{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7b70642d",
   "metadata": {},
   "source": "# Benchmarks: OSCAR\n\n*The compiled C++ of OSCAR, called from the R package, against the batched port on the CPU and on the GPU.*\n\nOSCAR (Dome & Wills, 2026) is the model of the participant-level run in [oscar/](https://github.com/lenarddome/psp-torch/blob/main/notebooks/oscar/README.md): a small network learning the inverse base-rate design trial by trial, one PSP search per participant. Its reference implementation is C++ with RcppArmadillo, called from R once per parameter set by `pspGlobal()`; the port, `psp.models.oscar`, runs a whole batch of parameter sets, and of participants, in one call. This notebook compares three builds:\n\n- **R + C++**: psp 1.0.6 from CRAN, OSCAR compiled from [`benchmarks/oscar/tmp-models`](https://github.com/lenarddome/psp-torch/tree/main/benchmarks/oscar/tmp-models) with `Rcpp::sourceCpp()`, run with the routine's own `evaluate()` and `discretize()` (`convert.R`, `imac.R`), the participants spread over worker processes as `psp.R` does;\n- **torch CPU float64** and **torch GPU float32**: `notebooks/oscar/run_oscar.py`, every participant's search advancing in lockstep with `PSPLockstep()`.\n\nTwo measurements: the cost of one call of the model, by the number of parameter sets it carries, and the whole routine for 14 participants. The scripts are in [`benchmarks/oscar`](https://github.com/lenarddome/psp-torch/tree/main/benchmarks/oscar) (`run.py` runs everything); this notebook only reads their results."
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b1da2e3c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-15T15:58:14.299788Z",
     "iopub.status.busy": "2026-10-15T15:58:14.299675Z",
     "iopub.status.idle": "2026-10-15T15:58:15.388473Z",
     "shell.execute_reply": "2026-10-15T15:58:15.387863Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import platform\n",
    "import subprocess\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "from IPython.display import Markdown, display\n",
    "import matplotlib.pyplot as plt\n",
    "from figure_style import figures, titled\n",
    "\n",
    "show = figures(\"benchmarks-oscar\")\n",
    "\n",
    "grey, muted = \"#6c757d\", \"#6c757d\"\n",
    "# the port's builds; R + C++ is the grey reference, drawn as dashed lines or grey bars\n",
    "colours = {\"cpu float64\": \"#eb6834\", \"cuda float32\": \"#2a78d6\", \"cuda float64\": \"#1baf7a\"}\n",
    "\n",
    "\n",
    "def us(v):\n",
    "    return f\"{v / 1000:,.2f} ms\" if v >= 1000 else f\"{v:,.1f} µs\"\n",
    "\n",
    "\n",
    "def times(v):\n",
    "    return f\"{v:,.0f}×\" if v >= 10 else f\"{v:.2g}×\"\n",
    "\n",
    "\n",
    "results = Path(\"../benchmarks/oscar/results\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c172659e",
   "metadata": {},
   "source": "## One call of the model\n\nThe first participant's 112 trials, with parameter sets drawn uniformly within the bounds of the routine. R calls the compiled `OSCAR()` once per parameter set, as `pspGlobal()` calls the model; the routine's `evaluate()` adds what happens around it in R: a fresh random initial state, and the data.table reduction to the mean P(rare) at test. The port's `evaluate()` does all of that for a whole batch in one call; its time includes `discretize()`."
