62,21 +62,12 @@ function gen_input_assignment_code(
     assignInputs = Vector{Expr}()
     for (name, symbols) in inputSymbols
         (type, index) = type_index_from_name(model(processDescription), name)
-        p = nothing
-
-        if (index > get(in_particles(processDescription), type, 0))
-            index -= get(in_particles(processDescription), type, 0)
-            @assert index <= out_particles(processDescription)[type] "Too few particles of type $type in input particles for this process"
-
-            p = "filter(x -> typeof(x) <: $type, out_particles($(processInputSymbol)))[$(index)]"
-        else
-            p = "filter(x -> typeof(x) <: $type, in_particles($(processInputSymbol)))[$(index)]"
-        end
+        p = "get_particle($(processInputSymbol), $(type), $(index))"
 
         for symbol in symbols
             device = entry_device(machine)
             evalExpr = eval(gen_access_expr(device, symbol))
-            push!(assignInputs, Meta.parse("$(evalExpr)::ParticleValue{$type} = ParticleValue($p, one(ComplexF64))"))
+            push!(assignInputs, Meta.parse("$(evalExpr) = ParticleValue{$type, ComplexF64}($p, one(ComplexF64))"))
         end
     end
 
@@ -111,10 +102,12 @@ end
 Execute the code of the given `graph` on the given input particles.
 
 This is essentially shorthand for
-    ```julia
-    compute_graph = get_compute_function(graph, process)
-    result = compute_graph(particles)
-    ```
+```julia
+compute_graph = get_compute_function(graph, process)
+result = compute_graph(particles)
+```
+If an exception occurs during the execution of the generated code, it will be printed for investigation.
+
 
 See also: [`parse_dag`](@ref), [`parse_process`](@ref), [`gen_process_input`](@ref)
 """
@@ -135,6 +128,8 @@ function execute(graph::DAG, process::AbstractProcessDescription, machine::Machi
     result = 0
     try
         result = @eval $func($input)
+        #functionStr = string(expr)
+        #println("Function:\n$functionStr")
     catch e
         println("Error while evaluating: $e")
 
diff --git a/src/estimator/global_metric.jl b/src/estimator/global_metric.jl
index d83a450..3e0a55e 100644
--- a/src/estimator/global_metric.jl
+++ b/src/estimator/global_metric.jl
@@ -75,3 +75,7 @@ function operation_effect(estimator::GlobalMetricEstimator, graph::DAG, operatio
     ce::Float64 = s * compute_effort(task(operation.input))
     return (data = d, computeEffort = ce, computeIntensity = ce / d)::CDCost
 end
+
+function String(::GlobalMetricEstimator)
+    return "global_metric"
+end
diff --git a/src/models/abc/compute.jl b/src/models/abc/compute.jl
index 6f1857d..8315c60 100644
--- a/src/models/abc/compute.jl
+++ b/src/models/abc/compute.jl
@@ -1,4 +1,5 @@
 using AccurateArithmetic
+using StaticArrays
 
 """
     compute(::ComputeTaskABC_P, data::ABCParticleValue)
@@ -75,14 +76,14 @@ function compute(::ComputeTaskABC_S1, data::ABCParticleValue{P})::ABCParticleVal
 end
 
 """
-    compute(::ComputeTaskABC_Sum, data::Vector{Float64})
+    compute(::ComputeTaskABC_Sum, data::StaticVector)
 
 Compute a sum over the vector. Use an algorithm that accounts for accumulated errors in long sums with potentially large differences in magnitude of the summands.
 
 Linearly many FLOP with growing data.
 """
-function compute(::ComputeTaskABC_Sum, data::Vector{Float64})::Float64
-    return sum_kbn(data)
+function compute(::ComputeTaskABC_Sum, data::StaticVector)::Float64
+    return sum(data)
 end
 
 """
@@ -159,5 +160,7 @@ function get_expression(::ComputeTaskABC_Sum, device::AbstractDevice, inExprs::V
     in = eval.(inExprs)
     out = eval(outExpr)
 
-    return Meta.parse("$out = compute(ComputeTaskABC_Sum(), [$(unroll_symbol_vector(in))])")
+    return Meta.parse(
+        "$out = compute(ComputeTaskABC_Sum(), SVector{$(length(inExprs)), Float64}($(unroll_symbol_vector(in))))",
+    )
 end
diff --git a/src/models/abc/create.jl b/src/models/abc/create.jl
index 0d5f844..ecc0126 100644
--- a/src/models/abc/create.jl
+++ b/src/models/abc/create.jl
@@ -5,6 +5,20 @@ using ForwardDiff
 
 ComputeTaskABC_Sum() = ComputeTaskABC_Sum(0)
 
+function _svector_from_type_in(processDescription::ABCProcessDescription, type, particles)
+    if haskey(in_particles(processDescription), type)
+        return SVector{in_particles(processDescription)[type], type}(filter(x -> typeof(x) <: type, particles))
+    end
+    return SVector{0, type}()
+end
+
+function _svector_from_type_out(processDescription::ABCProcessDescription, type, particles)
+    if haskey(out_particles(processDescription), type)
+        return SVector{out_particles(processDescription)[type], type}(filter(x -> typeof(x) <: type, particles))
+    end
+    return SVector{0, type}()
+end
+
 """
     gen_process_input(processDescription::ABCProcessDescription)
 
@@ -58,7 +72,15 @@ function gen_process_input(processDescription::ABCProcessDescription)
         end
     end
 
-    processInput = ABCProcessInput(processDescription, inputParticles, outputParticles)
+    inA = _svector_from_type_in(processDescription, ParticleA, inputParticles)
+    inB = _svector_from_type_in(processDescription, ParticleB, inputParticles)
+    inC = _svector_from_type_in(processDescription, ParticleC, inputParticles)
+
+    outA = _svector_from_type_out(processDescription, ParticleA, outputParticles)
+    outB = _svector_from_type_out(processDescription, ParticleB, outputParticles)
+    outC = _svector_from_type_out(processDescription, ParticleC, outputParticles)
+
+    processInput = ABCProcessInput(processDescription, inA, inB, inC, outA, outB, outC)
 
     return return processInput
 end
diff --git a/src/models/abc/particle.jl b/src/models/abc/particle.jl
index 8769eaf..d91bc0b 100644
--- a/src/models/abc/particle.jl
+++ b/src/models/abc/particle.jl
@@ -1,3 +1,5 @@
+using StaticArrays
+
 import QEDbase.mass
 
 """
@@ -60,27 +62,30 @@ Input for a ABC Process. Contains the [`ABCProcessDescription`](@ref) of the pro
 
 See also: [`gen_process_input`](@ref)
 """
-struct ABCProcessInput <: AbstractProcessInput
+struct ABCProcessInput{N1, N2, N3, N4, N5, N6} <: AbstractProcessInput
     process::ABCProcessDescription
-    inParticles::Vector{ABCParticle}
-    outParticles::Vector{ABCParticle}
+    inA::SVector{N1, ParticleA}
+    inB::SVector{N2, ParticleB}
+    inC::SVector{N3, ParticleC}
+    outA::SVector{N4, ParticleA}
+    outB::SVector{N5, ParticleB}
+    outC::SVector{N6, ParticleC}
 end
 
 ABCParticleValue{ParticleType <: ABCParticle} = ParticleValue{ParticleType, ComplexF64}
 
-"""
-    PARTICLE_MASSES
-
-A constant dictionary containing the masses of the different [`ABCParticle`](@ref)s.
-"""
-const PARTICLE_MASSES = Dict{Type, Float64}(ParticleA => 1.0, ParticleB => 1.0, ParticleC => 0.0)
-
 """
     mass(t::Type{T}) where {T <: ABCParticle}
     
