Restore timing accuracy

Author: awf

docs/source/04-benchmark.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -16,10 +16,7 @@
     "from gfloat.formats import format_info_ocp_e5m2\n",
     "from timeit import Timer\n",
     "\n",
-    "jax.config.update(\"jax_enable_x64\", True)\n",
-    "\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2"
+    "jax.config.update(\"jax_enable_x64\", True)"
    ]
   },
   {
@@ -37,17 +34,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "GFloat scalar                  :  7518.31 nsec (5 runs at size 10000)\n",
-      "GFloat vectorized, numpy arrays:    57.95 nsec (5 runs at size 1000000)\n",
-      "GFloat vectorized, JAX JIT     :     4.03 nsec (100 runs at size 1000000)\n",
-      "ML_dtypes                      :     3.34 nsec (100 runs at size 1000000)\n"
+      "GFloat scalar                  :  6062.08 nsec (25 runs at size 10000)\n",
+      "GFloat vectorized, numpy arrays:    53.39 nsec (25 runs at size 1000000)\n",
+      "GFloat vectorized, JAX JIT     :     3.48 nsec (500 runs at size 1000000)\n",
+      "ML_dtypes                      :     3.27 nsec (500 runs at size 1000000)\n"
      ]
     }
    ],
@@ -65,7 +69,7 @@
     "\n",
     "\n",
     "# About how many seconds to run for (autorange will take at least .2 sec)\n",
-    "ACCURACY = 0.2\n",
+    "ACCURACY = 1.0\n",
     "\n",
     "\n",
     "def time(f, problem_size=1.0):\n",