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "64b41910",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-15T15:58:15.389688Z",
     "iopub.status.busy": "2026-10-15T15:58:15.389530Z",
     "iopub.status.idle": "2026-10-15T15:58:15.405669Z",
     "shell.execute_reply": "2026-10-15T15:58:15.405325Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>per evaluation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R + RcppArmadillo, one call per parameter set</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>OSCAR() alone</th>\n",
       "      <td>2.27 ms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>evaluate()</th>\n",
       "      <td>5.36 ms</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              per evaluation\n",
       "R + RcppArmadillo, one call per parameter set               \n",
       "OSCAR() alone                                        2.27 ms\n",
       "evaluate()                                           5.36 ms"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_model = pd.read_csv(results / \"model_r.csv\")\n",
    "r_us = (1e6 * r_model.seconds / r_model.evaluations).groupby(r_model.what).median()\n",
    "t_model = pd.read_csv(results / \"model_torch.csv\")\n",
    "t_model[\"build\"] = t_model.device + \" \" + t_model.dtype\n",
    "per_call = t_model.groupby([\"build\", \"batch\"]).seconds.median().unstack(\"build\")[list(colours)]\n",
    "per_eval = 1e6 * per_call.div(per_call.index, axis=0)\n",
    "pd.DataFrame({\"per evaluation\": r_us.map(us)}).rename_axis(\"R + RcppArmadillo, one call per parameter set\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e36ead4c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-15T15:58:15.406754Z",
     "iopub.status.busy": "2026-10-15T15:58:15.406654Z",
     "iopub.status.idle": "2026-10-15T15:58:16.123616Z",
     "shell.execute_reply": "2026-10-15T15:58:16.123144Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 880x528 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 530,
       "width": 768
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>build</th>\n",
       "      <th>cpu float64</th>\n",
       "      <th>cuda float32</th>\n",
       "      <th>cuda float64</th>\n",
       "      <th>vs R evaluate(), cpu float64</th>\n",
       "      <th>vs R evaluate(), cuda float32</th>\n",
       "      <th>vs R evaluate(), cuda float64</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parameter sets per call</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>82.57 ms</td>\n",
       "      <td>232.44 ms</td>\n",
       "      <td>230.44 ms</td>\n",
       "      <td>0.065×</td>\n",
       "      <td>0.023×</td>\n",
       "      <td>0.023×</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22.30 ms</td>\n",
       "      <td>59.04 ms</td>\n",
       "      <td>59.24 ms</td>\n",
       "      <td>0.24×</td>\n",
       "      <td>0.091×</td>\n",
       "      <td>0.091×</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6.70 ms</td>\n",
       "      <td>14.82 ms</td>\n",
       "      <td>14.89 ms</td>\n",
       "      <td>0.8×</td>\n",
       "      <td>0.36×</td>\n",
       "      <td>0.36×</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2.47 ms</td>\n",
       "      <td>3.74 ms</td>\n",
       "      <td>3.73 ms</td>\n",
       "      <td>2.2×</td>\n",
       "      <td>1.4×</td>\n",
       "      <td>1.4×</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>1.32 ms</td>\n",
       "      <td>940.3 µs</td>\n",
       "      <td>936.6 µs</td>\n",
       "      <td>4.1×</td>\n",
       "      <td>5.7×</td>\n",
       "      <td>5.7×</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1024</th>\n",
       "      <td>1.03 ms</td>\n",
       "      <td>235.6 µs</td>\n",
       "      <td>233.4 µs</td>\n",
       "      <td>5.2×</td>\n",
       "      <td>23×</td>\n",
       "      <td>23×</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4096</th>\n",
       "      <td>379.3 µs</td>\n",
       "      <td>59.1 µs</td>\n",
       "      <td>125.0 µs</td>\n",
       "      <td>14×</td>\n",
       "      <td>91×</td>\n",
       "      <td>43×</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16384</th>\n",
       "      <td>312.6 µs</td>\n",
       "      <td>20.3 µs</td>\n",
       "      <td>118.0 µs</td>\n",
       "      <td>17×</td>\n",
       "      <td>264×</td>\n",
       "      <td>45×</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "build                   cpu float64 cuda float32 cuda float64  \\\n",
       "parameter sets per call                                         \n",
       "1                          82.57 ms    232.44 ms    230.44 ms   \n",
       "4                          22.30 ms     59.04 ms     59.24 ms   \n",
       "16                          6.70 ms     14.82 ms     14.89 ms   \n",