 Return the mass (at rest) of the given particle type.
 """
-mass(t::Type{T}) where {T <: ABCParticle} = PARTICLE_MASSES[t]
+mass(::ParticleA) = 1.0
+mass(::ParticleB) = 1.0
+mass(::ParticleC) = 0.0
+
+mass(::Type{ParticleA}) = 1.0
+mass(::Type{ParticleB}) = 1.0
+mass(::Type{ParticleC}) = 0.0
 
 """
     interaction_result(t1::Type{T1}, t2::Type{T2}) where {T1 <: ABCParticle, T2 <: ABCParticle}
@@ -126,7 +131,7 @@ Return the factor of the inner edge with the given (virtual) particle.
 Takes 10 effective FLOP. (3 here + 7 in square(p))
 """
 function ABC_inner_edge(p::ABCParticle)
-    return 1.0 / (square(p) - mass(typeof(p)) * mass(typeof(p)))
+    return 1.0 / (square(p) - mass(p)^2)
 end
 
 """
@@ -166,6 +171,10 @@ function ABC_conserve_momentum(p1::ABCParticle, p2::ABCParticle)
     return p3
 end
 
+function copy(process::ABCProcessDescription)
+    return ABCProcessDescription(copy(process.inParticles), copy(process.outParticles))
+end
+
 model(::ABCProcessDescription) = ABCModel()
 model(::ABCProcessInput) = ABCModel()
 
@@ -195,14 +204,29 @@ function in_particles(process::ABCProcessDescription)
     return process.inParticles
 end
 
-function in_particles(input::ABCProcessInput)
-    return input.inParticles
-end
-
 function out_particles(process::ABCProcessDescription)
     return process.outParticles
 end
 
-function out_particles(input::ABCProcessInput)
-    return input.outParticles
+function get_particle(input::ABCProcessInput, t::Type{Particle}, n::Int)::Particle where {Particle}
+    if (t <: ParticleA)
+        if (n > length(input.inA))
+            return input.outA[n - length(input.inA)]
+        else
+            return input.inA[n]
+        end
+    elseif (t <: ParticleB)
+        if (n > length(input.inB))
+            return input.outB[n - length(input.inB)]
+        else
+            return input.inB[n]
+        end
+    elseif (t <: ParticleC)
+        if (n > length(input.inC))
+            return input.outC[n - length(input.inC)]
+        else
+            return input.inC[n]
+        end
+    end
+    @assert false "Invalid type given"
 end
diff --git a/src/models/abc/print.jl b/src/models/abc/print.jl
index 26bfb39..9ae692a 100644
--- a/src/models/abc/print.jl
+++ b/src/models/abc/print.jl
@@ -36,15 +36,26 @@ Pretty print an [`ABCProcessInput`](@ref) (with newlines).
 """
 function show(io::IO, processInput::ABCProcessInput)
     println(io, "Input for $(processInput.process):")
-    println(io, "  $(length(processInput.inParticles)) Incoming particles:")
-    for particle in processInput.inParticles
-        println(io, "    $particle")
+    println(io, "Incoming particles:")
+    if !isempty(processInput.inA)
+        println(io, "  $(processInput.inA)")
     end
-    println(io, "  $(length(processInput.outParticles)) Outgoing Particles:")
-    for particle in processInput.outParticles
-        println(io, "    $particle")
+    if !isempty(processInput.inB)
+        println(io, "  $(processInput.inB)")
+    end
+    if !isempty(processInput.inC)
+        println(io, "  $(processInput.inC)")
+    end
+    println(io, "Outgoing particles:")
+    if !isempty(processInput.outA)
+        println(io, "  $(processInput.outA)")
+    end
+    if !isempty(processInput.outB)
+        println(io, "  $(processInput.outB)")
+    end
+    if !isempty(processInput.outC)
+        println(io, "  $(processInput.outC)")
     end
-    return nothing
 end
 
 """
diff --git a/src/models/interface.jl b/src/models/interface.jl
index ca30152..60e6d5e 100644
--- a/src/models/interface.jl
+++ b/src/models/interface.jl
@@ -80,6 +80,14 @@ Returns a `<: Vector{AbstractParticle}` object with the values of all outgoing p
 """
 function out_particles end
 
+"""
+    get_particle(::AbstractProcessInput, t::Type, n::Int)
+
+Interface function that must be implemented for every subtype of [`AbstractProcessInput`](@ref).
+Returns the `n`th particle of type `t`.
+"""
+function get_particle end
+
 """
     parse_process(::AbstractString, ::AbstractPhysicsModel)
 
diff --git a/src/models/qed/compute.jl b/src/models/qed/compute.jl
index 1225160..166529a 100644
--- a/src/models/qed/compute.jl
+++ b/src/models/qed/compute.jl
@@ -1,12 +1,13 @@
+using StaticArrays
 
 """
     compute(::ComputeTaskQED_P, data::QEDParticleValue)
 
 Return the particle as is and initialize the Value.
 """
-function compute(::ComputeTaskQED_P, data::QEDParticleValue{P})::QEDParticleValue{P} where {P <: QEDParticle}
+function compute(::ComputeTaskQED_P, data::QEDParticleValue{P}) where {P <: QEDParticle}
     # TODO do we actually need this for anything?
-    return QEDParticleValue{P}(data.p, one(DiracMatrix))
+    return ParticleValue{P, DiracMatrix}(data.p, one(DiracMatrix))
 end
 
 """
@@ -15,7 +16,8 @@ end
 Compute an outer edge. Return the particle value with the same particle and the value multiplied by an outer_edge factor.
 """
 function compute(::ComputeTaskQED_U, data::PV) where {P <: QEDParticle, PV <: QEDParticleValue{P}}
-    state = base_state(particle(data.p), direction(data.p), momentum(data.p), spin_or_pol(data.p))
+    part::P = data.p
+    state = base_state(particle(part), direction(part), momentum(part), spin_or_pol(part))
     return ParticleValue{P, typeof(state)}(
         data.p,
         state, # will return a SLorentzVector{ComplexF64}, BiSpinor or AdjointBiSpinor
@@ -34,7 +36,6 @@ function compute(
 ) where {P1 <: QEDParticle, P2 <: QEDParticle, PV1 <: QEDParticleValue{P1}, PV2 <: QEDParticleValue{P2}}
     p3 = QED_conserve_momentum(data1.p, data2.p)
     P3 = interaction_result(P1, P2)
-
     state = QED_vertex()
     if (typeof(data1.v) <: AdjointBiSpinor)
         state = data1.v * state
@@ -47,7 +48,7 @@ function compute(
         state = state * data2.v
     end
 
-    dataOut = ParticleValue{P3, typeof(state)}(P3(p3), state)
+    dataOut = ParticleValue{P3, typeof(state)}(P3(momentum(p3)), state)
     return dataOut
 end
 
@@ -64,13 +65,10 @@ function compute(
     ::ComputeTaskQED_S2,
     data1::ParticleValue{P1},
     data2::ParticleValue{P2},
-)::ComplexF64 where {
-    P1 <: Union{AntiFermionStateful, FermionStateful},
-    P2 <: Union{AntiFermionStateful, FermionStateful},
-}
-    @assert isapprox(data1.p.momentum, data2.p.momentum, rtol = sqrt(eps()), atol = sqrt(eps())) "$(data1.p.momentum) vs. $(data2.p.momentum)"
+) where {P1 <: Union{AntiFermionStateful, FermionStateful}, P2 <: Union{AntiFermionStateful, FermionStateful}}
+    #@assert isapprox(data1.p.momentum, data2.p.momentum, rtol = sqrt(eps()), atol = sqrt(eps())) "$(data1.p.momentum) vs. $(data2.p.momentum)"
 
-    inner = QED_inner_edge(propagation_result(P1)(data1.p))
+    inner = QED_inner_edge(propagation_result(P1)(momentum(data1.p)))
 
     # inner edge is just a "scalar", data1 and data2 are bispinor/adjointbispinnor, need to keep correct order
     if typeof(data1.v) <: BiSpinor
@@ -80,12 +78,11 @@ function compute(
     end
 end
 
