make julia go vrrrm

jakee417 · Mar 7, 2024 · 1f788ec · 1f788ec
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diff --git a/_includes/ising_model_speed.ipynb b/_includes/ising_model_speed.ipynb
@@ -282,7 +282,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Although faster than Python, it is still much slower than Cython."
+    "Although faster than Python, it is still ~2x slower than Cython."
    ]
   },
   {
@@ -1839,7 +1839,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -1849,7 +1849,7 @@
       "[NbConvertApp] Converting notebook ising_model_speed.ipynb to markdown\n",
       "/Users/jakeetaylor/opt/anaconda3/lib/python3.9/site-packages/nbconvert/filters/datatypefilter.py:39: UserWarning: Your element with mimetype(s) dict_keys(['application/vnd.plotly.v1+json']) is not able to be represented.\n",
       "  warn(\"Your element with mimetype(s) {mimetypes}\"\n",
-      "[NbConvertApp] Writing 23203 bytes to ising_model_speed.md\n"
+      "[NbConvertApp] Writing 23202 bytes to ising_model_speed.md\n"
      ]
     },
     {
@@ -1858,7 +1858,7 @@
        "CompletedProcess(args=['jupyter', 'nbconvert', '--to', 'markdown', 'ising_model_speed.ipynb'], returncode=0)"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }

diff --git a/_includes/ising_model_speed.md b/_includes/ising_model_speed.md
@@ -187,7 +187,7 @@ numba_ising_step(field)
     1.09 ms ± 7.63 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)
 
 
-Although faster than Python, it is still much slower than Cython.
+Although faster than Python, it is still ~2x slower than Cython.
 
 ### @njit(parallel=True) ~ 650µs
 Before trying to speed this up, lets first understand a common exploit of the Ising Model. First, lets extract our outer for-loops and simply keep record of the `n`'s, `m`s, and offset.

diff --git a/_includes/ising_model_speed_2.ipynb b/_includes/ising_model_speed_2.ipynb
@@ -38,15 +38,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
     "fn random_spin_field(N: Int, M: Int) -> Tensor[data_type]:\n",
     "    var t = rand[data_type](N, M)\n",
     "    for i in range(N):\n",
     "        for j in range(M):\n",
-    "            if t[Index(i, j)] < 0.5:\n",
+    "            if t[i, j] < 0.5:\n",
     "                t[Index(i, j)] = -1\n",
     "            else:\n",
     "                t[Index(i, j)] = 1\n",
@@ -62,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -75,11 +75,11 @@
     "        for j in range(m - 1, m + 2):\n",
     "            if i == n and j == m:\n",
     "                continue\n",
-    "            total += field[Index(i % N, j % M)]\n",
-    "    var dE = 2 * field[Index(n, m)] * total\n",
+    "            total += field[i % N, j % M]\n",
+    "    var dE = 2 * field[n, m] * total\n",
     "    if dE <= 0:\n",
     "        field[Index(n, m)] *= -1\n",
-    "    elif exp(-dE * beta) > rand[data_type](1)[Index(0)]:\n",
+    "    elif exp(-dE * beta) > rand[data_type](1)[0]:\n",
     "        field[Index(n, m)] *= -1"
    ]
   },
@@ -92,7 +92,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -116,29 +116,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@always_inline\n",
-    "fn bench() -> Report:\n",
-    "    var N = 200\n",
-    "    var M = 200\n",
-    "    var field = random_spin_field(N, M)\n",
-    "\n",
-    "    @always_inline\n",
-    "    @parameter\n",
-    "    fn ising_step_fn():\n",
-    "        ising_step(field=field)\n",
-    "\n",
-    "    return benchmark.run[ising_step_fn](max_runtime_secs=10)\n",
-    "\n",
-    "var report = bench()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -148,19 +126,33 @@
       "---------------------\n",
       "Benchmark Report (ms)\n",
       "---------------------\n",
-      "Mean: 2.915081212121212\n",
-      "Total: 2404.942\n",
+      "Mean: 2.9266933333333331\n",
+      "Total: 2414.5219999999999\n",
       "Iters: 825\n",
-      "Warmup Mean: 2.508\n",
-      "Warmup Total: 5.016\n",
+      "Warmup Mean: 2.5630000000000002\n",
+      "Warmup Total: 5.1260000000000003\n",
       "Warmup Iters: 2\n",
-      "Fastest Mean: 2.915081212121212\n",
-      "Slowest Mean: 2.915081212121212\n",
+      "Fastest Mean: 2.9266933333333336\n",
+      "Slowest Mean: 2.9266933333333336\n",
       "\n"
      ]
     }
    ],
    "source": [
+    "@always_inline\n",
+    "fn bench() -> Report:\n",
+    "    var N = 200\n",
+    "    var M = 200\n",
+    "    var field = random_spin_field(N, M)\n",
+    "\n",
+    "    @always_inline\n",
+    "    @parameter\n",
+    "    fn ising_step_fn():\n",
+    "        ising_step(field=field)\n",
+    "\n",
+    "    return benchmark.run[ising_step_fn](max_runtime_secs=10)\n",
+    "\n",
+    "var report = bench()\n",
     "# Print a report in Milliseconds\n",
     "report.print(\"ms\")"
    ]
@@ -174,15 +166,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "[NbConvertApp] Converting notebook ising_model_speed_2.ipynb to markdown\n",
-      "[NbConvertApp] Writing 3067 bytes to ising_model_speed_2.md\n"
+      "[NbConvertApp] Writing 3064 bytes to ising_model_speed_2.md\n"
      ]
     }
    ],