       "64                          2.47 ms      3.74 ms      3.73 ms   \n",
       "256                         1.32 ms     940.3 µs     936.6 µs   \n",
       "1024                        1.03 ms     235.6 µs     233.4 µs   \n",
       "4096                       379.3 µs      59.1 µs     125.0 µs   \n",
       "16384                      312.6 µs      20.3 µs     118.0 µs   \n",
       "\n",
       "build                   vs R evaluate(), cpu float64  \\\n",
       "parameter sets per call                                \n",
       "1                                             0.065×   \n",
       "4                                              0.24×   \n",
       "16                                              0.8×   \n",
       "64                                              2.2×   \n",
       "256                                             4.1×   \n",
       "1024                                            5.2×   \n",
       "4096                                             14×   \n",
       "16384                                            17×   \n",
       "\n",
       "build                   vs R evaluate(), cuda float32  \\\n",
       "parameter sets per call                                 \n",
       "1                                              0.023×   \n",
       "4                                              0.091×   \n",
       "16                                              0.36×   \n",
       "64                                               1.4×   \n",
       "256                                              5.7×   \n",
       "1024                                              23×   \n",
       "4096                                              91×   \n",
       "16384                                            264×   \n",
       "\n",
       "build                   vs R evaluate(), cuda float64  \n",
       "parameter sets per call                                \n",
       "1                                              0.023×  \n",
       "4                                              0.091×  \n",
       "16                                              0.36×  \n",
       "64                                               1.4×  \n",
       "256                                              5.7×  \n",
       "1024                                              23×  \n",
       "4096                                              43×  \n",
       "16384                                             45×  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 4.8))\n",
    "for b, colour in colours.items():\n",
    "    ax.plot(per_eval.index, per_eval[b], \"-o\", color=colour, linewidth=2, markersize=6,\n",
    "            markeredgecolor=\"white\", label=f\"torch {b}, one batched call\")\n",
    "for what, style in [(\"evaluate()\", \"--\"), (\"OSCAR() alone\", \":\")]:\n",
    "    ax.axhline(r_us[what], color=grey, linestyle=style, linewidth=1.6)\n",
    "    ax.annotate(f\"R + C++ {what}: {us(r_us[what])}\", xy=(per_eval.index.max(), r_us[what]),\n",
    "                xytext=(0, 4), textcoords=\"offset points\", color=muted, fontsize=9.5, ha=\"right\", va=\"bottom\")\n",
    "ax.set_xscale(\"log\", base=4)\n",
    "ax.set_yscale(\"log\")\n",
    "ax.xaxis.set_major_formatter(lambda v, _: f\"{v:,.0f}\")\n",
    "ax.yaxis.set_major_formatter(lambda v, _: f\"{v / 1000:g} ms\" if v >= 1000 else f\"{v:g} µs\")\n",
    "ax.set_xlabel(\"parameter sets per call\")\n",
    "ax.legend(loc=\"lower left\", fontsize=9.5)\n",
    "titled(ax, \"Time per evaluation of OSCAR, log scales\",\n",
    "       \"lower is faster; one participant, 112 trials; R makes one call per parameter set\")\n",
    "show(fig, \"model-per-evaluation\")\n",
    "table = per_eval.map(us)\n",
    "for b in colours:\n",
    "    table[f\"vs R evaluate(), {b}\"] = (r_us[\"evaluate()\"] / per_eval[b]).map(times)\n",
    "table.rename_axis(\"parameter sets per call\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5667abce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-15T15:58:16.124838Z",
     "iopub.status.busy": "2026-10-15T15:58:16.124728Z",
     "iopub.status.idle": "2026-10-15T15:58:16.128825Z",