-# TODO: S2 when the particles are photons?
 function compute(
     ::ComputeTaskQED_S2,
     data1::ParticleValue{P1},
     data2::ParticleValue{P2},
-)::ComplexF64 where {P1 <: PhotonStateful, P2 <: PhotonStateful}
+) where {P1 <: PhotonStateful, P2 <: PhotonStateful}
     # TODO: assert that data1 and data2 are opposites
     inner = QED_inner_edge(data1.p)
     # inner edge is just a scalar, data1 and data2 are photon states that are just Complex numbers here
@@ -97,9 +94,9 @@ end
 
 Compute inner edge (1 input particle, 1 output particle).
 """
-function compute(::ComputeTaskQED_S1, data::QEDParticleValue{P})::QEDParticleValue where {P <: QEDParticle}
+function compute(::ComputeTaskQED_S1, data::QEDParticleValue{P}) where {P <: QEDParticle}
     newP = propagation_result(P)
-    new_p = newP(data.p)
+    new_p = newP(momentum(data.p))
     # inner edge is just a scalar, can multiply from either side
     if typeof(data.v) <: BiSpinor
         return ParticleValue(new_p, QED_inner_edge(new_p) * data.v)
@@ -109,13 +106,13 @@ function compute(::ComputeTaskQED_S1, data::QEDParticleValue{P})::QEDParticleVal
 end
 
 """
-    compute(::ComputeTaskQED_Sum, data::Vector{ComplexF64})
+    compute(::ComputeTaskQED_Sum, data::StaticVector)
 
 Compute a sum over the vector. Use an algorithm that accounts for accumulated errors in long sums with potentially large differences in magnitude of the summands.
 
 Linearly many FLOP with growing data.
 """
-function compute(::ComputeTaskQED_Sum, data::Vector{ComplexF64})::ComplexF64
+function compute(::ComputeTaskQED_Sum, data::StaticVector)::ComplexF64
     # TODO: want to use sum_kbn here but it doesn't seem to support ComplexF64, do it element-wise?
     return sum(data)
 end
@@ -194,5 +191,7 @@ function get_expression(::ComputeTaskQED_Sum, device::AbstractDevice, inExprs::V
     in = eval.(inExprs)
     out = eval(outExpr)
 
-    return Meta.parse("$out = compute(ComputeTaskQED_Sum(), [$(unroll_symbol_vector(in))])")
+    return Meta.parse(
+        "$out = compute(ComputeTaskQED_Sum(), SVector{$(length(inExprs)), ComplexF64}($(unroll_symbol_vector(in))))",
+    )
 end
diff --git a/src/models/qed/create.jl b/src/models/qed/create.jl
index baf73f2..899e76d 100644
--- a/src/models/qed/create.jl
+++ b/src/models/qed/create.jl
@@ -1,6 +1,16 @@
 
 ComputeTaskQED_Sum() = ComputeTaskQED_Sum(0)
 
+function _svector_from_type(processDescription::QEDProcessDescription, type, particles)
+    if haskey(in_particles(processDescription), type)
+        return SVector{in_particles(processDescription)[type], type}(filter(x -> typeof(x) <: type, particles))
+    end
+    if haskey(out_particles(processDescription), type)
+        return SVector{out_particles(processDescription)[type], type}(filter(x -> typeof(x) <: type, particles))
+    end
+    return SVector{0, type}()
+end
+
 """
     gen_process_input(processDescription::QEDProcessDescription)
 
@@ -29,30 +39,37 @@ function gen_process_input(processDescription::QEDProcessDescription)
     massSum += rand(rng[threadid()]) * (length(inputMasses) + length(outputMasses))
 
 
-    inputParticles = Vector{QEDParticle}()
+    particles = Vector{QEDParticle}()
     initialMomenta = generate_initial_moms(massSum, inputMasses)
     index = 1
     for (particle, n) in processDescription.inParticles
         for _ in 1:n
             mom = initialMomenta[index]
-            push!(inputParticles, particle(mom))
+            push!(particles, particle(mom))
             index += 1
         end
     end
 
-    outputParticles = Vector{QEDParticle}()
     final_momenta = generate_physical_massive_moms(rng[threadid()], massSum, outputMasses)
     index = 1
     for (particle, n) in processDescription.outParticles
         for _ in 1:n
-            push!(outputParticles, particle(final_momenta[index]))
+            push!(particles, particle(final_momenta[index]))
             index += 1
         end
     end
 
-    processInput = QEDProcessInput(processDescription, inputParticles, outputParticles)
+    inFerms = _svector_from_type(processDescription, FermionStateful{Incoming, SpinUp}, particles)
+    outFerms = _svector_from_type(processDescription, FermionStateful{Outgoing, SpinUp}, particles)
+    inAntiferms = _svector_from_type(processDescription, AntiFermionStateful{Incoming, SpinUp}, particles)
+    outAntiferms = _svector_from_type(processDescription, AntiFermionStateful{Outgoing, SpinUp}, particles)
+    inPhotons = _svector_from_type(processDescription, PhotonStateful{Incoming, PolX}, particles)
+    outPhotons = _svector_from_type(processDescription, PhotonStateful{Outgoing, PolX}, particles)
 
-    return return processInput
+    processInput =
+        QEDProcessInput(processDescription, inFerms, outFerms, inAntiferms, outAntiferms, inPhotons, outPhotons)
+
+    return processInput
 end
 
 """
diff --git a/src/models/qed/diagrams.jl b/src/models/qed/diagrams.jl
index eb207a4..073fe4e 100644
--- a/src/models/qed/diagrams.jl
+++ b/src/models/qed/diagrams.jl
@@ -82,7 +82,7 @@ end
 
 function particle_after_tie(p::FeynmanParticle, t::FeynmanTie)
     if p == t.in1 || p == t.in2
-        return FeynmanParticle(FermionStateful{Incoming}, -1) # placeholder particle and id for tied particles
+        return FeynmanParticle(FermionStateful{Incoming, SpinUp}, -1) # placeholder particle and id for tied particles
     end
     return p
 end
diff --git a/src/models/qed/particle.jl b/src/models/qed/particle.jl
index 208a5ae..dfdbb99 100644
--- a/src/models/qed/particle.jl
+++ b/src/models/qed/particle.jl
@@ -1,4 +1,5 @@
 using QEDprocesses
+using StaticArrays
 import QEDbase.mass
 
 # TODO check
@@ -34,19 +35,6 @@ struct QEDProcessDescription <: AbstractProcessDescription
     outParticles::Dict{Type{<:QEDParticle{Outgoing}}, Int}
 end
 
-"""
-    QEDProcessInput <: AbstractProcessInput
-
-Input for a QED Process. Contains the [`QEDProcessDescription`](@ref) of the process it is an input for, and the values of the in and out particles.
-
-See also: [`gen_process_input`](@ref)
-"""
-struct QEDProcessInput <: AbstractProcessInput
-    process::QEDProcessDescription
-    inParticles::Vector{QEDParticle}
-    outParticles::Vector{QEDParticle}
-end
-
 QEDParticleValue{ParticleType <: QEDParticle} = Union{
     ParticleValue{ParticleType, BiSpinor},
     ParticleValue{ParticleType, AdjointBiSpinor},
@@ -60,51 +48,44 @@ QEDParticleValue{ParticleType <: QEDParticle} = Union{
 
 A photon of the [`QEDModel`](@ref) with its state.
 """
-struct PhotonStateful{Direction <: ParticleDirection} <: QEDParticle{Direction}
+struct PhotonStateful{Direction <: ParticleDirection, Pol <: AbstractDefinitePolarization} <: QEDParticle{Direction}
     momentum::SFourMomentum
-    # this will maybe change to the full polarization vector? or do i need both
-    polarization::AbstractDefinitePolarization
 end
 