diff --git a/_includes/ising_model_speed_2.md b/_includes/ising_model_speed_2.md
@@ -26,7 +26,7 @@ fn random_spin_field(N: Int, M: Int) -> Tensor[data_type]:
     var t = rand[data_type](N, M)
     for i in range(N):
         for j in range(M):
-            if t[Index(i, j)] < 0.5:
+            if t[i, j] < 0.5:
                 t[Index(i, j)] = -1
             else:
                 t[Index(i, j)] = 1
@@ -46,11 +46,11 @@ fn _ising_update(inout field: Tensor[data_type], n: Int, m: Int, beta: Float32)
         for j in range(m - 1, m + 2):
             if i == n and j == m:
                 continue
-            total += field[Index(i % N, j % M)]
-    var dE = 2 * field[Index(n, m)] * total
+            total += field[i % N, j % M]
+    var dE = 2 * field[n, m] * total
     if dE <= 0:
         field[Index(n, m)] *= -1
-    elif exp(-dE * beta) > rand[data_type](1)[Index(0)]:
+    elif exp(-dE * beta) > rand[data_type](1)[0]:
         field[Index(n, m)] *= -1
 ```
 
@@ -87,25 +87,21 @@ fn bench() -> Report:
     return benchmark.run[ising_step_fn](max_runtime_secs=10)
 
 var report = bench()
-```
-
-
-```python
 # Print a report in Milliseconds
 report.print("ms")
 ```
 
     ---------------------
     Benchmark Report (ms)
     ---------------------
-    Mean: 2.915081212121212
-    Total: 2404.942
+    Mean: 2.9266933333333331
+    Total: 2414.5219999999999
     Iters: 825
-    Warmup Mean: 2.508
-    Warmup Total: 5.016
+    Warmup Mean: 2.5630000000000002
+    Warmup Total: 5.1260000000000003
     Warmup Iters: 2
-    Fastest Mean: 2.915081212121212
-    Slowest Mean: 2.915081212121212
+    Fastest Mean: 2.9266933333333336
+    Slowest Mean: 2.9266933333333336
 
 
 

diff --git a/_includes/ising_model_speed_2.md-e b/_includes/ising_model_speed_2.md-e
@@ -26,7 +26,7 @@ fn random_spin_field(N: Int, M: Int) -> Tensor[data_type]:
     var t = rand[data_type](N, M)
     for i in range(N):
         for j in range(M):
-            if t[Index(i, j)] < 0.5:
+            if t[i, j] < 0.5:
                 t[Index(i, j)] = -1
             else:
                 t[Index(i, j)] = 1
@@ -46,11 +46,11 @@ fn _ising_update(inout field: Tensor[data_type], n: Int, m: Int, beta: Float32)
         for j in range(m - 1, m + 2):
             if i == n and j == m:
                 continue
-            total += field[Index(i % N, j % M)]
-    var dE = 2 * field[Index(n, m)] * total
+            total += field[i % N, j % M]
+    var dE = 2 * field[n, m] * total
     if dE <= 0:
         field[Index(n, m)] *= -1
-    elif exp(-dE * beta) > rand[data_type](1)[Index(0)]:
+    elif exp(-dE * beta) > rand[data_type](1)[0]:
         field[Index(n, m)] *= -1
 ```
 
@@ -87,25 +87,21 @@ fn bench() -> Report:
     return benchmark.run[ising_step_fn](max_runtime_secs=10)
 
 var report = bench()
-```
-
-
-```mojo
 # Print a report in Milliseconds
 report.print("ms")
 ```
 
     ---------------------
     Benchmark Report (ms)
     ---------------------
-    Mean: 2.915081212121212
-    Total: 2404.942
+    Mean: 2.9266933333333331
+    Total: 2414.5219999999999
     Iters: 825
-    Warmup Mean: 2.508
-    Warmup Total: 5.016
+    Warmup Mean: 2.5630000000000002
+    Warmup Total: 5.1260000000000003
     Warmup Iters: 2
-    Fastest Mean: 2.915081212121212
-    Slowest Mean: 2.915081212121212
+    Fastest Mean: 2.9266933333333336
+    Slowest Mean: 2.9266933333333336