     "shell.execute_reply": "2026-10-15T15:58:16.128476Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/markdown": "\nA call of the port has a large fixed cost: 82.57 ms on the CPU and 232.44 ms on the GPU for a single parameter set, against 2.27 ms for one call of the C++. On the GPU a call costs about the same up to 4,096 parameter sets; on the CPU it grows, but far more slowly than the batch (338.12 ms for 256). OSCAR runs its 112 trials in order, and on every trial the port runs the ten recurrent attention shifts in order (for every row, keeping the result only where the trial learns), so one call is some 1,200 steps one after another, each a handful of tensor operations. Every operation costs a fixed amount of dispatch on the CPU and a kernel launch on the GPU, whether it works on one parameter set or on thousands. The C++ does the same steps without that overhead, which is why it wins for a single parameter set by a factor of 36 on the CPU.\n\nThe batch pays that fixed cost once for every parameter set it carries. The port beats R's `evaluate()` from 64 parameter sets per call on the CPU and from 64 on the GPU, and at 16,384 it is 17× faster on the CPU, 45× on the GPU in float64 and 264× in float32. Below a few thousand parameter sets the GPU's time is launches, not arithmetic, so float32 and float64 cost the same there (their lines coincide); above it, the card's slow double precision shows.\n",
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def crossover(b):\n",
    "    faster = per_eval.index[per_eval[b] < r_us[\"evaluate()\"]]\n",
    "    return int(faster.min()) if len(faster) else None\n",
    "\n",
    "\n",
    "single = per_call.loc[1]\n",
    "gpu_flat = int(per_call.index[per_call[\"cuda float32\"] < 1.1 * single[\"cuda float32\"]].max())\n",
    "largest = per_eval.index.max()\n",
    "display(Markdown(f'''\n",
    "A call of the port has a large fixed cost: {us(1e6 * single[\"cpu float64\"])} on the CPU and {us(1e6 * single[\"cuda float32\"])} on the GPU for a single parameter set, against {us(r_us[\"OSCAR() alone\"])} for one call of the C++. On the GPU a call costs about the same up to {gpu_flat:,} parameter sets; on the CPU it grows, but far more slowly than the batch ({us(1e6 * per_call.loc[256, \"cpu float64\"])} for 256). OSCAR runs its 112 trials in order, and on every trial the port runs the ten recurrent attention shifts in order (for every row, keeping the result only where the trial learns), so one call is some 1,200 steps one after another, each a handful of tensor operations. Every operation costs a fixed amount of dispatch on the CPU and a kernel launch on the GPU, whether it works on one parameter set or on thousands. The C++ does the same steps without that overhead, which is why it wins for a single parameter set by a factor of {single[\"cpu float64\"] / (r_us[\"OSCAR() alone\"] / 1e6):.0f} on the CPU.\n",
    "\n",
    "The batch pays that fixed cost once for every parameter set it carries. The port beats R's `evaluate()` from {crossover(\"cpu float64\")} parameter sets per call on the CPU and from {crossover(\"cuda float32\")} on the GPU, and at {largest:,} it is {times(r_us[\"evaluate()\"] / per_eval.loc[largest, \"cpu float64\"])} faster on the CPU, {times(r_us[\"evaluate()\"] / per_eval.loc[largest, \"cuda float64\"])} on the GPU in float64 and {times(r_us[\"evaluate()\"] / per_eval.loc[largest, \"cuda float32\"])} in float32. Below a few thousand parameter sets the GPU's time is launches, not arithmetic, so float32 and float64 cost the same there (their lines coincide); above it, the card's slow double precision shows.\n",
    "'''))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f15835c1",
   "metadata": {},
   "source": "## The participant-level routine\n\nThe routine of `psp.R` with reduced settings: the first 14 participants, 100 random starting points (one matrix shared by every participant, as `psp.R` draws it), radius 1, population 1,000,000 (so every pattern keeps proposing until the cap), 200 iterations, seed 2026.\n\n- **R + C++** runs `pspGlobal()` once per participant, the participants spread over 14 forked worker processes: `psp.R` uses foreach over `detectCores() - 2` cores, which is 14 on this machine, so every worker runs one participant. Its time is the wall clock of the whole parallel run.\n- **The port** runs `run_oscar.py --no-save` in one process: every participant's search advances in lockstep, and each iteration evaluates the proposals of all 14 participants in one batched call. Its time is the wall clock of that process, including Python's start-up, loading the data and initialising CUDA."
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7e565b02",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-15T15:58:16.129870Z",
     "iopub.status.busy": "2026-10-15T15:58:16.129770Z",
     "iopub.status.idle": "2026-10-15T15:58:16.508256Z",
     "shell.execute_reply": "2026-10-15T15:58:16.507852Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 880x308 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 336,
       "width": 862
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>minutes, all participants</th>\n",
       "      <th>speed-up against R</th>\n",
       "      <th>evaluations per second</th>\n",
       "      <th>regions per participant</th>\n",
       "      <th>evaluations per participant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>R + C++</th>\n",
       "      <td>7.4</td>\n",
       "      <td>1×</td>\n",
       "      <td>1,111</td>\n",