 PhotonStateful{Direction}(mom::SFourMomentum) where {Direction <: ParticleDirection} =
-    PhotonStateful{Direction}(mom, PolX()) # TODO: make allpol possible
+    PhotonStateful{Direction, PolX}(mom)
 
-PhotonStateful{Dir1}(ph::PhotonStateful{Dir2}) where {Dir1 <: ParticleDirection, Dir2 <: ParticleDirection} =
-    PhotonStateful{Dir1}(ph.momentum, ph.polarization)
+PhotonStateful{Dir, Pol}(ph::PhotonStateful) where {Dir, Pol} = PhotonStateful{Dir, Pol}(ph.momentum)
 
 """
     FermionStateful <: QEDParticle
 
 A fermion of the [`QEDModel`](@ref) with its state.
 """
-struct FermionStateful{Direction <: ParticleDirection} <: QEDParticle{Direction}
+struct FermionStateful{Direction <: ParticleDirection, Spin <: AbstractDefiniteSpin} <: QEDParticle{Direction}
     momentum::SFourMomentum
-    spin::AbstractDefiniteSpin
     # TODO: mass for electron/muon/tauon representation?
 end
 
 FermionStateful{Direction}(mom::SFourMomentum) where {Direction <: ParticleDirection} =
-    FermionStateful{Direction}(mom, SpinUp()) # TODO: make allspin possible
+    FermionStateful{Direction, SpinUp}(mom)
 
-FermionStateful{Dir1}(f::FermionStateful{Dir2}) where {Dir1 <: ParticleDirection, Dir2 <: ParticleDirection} =
-    FermionStateful{Dir1}(f.momentum, f.spin)
+FermionStateful{Dir, Spin}(f::FermionStateful) where {Dir, Spin} = FermionStateful{Dir, Spin}(f.momentum)
 
 """
     AntiFermionStateful <: QEDParticle
 
 An anti-fermion of the [`QEDModel`](@ref) with its state.
 """
-struct AntiFermionStateful{Direction <: ParticleDirection} <: QEDParticle{Direction}
+struct AntiFermionStateful{Direction <: ParticleDirection, Spin <: AbstractDefiniteSpin} <: QEDParticle{Direction}
     momentum::SFourMomentum
-    spin::AbstractDefiniteSpin
     # TODO: mass for electron/muon/tauon representation?
 end
 
 AntiFermionStateful{Direction}(mom::SFourMomentum) where {Direction <: ParticleDirection} =
-    AntiFermionStateful{Direction}(mom, SpinUp()) # TODO: make allspin possible
+    AntiFermionStateful{Direction, SpinUp}(mom)
 
-AntiFermionStateful{Dir1}(f::AntiFermionStateful{Dir2}) where {Dir1 <: ParticleDirection, Dir2 <: ParticleDirection} =
-    AntiFermionStateful{Dir1}(f.momentum, f.spin)
+AntiFermionStateful{Dir, Spin}(f::AntiFermionStateful) where {Dir, Spin} = AntiFermionStateful{Dir, Spin}(f.momentum)
 
 """
     interaction_result(t1::Type{T1}, t2::Type{T2}) where {T1 <: QEDParticle, T2 <: QEDParticle}
@@ -115,19 +96,33 @@ function interaction_result(t1::Type{T1}, t2::Type{T2}) where {T1 <: QEDParticle
     @assert false "Invalid interaction between particles of types $t1 and $t2"
 end
 
-interaction_result(::Type{FermionStateful{Incoming}}, ::Type{FermionStateful{Outgoing}}) = PhotonStateful{Incoming}
-interaction_result(::Type{FermionStateful{Incoming}}, ::Type{AntiFermionStateful{Incoming}}) = PhotonStateful{Incoming}
-interaction_result(::Type{FermionStateful{Incoming}}, ::Type{<:PhotonStateful}) = FermionStateful{Outgoing}
+interaction_result(
+    ::Type{FermionStateful{Incoming, Spin1}},
+    ::Type{FermionStateful{Outgoing, Spin2}},
+) where {Spin1, Spin2} = PhotonStateful{Incoming, PolX}
+interaction_result(
+    ::Type{FermionStateful{Incoming, Spin1}},
+    ::Type{AntiFermionStateful{Incoming, Spin2}},
+) where {Spin1, Spin2} = PhotonStateful{Incoming, PolX}
+interaction_result(::Type{FermionStateful{Incoming, Spin1}}, ::Type{<:PhotonStateful}) where {Spin1} =
+    FermionStateful{Outgoing, SpinUp}
 
-interaction_result(::Type{FermionStateful{Outgoing}}, ::Type{FermionStateful{Incoming}}) = PhotonStateful{Incoming}
-interaction_result(::Type{FermionStateful{Outgoing}}, ::Type{AntiFermionStateful{Outgoing}}) = PhotonStateful{Incoming}
-interaction_result(::Type{FermionStateful{Outgoing}}, ::Type{<:PhotonStateful}) = FermionStateful{Incoming}
+interaction_result(
+    ::Type{FermionStateful{Outgoing, Spin1}},
+    ::Type{FermionStateful{Incoming, Spin2}},
+) where {Spin1, Spin2} = PhotonStateful{Incoming, PolX}
+interaction_result(
+    ::Type{FermionStateful{Outgoing, Spin1}},
+    ::Type{AntiFermionStateful{Outgoing, Spin2}},
+) where {Spin1, Spin2} = PhotonStateful{Incoming, PolX}
+interaction_result(::Type{FermionStateful{Outgoing, Spin1}}, ::Type{<:PhotonStateful}) where {Spin1} =
+    FermionStateful{Incoming, SpinUp}
 
 # antifermion mirror
-interaction_result(::Type{AntiFermionStateful{Incoming}}, t2::Type{<:QEDParticle}) =
-    interaction_result(FermionStateful{Outgoing}, t2)
-interaction_result(::Type{AntiFermionStateful{Outgoing}}, t2::Type{<:QEDParticle}) =
-    interaction_result(FermionStateful{Incoming}, t2)
+interaction_result(::Type{AntiFermionStateful{Incoming, Spin}}, t2::Type{<:QEDParticle}) where {Spin} =
+    interaction_result(FermionStateful{Outgoing, Spin}, t2)
+interaction_result(::Type{AntiFermionStateful{Outgoing, Spin}}, t2::Type{<:QEDParticle}) where {Spin} =
+    interaction_result(FermionStateful{Incoming, Spin}, t2)
 
 # photon commutativity
 interaction_result(t1::Type{<:PhotonStateful}, t2::Type{<:QEDParticle}) = interaction_result(t2, t1)
@@ -142,12 +137,18 @@ end
 
 Return the type of the inverted direction. E.g.
 """
-propagation_result(::Type{FermionStateful{Incoming}}) = FermionStateful{Outgoing}
-propagation_result(::Type{FermionStateful{Outgoing}}) = FermionStateful{Incoming}
-propagation_result(::Type{AntiFermionStateful{Incoming}}) = AntiFermionStateful{Outgoing}
-propagation_result(::Type{AntiFermionStateful{Outgoing}}) = AntiFermionStateful{Incoming}
-propagation_result(::Type{PhotonStateful{Incoming}}) = PhotonStateful{Outgoing}
-propagation_result(::Type{PhotonStateful{Outgoing}}) = PhotonStateful{Incoming}
+propagation_result(::Type{FermionStateful{Incoming, Spin}}) where {Spin <: AbstractDefiniteSpin} =
+    FermionStateful{Outgoing, Spin}
+propagation_result(::Type{FermionStateful{Outgoing, Spin}}) where {Spin <: AbstractDefiniteSpin} =
+    FermionStateful{Incoming, Spin}
+propagation_result(::Type{AntiFermionStateful{Incoming, Spin}}) where {Spin <: AbstractDefiniteSpin} =
+    AntiFermionStateful{Outgoing, Spin}
+propagation_result(::Type{AntiFermionStateful{Outgoing, Spin}}) where {Spin <: AbstractDefiniteSpin} =
+    AntiFermionStateful{Incoming, Spin}
+propagation_result(::Type{PhotonStateful{Incoming, Pol}}) where {Pol <: AbstractDefinitePolarization} =
+    PhotonStateful{Outgoing, Pol}
+propagation_result(::Type{PhotonStateful{Outgoing, Pol}}) where {Pol <: AbstractDefinitePolarization} =
+    PhotonStateful{Incoming, Pol}
 
 """
     types(::QEDModel)
@@ -156,12 +157,12 @@ Return a Vector of the possible types of particle in the [`QEDModel`](@ref).
 """
 function types(::QEDModel)
     return [
-        PhotonStateful{Incoming},
-        PhotonStateful{Outgoing},
-        FermionStateful{Incoming},
-        FermionStateful{Outgoing},
-        AntiFermionStateful{Incoming},
-        AntiFermionStateful{Outgoing},
+        PhotonStateful{Incoming, PolX},
+        PhotonStateful{Outgoing, PolX},
+        FermionStateful{Incoming, SpinUp},
+        FermionStateful{Outgoing, SpinUp},
+        AntiFermionStateful{Incoming, SpinUp},
+        AntiFermionStateful{Outgoing, SpinUp},
     ]
 end
 
@@ -190,17 +191,23 @@ end
 @inline momentum(p::FermionStateful)::SFourMomentum = p.momentum
 @inline momentum(p::AntiFermionStateful)::SFourMomentum = p.momentum
 
-@inline spin_or_pol(p::PhotonStateful)::AbstractPolarization = p.polarization
-@inline spin_or_pol(p::FermionStateful)::AbstractSpin = p.spin
-@inline spin_or_pol(p::AntiFermionStateful)::AbstractSpin = p.spin
+@inline spin_or_pol(p::PhotonStateful{Dir, Pol}) where {Dir, Pol <: AbstractDefinitePolarization} = Pol()
+@inline spin_or_pol(p::FermionStateful{Dir, Spin}) where {Dir, Spin <: AbstractDefiniteSpin} = Spin()
+@inline spin_or_pol(p::AntiFermionStateful{Dir, Spin}) where {Dir, Spin <: AbstractDefiniteSpin} = Spin()
 
-@inline direction(::PhotonStateful{Dir}) where {Dir <: ParticleDirection} = Dir()
-@inline direction(::FermionStateful{Dir}) where {Dir <: ParticleDirection} = Dir()
-@inline direction(::AntiFermionStateful{Dir}) where {Dir <: ParticleDirection} = Dir()
+@inline direction(
+    ::Type{P},
+) where {P <: Union{FermionStateful{Incoming}, AntiFermionStateful{Incoming}, PhotonStateful{Incoming}}} = Incoming()
+@inline direction(
+    ::Type{P},
+) where {P <: Union{FermionStateful{Outgoing}, AntiFermionStateful{Outgoing}, PhotonStateful{Outgoing}}} = Outgoing()
 
-@inline direction(::Type{PhotonStateful{Dir}}) where {Dir <: ParticleDirection} = Dir()
-@inline direction(::Type{FermionStateful{Dir}}) where {Dir <: ParticleDirection} = Dir()
-@inline direction(::Type{AntiFermionStateful{Dir}}) where {Dir <: ParticleDirection} = Dir()
+@inline direction(
+    ::P,
+) where {P <: Union{FermionStateful{Incoming}, AntiFermionStateful{Incoming}, PhotonStateful{Incoming}}} = Incoming()
+@inline direction(
+    ::P,
+) where {P <: Union{FermionStateful{Outgoing}, AntiFermionStateful{Outgoing}, PhotonStateful{Outgoing}}} = Outgoing()
 
 @inline isincoming(::QEDParticle{Incoming}) = true
 @inline isincoming(::QEDParticle{Outgoing}) = false
@@ -216,12 +223,12 @@ end
 @inline mass(::Type{<:AntiFermionStateful}) = 1.0
 @inline mass(::Type{<:PhotonStateful}) = 0.0
 
-@inline invert_momentum(p::FermionStateful{Dir}) where {Dir <: ParticleDirection} =
-    FermionStateful{Dir}(-p.momentum, p.spin)
-@inline invert_momentum(p::AntiFermionStateful{Dir}) where {Dir <: ParticleDirection} =
-    AntiFermionStateful{Dir}(-p.momentum, p.spin)
-@inline invert_momentum(k::PhotonStateful{Dir}) where {Dir <: ParticleDirection} =
-    PhotonStateful{Dir}(-k.momentum, k.polarization)
+@inline invert_momentum(p::FermionStateful{Dir, Spin}) where {Dir, Spin} =
+    FermionStateful{Dir, Spin}(-p.momentum, p.spin)
+@inline invert_momentum(p::AntiFermionStateful{Dir, Spin}) where {Dir, Spin} =
+    AntiFermionStateful{Dir, Spin}(-p.momentum, p.spin)
+@inline invert_momentum(k::PhotonStateful{Dir, Spin}) where {Dir, Spin} =
+    PhotonStateful{Dir, Spin}(-k.momentum, k.polarization)
 
 
 """
@@ -240,10 +247,10 @@ function caninteract(T1::Type{<:QEDParticle}, T2::Type{<:QEDParticle})
     end
 
     for (P1, P2) in [(T1, T2), (T2, T1)]
-        if (P1 == FermionStateful{Incoming} && P2 == AntiFermionStateful{Outgoing})
+        if (P1 <: FermionStateful{Incoming} && P2 <: AntiFermionStateful{Outgoing})
             return false
         end
-        if (P1 == FermionStateful{Outgoing} && P2 == AntiFermionStateful{Incoming})
+        if (P1 <: FermionStateful{Outgoing} && P2 <: AntiFermionStateful{Incoming})
             return false
         end
     end
@@ -253,17 +260,17 @@ end
 
 function type_index_from_name(::QEDModel, name::String)
     if startswith(name, "ki")
-        return (PhotonStateful{Incoming}, parse(Int, name[3:end]))
+        return (PhotonStateful{Incoming, PolX}, parse(Int, name[3:end]))
     elseif startswith(name, "ko")
-        return (PhotonStateful{Outgoing}, parse(Int, name[3:end]))
+        return (PhotonStateful{Outgoing, PolX}, parse(Int, name[3:end]))
     elseif startswith(name, "ei")
-        return (FermionStateful{Incoming}, parse(Int, name[3:end]))
+        return (FermionStateful{Incoming, SpinUp}, parse(Int, name[3:end]))
     elseif startswith(name, "eo")
-        return (FermionStateful{Outgoing}, parse(Int, name[3:end]))
+        return (FermionStateful{Outgoing, SpinUp}, parse(Int, name[3:end]))
     elseif startswith(name, "pi")
-        return (AntiFermionStateful{Incoming}, parse(Int, name[3:end]))
+        return (AntiFermionStateful{Incoming, SpinUp}, parse(Int, name[3:end]))
     elseif startswith(name, "po")
-        return (AntiFermionStateful{Outgoing}, parse(Int, name[3:end]))
+        return (AntiFermionStateful{Outgoing, SpinUp}, parse(Int, name[3:end]))
     else
         throw("Invalid name for a particle in the QED model")
     end
@@ -291,8 +298,7 @@ Return the factor of a vertex in a QED feynman diagram.
 end
 
 @inline function QED_inner_edge(p::QEDParticle)
-    pos_mom = p.momentum
-    return propagator(particle(p), pos_mom)
+    return propagator(particle(p), p.momentum)
 end
 