       "      <td>198–216, median 204</td>\n",
       "      <td>35,417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>torch cpu float64</th>\n",
       "      <td>4.4</td>\n",
       "      <td>1.7×</td>\n",
       "      <td>1,864</td>\n",
       "      <td>194–215, median 205</td>\n",
       "      <td>34,916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>torch cuda float32</th>\n",
       "      <td>0.9</td>\n",
       "      <td>8.6×</td>\n",
       "      <td>9,706</td>\n",
       "      <td>196–220, median 208</td>\n",
       "      <td>35,461</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   minutes, all participants speed-up against R  \\\n",
       "R + C++                                  7.4                 1×   \n",
       "torch cpu float64                        4.4               1.7×   \n",
       "torch cuda float32                       0.9               8.6×   \n",
       "\n",
       "                   evaluations per second regions per participant  \\\n",
       "R + C++                             1,111     198–216, median 204   \n",
       "torch cpu float64                   1,864     194–215, median 205   \n",
       "torch cuda float32                  9,706     196–220, median 208   \n",
       "\n",
       "                   evaluations per participant  \n",
       "R + C++                                 35,417  \n",
       "torch cpu float64                       34,916  \n",
       "torch cuda float32                      35,461  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def keys_torch(directory):\n",
    "    # every inequality matrix a run found, as the upper triangle in column-major order (as compare.R writes R's)\n",
    "    rows = []\n",
    "    for f in sorted((directory / \"patterns\").glob(\"OSCAR_4.*.npz\")):\n",
    "        z = np.load(f)\n",
    "        for m, c in zip(z[\"ordinal_patterns\"], z[\"ordinal_counts\"], strict=True):\n",
    "            rows.append({\"ppt\": f.stem.split(\".\", 1)[1], \"count\": int(c),\n",
    "                         \"key\": \",\".join(str(int(m[i, j])) for j in range(5) for i in range(j))})\n",
    "    return pd.DataFrame(rows)\n",
    "\n",
    "\n",
    "r_wall = pd.read_csv(results / \"psp_r\" / \"wall.csv\").iloc[0]\n",
    "runs = {\"R + C++\": (float(r_wall.wall_seconds), pd.read_csv(results / \"psp_r\" / \"summary.csv\"),\n",
    "                    pd.read_csv(results / \"psp_r\" / \"patterns.csv\"))}\n",
    "for _, w in pd.read_csv(results / \"psp_torch_wall.csv\").iterrows():\n",
    "    d = results / f\"psp_torch_{w['device']}_{w['dtype']}\"\n",
    "    runs[f\"torch {w['device']} {w['dtype']}\"] = (float(w[\"wall_seconds\"]), pd.read_csv(d / \"tracking.csv\"), keys_torch(d))\n",
    "run_colours = {name: grey if name == \"R + C++\" else colours[name.removeprefix(\"torch \")] for name in runs}\n",
    "n_ppt = int(r_wall.participants)\n",
    "r_seconds = runs[\"R + C++\"][0]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 2.8))\n",
    "names = list(runs)[::-1]\n",
    "minutes = [runs[n][0] / 60 for n in names]\n",
    "ax.barh(names, minutes, color=[run_colours[n] for n in names], height=0.6)\n",
    "for y, m in enumerate(minutes):\n",
    "    ax.text(m + max(minutes) * 0.01, y, f\"{m:.1f} min\", va=\"center\", fontsize=10.5)\n",
    "ax.grid(axis=\"y\", visible=False)\n",
    "ax.set_xlim(0, max(minutes) * 1.15)\n",
    "ax.xaxis.set_major_formatter(lambda v, _: f\"{v:g} min\")\n",
    "titled(ax, f\"Wall-clock time of the routine for {n_ppt} participants\",\n",
    "       \"lower is faster; R in parallel processes, the port all participants in one\")\n",
    "show(fig, \"routine-wall-clock\")\n",
    "pd.DataFrame({\n",
    "    name: {\n",
    "        \"minutes, all participants\": f\"{seconds / 60:.1f}\",\n",
    "        \"speed-up against R\": times(r_seconds / seconds),\n",
    "        \"evaluations per second\": f\"{summary.evaluations.sum() / seconds:,.0f}\",\n",
    "        \"regions per participant\": f\"{summary.patterns.min()}–{summary.patterns.max()}, median {summary.patterns.median():.0f}\",\n",
    "        \"evaluations per participant\": f\"{summary.evaluations.median():,.0f}\",\n",
    "    }\n",
    "    for name, (seconds, summary, _) in runs.items()\n",
    "}).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9936ffcd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-15T15:58:16.509432Z",
     "iopub.status.busy": "2026-10-15T15:58:16.509322Z",
     "iopub.status.idle": "2026-10-15T15:58:16.512431Z",
     "shell.execute_reply": "2026-10-15T15:58:16.512157Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/markdown": "\nEach R participant took a median of 419 s in the parallel run. The same 35,417 evaluations at the 5.36 ms of a lone `evaluate()` would take 190 s, so with 14 workers on 16 hardware threads (8 cores) each R process ran 2.2 times slower than alone: the workers compete for the cores. That is the routine as `psp.R` runs it on this machine. The port's CPU run is one process using torch's own threads (8 here) inside each operation.\n",