 """
@@ -300,24 +306,49 @@ end
 
 Calculate and return a new particle from two given interacting ones at a vertex.
 """
-function QED_conserve_momentum(p1::QEDParticle, p2::QEDParticle)
-    #println("Conserving momentum of \n$(direction(p1)) $(p1)\n and \n$(direction(p2)) $(p2)")
-    T3 = interaction_result(typeof(p1), typeof(p2))
-    # TODO: probably also need to do something about the spin/pol
+function QED_conserve_momentum(
+    p1::P1,
+    p2::P2,
+) where {
+    Dir1 <: ParticleDirection,
+    Dir2 <: ParticleDirection,
+    SpinPol1 <: AbstractSpinOrPolarization,
+    SpinPol2 <: AbstractSpinOrPolarization,
+    P1 <: Union{FermionStateful{Dir1, SpinPol1}, AntiFermionStateful{Dir1, SpinPol1}, PhotonStateful{Dir1, SpinPol1}},
+    P2 <: Union{FermionStateful{Dir2, SpinPol2}, AntiFermionStateful{Dir2, SpinPol2}, PhotonStateful{Dir2, SpinPol2}},
+}
+    P3 = interaction_result(P1, P2)
     p1_mom = p1.momentum
-    if (typeof(direction(p1)) <: Outgoing)
+    if (Dir1 <: Outgoing)
         p1_mom *= -1
     end
     p2_mom = p2.momentum
-    if (typeof(direction(p2)) <: Outgoing)
+    if (Dir2 <: Outgoing)
         p2_mom *= -1
     end
 
     p3_mom = p1_mom + p2_mom
-    if (typeof(direction(T3)) <: Incoming)
-        return T3(-p3_mom)
+    if (typeof(direction(P3)) <: Incoming)
+        return P3(-p3_mom)
     end
-    return T3(p3_mom)
+    return P3(p3_mom)
+end
+
+"""
+    QEDProcessInput <: AbstractProcessInput
+
+Input for a QED Process. Contains the [`QEDProcessDescription`](@ref) of the process it is an input for, and the values of the in and out particles.
+
+See also: [`gen_process_input`](@ref)
+"""
+struct QEDProcessInput{N1, N2, N3, N4, N5, N6} <: AbstractProcessInput
+    process::QEDProcessDescription
+    inFerms::SVector{N1, FermionStateful{Incoming, SpinUp}}
+    outFerms::SVector{N2, FermionStateful{Outgoing, SpinUp}}
+    inAntiferms::SVector{N3, AntiFermionStateful{Incoming, SpinUp}}
+    outAntiferms::SVector{N4, AntiFermionStateful{Outgoing, SpinUp}}
+    inPhotons::SVector{N5, PhotonStateful{Incoming, PolX}}
+    outPhotons::SVector{N6, PhotonStateful{Outgoing, PolX}}
 end
 
 """
@@ -328,6 +359,10 @@ Return the model of this process description.
 model(::QEDProcessDescription) = QEDModel()
 model(::QEDProcessInput) = QEDModel()
 
+function copy(process::QEDProcessDescription)
+    return QEDProcessDescription(copy(process.inParticles), copy(process.outParticles))
+end
+
 ==(p1::QEDProcessDescription, p2::QEDProcessDescription) =
     p1.inParticles == p2.inParticles && p1.outParticles == p2.outParticles
 
@@ -335,14 +370,23 @@ function in_particles(process::QEDProcessDescription)
     return process.inParticles
 end
 
-function in_particles(input::QEDProcessInput)
-    return input.inParticles
-end
-
 function out_particles(process::QEDProcessDescription)
     return process.outParticles
 end
 
-function out_particles(input::QEDProcessInput)
-    return input.outParticles
+function get_particle(input::QEDProcessInput, t::Type{Particle}, n::Int)::Particle where {Particle}
+    if (t <: FermionStateful{Incoming})
+        return input.inFerms[n]
+    elseif (t <: FermionStateful{Outgoing})
+        return input.outFerms[n]
+    elseif (t <: AntiFermionStateful{Incoming})
+        return input.inAntiferms[n]
+    elseif (t <: AntiFermionStateful{Outgoing})
+        return input.outAntiferms[n]
+    elseif (t <: PhotonStateful{Incoming})
+        return input.inPhotons[n]
+    elseif (t <: PhotonStateful{Outgoing})
+        return input.outPhotons[n]
+    end
+    @assert false "Invalid type given"
 end
diff --git a/src/models/qed/print.jl b/src/models/qed/print.jl
index 76f2166..7ba4160 100644
--- a/src/models/qed/print.jl
+++ b/src/models/qed/print.jl
@@ -32,20 +32,63 @@ function show(io::IO, process::QEDProcessDescription)
     return nothing
 end
 
+
+"""
+    String(process::QEDProcessDescription)
+
+Create a short string suitable as a filename or similar, describing the given process.
+
+```jldoctest
+julia> using MetagraphOptimization
+
+julia> String(parse_process("ke->ke", QEDModel()))
+qed_ke-ke
+
+julia> print(parse_process("kk->ep", QEDModel()))
+qed_kk-ep
+```
+"""
+function String(process::QEDProcessDescription)
+    # types() gives the types in order (QED) instead of random like keys() would
+    str = "qed_"
+    for type in types(QEDModel())
+        for _ in 1:get(process.inParticles, type, 0)
+            str = str * String(type)
+        end
+    end
+    str = str * "-"
+    for type in types(QEDModel())
+        for _ in 1:get(process.outParticles, type, 0)
+            str = str * String(type)
+        end
+    end
+    return str
+end
+
 """
     show(io::IO, processInput::QEDProcessInput)
 
-Pretty print an [`QEDProcessInput`](@ref) (with newlines).
+Pretty print a [`QEDProcessInput`](@ref) (with newlines).
 """
 function show(io::IO, processInput::QEDProcessInput)
     println(io, "Input for $(processInput.process):")
-    println(io, "  $(length(processInput.inParticles)) Incoming particles:")
-    for particle in processInput.inParticles
-        println(io, "    $particle")
+    if !isempty(processInput.inFerms)
+        println(io, "  $(processInput.inFerms)")
     end
-    println(io, "  $(length(processInput.outParticles)) Outgoing Particles:")
-    for particle in processInput.outParticles
-        println(io, "    $particle")
+    if !isempty(processInput.outFerms)
+        println(io, "  $(processInput.outFerms)")
+    end
+    if !isempty(processInput.inAntiferms)
+        println(io, "  $(processInput.inAntiferms)")
+    end
+    if !isempty(processInput.outAntiferms)
+        println(io, "  $(processInput.outAntiferms)")
+    end
+    if !isempty(processInput.inPhotons)
+        println(io, "  $(processInput.inPhotons)")
+    end
+    if !isempty(processInput.outPhotons)
+        println(io, "  $(processInput.outPhotons)")
     end
     return nothing
 end
@@ -53,7 +96,7 @@ end
 """
     show(io::IO, particle::T) where {T <: QEDParticle}
 
-Pretty print an [`QEDParticle`](@ref) (no newlines).
+Pretty print a [`QEDParticle`](@ref) (no newlines).
 """
 function show(io::IO, particle::T) where {T <: QEDParticle}
     print(io, "$(String(typeof(particle))): $(particle.momentum)")
diff --git a/src/node/type.jl b/src/node/type.jl
index 9105424..39283d0 100644
--- a/src/node/type.jl
+++ b/src/node/type.jl
@@ -3,7 +3,7 @@ using UUIDs
 using Base.Threads
 
 # TODO: reliably find out how many threads we're running with (nthreads() returns 1 when precompiling :/)
-rng = [Random.MersenneTwister(0) for _ in 1:32]
+rng = [Random.MersenneTwister(0) for _ in 1:64]
 
 """
     Node
diff --git a/src/optimization/greedy.jl b/src/optimization/greedy.jl
index 20ef336..a777587 100644
--- a/src/optimization/greedy.jl
+++ b/src/optimization/greedy.jl
@@ -71,3 +71,7 @@ function optimize_to_fixpoint!(optimizer::GreedyOptimizer, graph::DAG)
     end
     return nothing
 end
+
+function String(optimizer::GreedyOptimizer)
+    return "greedy_optimizer_$(optimizer.estimator)"
+end
diff --git a/src/optimization/random_walk.jl b/src/optimization/random_walk.jl
index e43507e..41e4d21 100644
--- a/src/optimization/random_walk.jl
+++ b/src/optimization/random_walk.jl
@@ -47,3 +47,7 @@ function optimize_step!(optimizer::RandomWalkOptimizer, graph::DAG)
         end
     end
 end
+
+function String(::RandomWalkOptimizer)
+    return "random_walker"
+end
diff --git a/src/optimization/reduce.jl b/src/optimization/reduce.jl
index 625874a..c6414f5 100644
--- a/src/optimization/reduce.jl
+++ b/src/optimization/reduce.jl
@@ -28,3 +28,7 @@ function optimize_to_fixpoint!(optimizer::ReductionOptimizer, graph::DAG)
     end
     return nothing
 end
+
+function String(::ReductionOptimizer)
+    return "reduction_optimizer"
+end
diff --git a/test/Project.toml b/test/Project.toml
index 5c459da..41c02e3 100644
--- a/test/Project.toml
+++ b/test/Project.toml
@@ -4,5 +4,7 @@ QEDbase = "10e22c08-3ccb-4172-bfcf-7d7aa3d04d93"
 QEDprocesses = "46de9c38-1bb3-4547-a1ec-da24d767fdad"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
+StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
diff --git a/test/runtests.jl b/test/runtests.jl
index 39a7cca..fa37b10 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -18,12 +18,12 @@ end
 @safetestset "ABC-Model Unit Tests                " begin
     include("unit_tests_abcmodel.jl")
 end
-@safetestset "QED Feynman Diagram Generation Tests" begin
-    include("unit_tests_qed_diagrams.jl")
-end
 @safetestset "QED-Model Unit Tests                " begin
     include("unit_tests_qedmodel.jl")
 end
+@safetestset "QED Feynman Diagram Generation Tests" begin
+    include("unit_tests_qed_diagrams.jl")
+end
 @safetestset "Node Reduction Unit Tests           " begin
     include("node_reduction.jl")
 end
diff --git a/test/unit_tests_execution.jl b/test/unit_tests_execution.jl
index dea4072..6c8b43f 100644
--- a/test/unit_tests_execution.jl
+++ b/test/unit_tests_execution.jl
@@ -2,6 +2,8 @@ using MetagraphOptimization
 using QEDbase
 using AccurateArithmetic
 using Random
+using UUIDs
+using StaticArrays
 
 import MetagraphOptimization.ABCParticle
 import MetagraphOptimization.interaction_result
@@ -27,11 +29,11 @@ function ground_truth_graph_result(input::ABCProcessInput)
     constant = (1 / 137.0)^2
 
     # calculate particle C in diagram 1
-    diagram1_C = ParticleC(input.inParticles[1].momentum + input.inParticles[2].momentum)
-    diagram2_C = ParticleC(input.inParticles[1].momentum + input.outParticles[2].momentum)
+    diagram1_C = ParticleC(input.inA[1].momentum + input.inB[1].momentum)
+    diagram2_C = ParticleC(input.inA[1].momentum + input.outB[1].momentum)
 
-    diagram1_Cp = ParticleC(input.outParticles[1].momentum + input.outParticles[2].momentum)
-    diagram2_Cp = ParticleC(input.outParticles[1].momentum + input.inParticles[2].momentum)
+    diagram1_Cp = ParticleC(input.outA[1].momentum + input.outB[1].momentum)
+    diagram2_Cp = ParticleC(input.outA[1].momentum + input.inB[1].momentum)
 
     check_particle_reverse_moment(diagram1_Cp.momentum, diagram1_C.momentum)
     check_particle_reverse_moment(diagram2_Cp.momentum, diagram2_C.momentum)
@@ -47,7 +49,18 @@ function ground_truth_graph_result(input::ABCProcessInput)
     return sum_kbn([diagram1_result, diagram2_result])
 end
 
-machine = get_machine_info()
+machine = Machine(
+    [
+        MetagraphOptimization.NumaNode(
+            0,
+            1,
+            MetagraphOptimization.default_strategy(MetagraphOptimization.NumaNode),
+            -1.0,
+            UUIDs.uuid1(),
+        ),
+    ],
+    [-1.0;;],
+)
 
 process_2_2 = ABCProcessDescription(
     Dict{Type, Int64}(ParticleA => 1, ParticleB => 1),
@@ -56,14 +69,12 @@ process_2_2 = ABCProcessDescription(
 
 particles_2_2 = ABCProcessInput(
     process_2_2,
-    ABCParticle[
-        ParticleA(SFourMomentum(0.823648, 0.0, 0.0, 0.823648)),
-        ParticleB(SFourMomentum(0.823648, 0.0, 0.0, -0.823648)),
-    ],
-    ABCParticle[
-        ParticleA(SFourMomentum(0.823648, -0.835061, -0.474802, 0.277915)),
-        ParticleB(SFourMomentum(0.823648, 0.835061, 0.474802, -0.277915)),
-    ],
+    SVector{1}(ParticleA(SFourMomentum(0.823648, 0.0, 0.0, 0.823648))),
+    SVector{1}(ParticleB(SFourMomentum(0.823648, 0.0, 0.0, -0.823648))),
+    SVector{0, ParticleC}(),
+    SVector{1}(ParticleA(SFourMomentum(0.823648, -0.835061, -0.474802, 0.277915))),
+    SVector{1}(ParticleB(SFourMomentum(0.823648, 0.835061, 0.474802, -0.277915))),
+    SVector{0, ParticleC}(),
 )
 expected_result = ground_truth_graph_result(particles_2_2)
 
diff --git a/test/unit_tests_qedmodel.jl b/test/unit_tests_qedmodel.jl
index af63fdf..14a03ac 100644
--- a/test/unit_tests_qedmodel.jl
+++ b/test/unit_tests_qedmodel.jl
@@ -3,6 +3,7 @@ using QEDbase
 using QEDprocesses
 using StatsBase # for countmap
 using Random
+using UUIDs
 
 import MetagraphOptimization.caninteract
 import MetagraphOptimization.issame
@@ -17,32 +18,32 @@ def_momentum = SFourMomentum(1.0, 0.0, 0.0, 0.0)
 RNG = Random.default_rng()
 
 testparticleTypes = [
-    PhotonStateful{Incoming},
-    PhotonStateful{Outgoing},
-    FermionStateful{Incoming},
-    FermionStateful{Outgoing},
-    AntiFermionStateful{Incoming},
-    AntiFermionStateful{Outgoing},
+    PhotonStateful{Incoming, PolX},
+    PhotonStateful{Outgoing, PolX},
+    FermionStateful{Incoming, SpinUp},
+    FermionStateful{Outgoing, SpinUp},
+    AntiFermionStateful{Incoming, SpinUp},
+    AntiFermionStateful{Outgoing, SpinUp},
 ]
 
 testparticleTypesPropagated = [
-    PhotonStateful{Outgoing},
-    PhotonStateful{Incoming},
-    FermionStateful{Outgoing},
-    FermionStateful{Incoming},
-    AntiFermionStateful{Outgoing},
-    AntiFermionStateful{Incoming},
+    PhotonStateful{Outgoing, PolX},
+    PhotonStateful{Incoming, PolX},
+    FermionStateful{Outgoing, SpinUp},
+    FermionStateful{Incoming, SpinUp},
+    AntiFermionStateful{Outgoing, SpinUp},
+    AntiFermionStateful{Incoming, SpinUp},
 ]
 
 function compton_groundtruth(input::QEDProcessInput)
     # p1k1 -> p2k2
     # formula: −(ie)^2 (u(p2) slashed(ε1) S(p2 − k1) slashed(ε2) u(p1) + u(p2) slashed(ε2) S(p1 + k1) slashed(ε1) u(p1))
 