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r_summary = runs[\"R + C++\"][1]\n",
    "lone = r_summary.evaluations.median() * r_us[\"evaluate()\"] / 1e6\n",
    "display(Markdown(f'''\n",
    "Each R participant took a median of {r_summary.seconds.median():.0f} s in the parallel run. The same {r_summary.evaluations.median():,.0f} evaluations at the {us(r_us[\"evaluate()\"])} of a lone `evaluate()` would take {lone:.0f} s, so with {int(r_wall.workers)} workers on {os.cpu_count()} hardware threads ({os.cpu_count() // 2} cores) each R process ran {r_summary.seconds.median() / lone:.1f} times slower than alone: the workers compete for the cores. That is the routine as `psp.R` runs it on this machine. The port's CPU run is one process using torch's own threads ({torch.get_num_threads()} here) inside each operation.\n",
    "'''))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1986e04",
   "metadata": {},
   "source": "### How large are the batches of a real run?\n\nThe single-call measurements above predict the routine only if we know how many parameter sets a call of the routine carries. In a PSP run that is the number of patterns still filling, summed over the participants: iteration 0 evaluates every starting point of every participant, and every later iteration one proposal per pattern found so far. With a population of a million, no pattern ever fills, so every pattern proposes on every iteration."
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c9539f49",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-15T15:58:16.513545Z",
     "iopub.status.busy": "2026-10-15T15:58:16.513447Z",
     "iopub.status.idle": "2026-10-15T15:58:16.517766Z",
     "shell.execute_reply": "2026-10-15T15:58:16.517387Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/markdown": "\nIteration 0 evaluates 1,400 parameter sets (14 participants × 100 starting points); by the last iteration the participants hold 2,903 patterns between them, so the last call carries 2,903, and on average a call carried 2,498. At that batch size the curve above puts one evaluation of the model alone at about 541.4 µs on the CPU and 96.8 µs on the GPU in float32. The whole routine, divided by the evaluations it made, cost 536.4 µs and 103.0 µs per evaluation: almost all of a run is the model, and proposing, discretizing and the pattern stores add little.\n\nR's single-process cost is 5.36 ms per evaluation, but its 14 processes together made one evaluation every 900.1 µs: running the participants in parallel bought 6.0× on 8 cores. Against that parallel R, the port's batched call is what remains of its advantage: 1.7× on the CPU in one process, and 8.6× on the GPU.\n\nThe batch grows with the number of participants run together. 14 participants with about 200 patterns each give a few thousand parameter sets per call; all 354 participants of the dataset would give some 70,000, where the GPU's lead per evaluation is largest, while R's processes would still share the same 8 cores.\n",
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seconds, tracking, _ = runs[\"torch cuda float32\"]\n",
    "iterations = int(tracking.iterations.max())\n",
    "per_iteration = tracking.evaluations.sum() / iterations\n",
    "start = 100 * n_ppt\n",
    "final = tracking.patterns.sum()\n",
    "\n",
    "\n",
    "def model_us(b, batch):\n",
    "    # time per evaluation of one call at `batch` parameter sets, interpolated on the log-log curve above\n",
    "    return float(np.exp(np.interp(np.log(batch), np.log(per_eval.index), np.log(per_eval[b]))))\n",
    "\n",
    "\n",
    "def routine_us(name):\n",
    "    seconds, summary, _ = runs[name]\n",
    "    return 1e6 * seconds / summary.evaluations.sum()\n",
    "\n",
    "\n",
    "display(Markdown(f'''\n",
    "Iteration 0 evaluates {start:,} parameter sets ({n_ppt} participants × 100 starting points); by the last iteration the participants hold {final:,} patterns between them, so the last call carries {final:,}, and on average a call carried {per_iteration:,.0f}. At that batch size the curve above puts one evaluation of the model alone at about {us(model_us(\"cpu float64\", per_iteration))} on the CPU and {us(model_us(\"cuda float32\", per_iteration))} on the GPU in float32. The whole routine, divided by the evaluations it made, cost {us(routine_us(\"torch cpu float64\"))} and {us(routine_us(\"torch cuda float32\"))} per evaluation: almost all of a run is the model, and proposing, discretizing and the pattern stores add little.\n",
    "\n",