-    p1 = input.inParticles[findfirst(x -> typeof(x) <: FermionStateful, input.inParticles)]
-    p2 = input.outParticles[findfirst(x -> typeof(x) <: FermionStateful, input.outParticles)]
+    p1 = input.inFerms[1]
+    p2 = input.outFerms[1]
 
-    k1 = input.inParticles[findfirst(x -> typeof(x) <: PhotonStateful, input.inParticles)]
-    k2 = input.outParticles[findfirst(x -> typeof(x) <: PhotonStateful, input.outParticles)]
+    k1 = input.inPhotons[1]
+    k2 = input.outPhotons[1]
 
     u_p1 = base_state(Electron(), Incoming(), p1.momentum, spin_or_pol(p1))
     u_p2 = base_state(Electron(), Outgoing(), p2.momentum, spin_or_pol(p2))
@@ -117,36 +118,36 @@ end
 
     @testset "Known processes" begin
         compton_process = QEDProcessDescription(
-            Dict{Type, Int}(PhotonStateful{Incoming} => 1, FermionStateful{Incoming} => 1),
-            Dict{Type, Int}(PhotonStateful{Outgoing} => 1, FermionStateful{Outgoing} => 1),
+            Dict{Type, Int}(PhotonStateful{Incoming, PolX} => 1, FermionStateful{Incoming, SpinUp} => 1),
+            Dict{Type, Int}(PhotonStateful{Outgoing, PolX} => 1, FermionStateful{Outgoing, SpinUp} => 1),
         )
 
         @test parse_process("ke->ke", QEDModel()) == compton_process
 
         positron_compton_process = QEDProcessDescription(
-            Dict{Type, Int}(PhotonStateful{Incoming} => 1, AntiFermionStateful{Incoming} => 1),
-            Dict{Type, Int}(PhotonStateful{Outgoing} => 1, AntiFermionStateful{Outgoing} => 1),
+            Dict{Type, Int}(PhotonStateful{Incoming, PolX} => 1, AntiFermionStateful{Incoming, SpinUp} => 1),
+            Dict{Type, Int}(PhotonStateful{Outgoing, PolX} => 1, AntiFermionStateful{Outgoing, SpinUp} => 1),
         )
 
         @test parse_process("kp->kp", QEDModel()) == positron_compton_process
 
         trident_process = QEDProcessDescription(
-            Dict{Type, Int}(PhotonStateful{Incoming} => 1, FermionStateful{Incoming} => 1),
-            Dict{Type, Int}(FermionStateful{Outgoing} => 2, AntiFermionStateful{Outgoing} => 1),
+            Dict{Type, Int}(PhotonStateful{Incoming, PolX} => 1, FermionStateful{Incoming, SpinUp} => 1),
+            Dict{Type, Int}(FermionStateful{Outgoing, SpinUp} => 2, AntiFermionStateful{Outgoing, SpinUp} => 1),
         )
 
         @test parse_process("ke->eep", QEDModel()) == trident_process
 
         pair_production_process = QEDProcessDescription(
-            Dict{Type, Int}(PhotonStateful{Incoming} => 2),
-            Dict{Type, Int}(FermionStateful{Outgoing} => 1, AntiFermionStateful{Outgoing} => 1),
+            Dict{Type, Int}(PhotonStateful{Incoming, PolX} => 2),
+            Dict{Type, Int}(FermionStateful{Outgoing, SpinUp} => 1, AntiFermionStateful{Outgoing, SpinUp} => 1),
         )
 
         @test parse_process("kk->pe", QEDModel()) == pair_production_process
 
         pair_annihilation_process = QEDProcessDescription(
-            Dict{Type, Int}(FermionStateful{Incoming} => 1, AntiFermionStateful{Incoming} => 1),
-            Dict{Type, Int}(PhotonStateful{Outgoing} => 2),
+            Dict{Type, Int}(FermionStateful{Incoming, SpinUp} => 1, AntiFermionStateful{Incoming, SpinUp} => 1),
+            Dict{Type, Int}(PhotonStateful{Outgoing, PolX} => 2),
         )
 
         @test parse_process("pe->kk", QEDModel()) == pair_annihilation_process
@@ -160,12 +161,24 @@ end
 
         for i in 1:100
             input = gen_process_input(process)
-            @test countmap(typeof.(input.inParticles)) == process.inParticles
-            @test countmap(typeof.(input.outParticles)) == process.outParticles
+            @test length(input.inFerms) == get(process.inParticles, FermionStateful{Incoming, SpinUp}, 0)
+            @test length(input.inAntiferms) == get(process.inParticles, AntiFermionStateful{Incoming, SpinUp}, 0)
+            @test length(input.inPhotons) == get(process.inParticles, PhotonStateful{Incoming, PolX}, 0)
+            @test length(input.outFerms) == get(process.outParticles, FermionStateful{Outgoing, SpinUp}, 0)
+            @test length(input.outAntiferms) == get(process.outParticles, AntiFermionStateful{Outgoing, SpinUp}, 0)
+            @test length(input.outPhotons) == get(process.outParticles, PhotonStateful{Outgoing, PolX}, 0)
 
             @test isapprox(
-                sum(getfield.(input.inParticles, :momentum)),
-                sum(getfield.(input.outParticles, :momentum));
+                sum([
+                    getfield.(input.inFerms, :momentum)...,
+                    getfield.(input.inAntiferms, :momentum)...,
+                    getfield.(input.inPhotons, :momentum)...,
+                ]),
+                sum([
+                    getfield.(input.outFerms, :momentum)...,
+                    getfield.(input.outAntiferms, :momentum)...,
+                    getfield.(input.outPhotons, :momentum)...,
+                ]);
                 atol = sqrt(eps()),
             )
         end
@@ -179,7 +192,18 @@ end
 
     model = QEDModel()
     process = parse_process("ke->ke", model)
-    machine = get_machine_info()
+    machine = Machine(
+        [
+            MetagraphOptimization.NumaNode(
+                0,
+                1,
+                MetagraphOptimization.default_strategy(MetagraphOptimization.NumaNode),
+                -1.0,
+                UUIDs.uuid1(),
+            ),
+        ],
+        [-1.0;;],
+    )
 
     graph = MetagraphOptimization.DAG()
 
@@ -289,3 +313,37 @@ end
     compton_function = get_compute_function(graph_generated, process, machine)
     @test isapprox(compton_function.(input), compton_groundtruth.(input))
 end
+
+@testset "Equal results after optimization" for optimizer in
+                                                [ReductionOptimizer(), RandomWalkOptimizer(MersenneTwister(0))]
+    @testset "Process $proc_str" for proc_str in ["ke->ke", "kp->kp", "kk->ep", "ep->kk", "ke->kke", "ke->kkke"]
+        model = QEDModel()
+        process = parse_process(proc_str, model)
+        machine = Machine(
+            [
+                MetagraphOptimization.NumaNode(
+                    0,
+                    1,
+                    MetagraphOptimization.default_strategy(MetagraphOptimization.NumaNode),
+                    -1.0,
+                    UUIDs.uuid1(),
+                ),
+            ],
+            [-1.0;;],
+        )
+        graph = gen_graph(process)
+
+        compute_function = get_compute_function(graph, process, machine)
+
+        if (typeof(optimizer) <: RandomWalkOptimizer)
+            optimize!(optimizer, graph, 100)
+        elseif (typeof(optimizer) <: ReductionOptimizer)
+            optimize_to_fixpoint!(optimizer, graph)
+        end
+        reduced_compute_function = get_compute_function(graph, process, machine)
+
+        input = [gen_process_input(process) for _ in 1:100]
+
+        @test isapprox(compute_function.(input), reduced_compute_function.(input))
+    end
+end