    "R's single-process cost is {us(r_us[\"evaluate()\"])} per evaluation, but its {int(r_wall.workers)} processes together made one evaluation every {us(routine_us(\"R + C++\"))}: running the participants in parallel bought {r_us[\"evaluate()\"] / routine_us(\"R + C++\"):.1f}× on {os.cpu_count() // 2} cores. Against that parallel R, the port's batched call is what remains of its advantage: {times(r_seconds / runs[\"torch cpu float64\"][0])} on the CPU in one process, and {times(r_seconds / seconds)} on the GPU.\n",
    "\n",
    "The batch grows with the number of participants run together. {n_ppt} participants with about 200 patterns each give a few thousand parameter sets per call; all 354 participants of the dataset would give some 70,000, where the GPU's lead per evaluation is largest, while R's processes would still share the same {os.cpu_count() // 2} cores.\n",
    "'''))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f7a57ca",
   "metadata": {},
   "source": "### Do the builds find the same regions?\n\nThe model draws its initial state at random on every evaluation, and R and torch use different random numbers, so no two runs take the same path. Per participant, the inequality matrices each port build shares with R:"
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "557f67f4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-15T15:58:16.518590Z",
     "iopub.status.busy": "2026-10-15T15:58:16.518494Z",
     "iopub.status.idle": "2026-10-15T15:58:16.604728Z",
     "shell.execute_reply": "2026-10-15T15:58:16.604270Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>matrices shared with R, median</th>\n",
       "      <th>found by R only, median</th>\n",
       "      <th>found by the port only, median</th>\n",
       "      <th>evaluations in shared matrices, worst participant</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>build</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>torch cpu float64</th>\n",
       "      <td>196</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>R 99.7%, port 99.9%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>torch cuda float32</th>\n",
       "      <td>198</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>R 99.9%, port 99.7%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   matrices shared with R, median found by R only, median  \\\n",
       "build                                                                       \n",
       "torch cpu float64                             196                       8   \n",
       "torch cuda float32                            198                       6   \n",
       "\n",
       "                   found by the port only, median  \\\n",
       "build                                               \n",
       "torch cpu float64                               6   \n",
       "torch cuda float32                              9   \n",
       "\n",
       "                   evaluations in shared matrices, worst participant  \n",
       "build                                                                 \n",
       "torch cpu float64                                R 99.7%, port 99.9%  \n",
       "torch cuda float32                               R 99.9%, port 99.7%  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_keys = runs[\"R + C++\"][2]\n",
    "rows = []\n",
    "for name, (_, _, keys) in list(runs.items())[1:]:\n",
    "    for ppt, mine in keys.groupby(\"ppt\"):\n",
    "        theirs = r_keys[r_keys.ppt == ppt]\n",
    "        shared = set(mine.key) & set(theirs.key)\n",
    "        rows.append({\n",
    "            \"build\": name, \"ppt\": ppt, \"shared\": len(shared),\n",
    "            \"R only\": len(set(theirs.key) - shared), \"torch only\": len(set(mine.key) - shared),\n",
    "            \"R\": theirs[theirs.key.isin(shared)][\"count\"].sum() / theirs[\"count\"].sum(),\n",
    "            \"torch\": mine[mine.key.isin(shared)][\"count\"].sum() / mine[\"count\"].sum(),\n",
    "        })\n",
    "overlap = pd.DataFrame(rows)\n",
    "summary = overlap.groupby(\"build\").agg(\n",
    "    shared=(\"shared\", \"median\"), r_only=(\"R only\", \"median\"), torch_only=(\"torch only\", \"median\"),\n",
    "    r_worst=(\"R\", \"min\"), torch_worst=(\"torch\", \"min\"))\n",
    "pd.DataFrame({\n",
    "    \"matrices shared with R, median\": summary.shared.map(\"{:.0f}\".format),\n",
    "    \"found by R only, median\": summary.r_only.map(\"{:.0f}\".format),\n",
    "    \"found by the port only, median\": summary.torch_only.map(\"{:.0f}\".format),\n",
    "    \"evaluations in shared matrices, worst participant\": [f\"R {r.r_worst:.1%}, port {r.torch_worst:.1%}\" for _, r in summary.iterrows()],\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e25f6a4",
   "metadata": {},
   "source": "The matrices only one build found are the rare ones, holding one or a few evaluations each: practically every evaluation of either run falls in a matrix both found. The port finds the same partition as the C++, at a fraction of the time. That the two implementations compute the same model is checked separately, to 1e-10 on the trial-by-trial output, by `tests/test_oscar.py` and [oscar/try_oscar.py](https://github.com/lenarddome/psp-torch/blob/main/notebooks/oscar/try_oscar.py).\n\n## How this was measured"
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d874c42e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-15T15:58:16.605774Z",
     "iopub.status.busy": "2026-10-15T15:58:16.605655Z",
     "iopub.status.idle": "2026-10-15T15:58:16.842912Z",
     "shell.execute_reply": "2026-10-15T15:58:16.842509Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Environment</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CPU</th>\n",
       "      <td>AMD Ryzen 7 5800X 8-Core Processor, 16 threads</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GPU</th>\n",
       "      <td>NVIDIA GeForce RTX 3060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>System</th>\n",
       "      <td>Linux 7.0.0-38-generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>torch</th>\n",
       "      <td>2.11.0+cu128, 8 threads</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Python</th>\n",
       "      <td>3.14.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R</th>\n",
       "      <td>R version 4.5.2 (2025-10-31)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R package</th>\n",
       "      <td>psp 1.0.6 (CRAN), OSCAR compiled with Rcpp::so...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                              \n",
       "Environment                                                   \n",
       "CPU             AMD Ryzen 7 5800X 8-Core Processor, 16 threads\n",
       "GPU                                    NVIDIA GeForce RTX 3060\n",
       "System                                  Linux 7.0.0-38-generic\n",
       "torch                                  2.11.0+cu128, 8 threads\n",
       "Python                                                  3.14.4\n",
       "R                                 R version 4.5.2 (2025-10-31)\n",
       "R package    psp 1.0.6 (CRAN), OSCAR compiled with Rcpp::so..."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def r_version():\n",
    "    try:\n",
    "        return subprocess.run([\"Rscript\", \"-e\", \"cat(R.version.string)\"], capture_output=True, text=True,\n",
    "                              timeout=60).stdout.strip() or \"not found\"\n",
    "    except OSError:\n",
    "        return \"not found\"\n",
    "\n",
    "\n",
    "def cpu_name():\n",
    "    try:\n",
    "        return next(line.split(\":\", 1)[1].strip() for line in open(\"/proc/cpuinfo\") if line.startswith(\"model name\"))\n",
    "    except (OSError, StopIteration):\n",
    "        return platform.processor() or \"unknown\"\n",
    "\n",
    "\n",
    "pd.DataFrame({\"\": {\n",
    "    \"CPU\": f\"{cpu_name()}, {os.cpu_count()} threads\",\n",
    "    \"GPU\": torch.cuda.get_device_name(0) if torch.cuda.is_available() else \"none\",\n",
    "    \"System\": f\"{platform.system()} {platform.release()}\",\n",
    "    \"torch\": f\"{torch.__version__}, {torch.get_num_threads()} threads\",\n",
    "    \"Python\": platform.python_version(),\n",
    "    \"R\": r_version(),\n",
    "    \"R package\": \"psp 1.0.6 (CRAN), OSCAR compiled with Rcpp::sourceCpp() at R's default -O2\",\n",
    "}}).rename_axis(\"Environment\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85cfddd8",
   "metadata": {},
   "source": "The results are from this machine, measured with nothing else running. To measure them again (about 15 minutes), install the R package into its own library, run the benchmark and re-execute this notebook:\n\n```sh\nRscript -e 'install.packages(\"psp\", lib = \"benchmarks/oscar/r-lib\", repos = \"https://cloud.r-project.org\")'\nuv run python benchmarks/oscar/run.py --r-library benchmarks/oscar/r-lib\nuv run python notebooks/execute.py benchmarks-oscar.ipynb\n```\n\n`--participants`, `--starts`, `--iterations` and `--workers` change the routine's settings; [benchmarks/oscar/README.md](https://github.com/lenarddome/psp-torch/blob/main/benchmarks/oscar/README.md) describes every script."
  },
  {
   "cell_type": "markdown",
   "id": "b1c9fe2a",
   "metadata": {},
   "source": "## References\n\nDome, L., & Wills, A. J. (2026). Errorless Irrationality: A unified computational account of the inverse base-rate effect across predictive, observational, and unsupervised procedures (arXiv:2608.06149). arXiv. [https://doi.org/10.48550/arXiv.2608.06149](https://doi.org/10.48550/arXiv.2608.06149)"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.14.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}