Fix ClusterVector move semantics

python/tests/ClusterFinderCUDA.ipynb

@@ -65,7 +65,7 @@
                 "N           = 88999\n",
                 "cluster_size = (9, 9)\n",
                 "image_size   = (f.rows, f.cols)\n",
-                "capacity     = 100_000 #3_000_000\n",
+                "capacity     = 1_000 #3_000_000\n",
                 "\n",
                 "print(f'Image size:       {image_size}')\n",
                 "print(f'Pedestal frames:  {n_frames_pd}')\n",
@@ -154,7 +154,7 @@
                     "name": "stdout",
                     "output_type": "stream",
                     "text": [
-                        "Pedestal (1000 frames): 0.501s\n"
+                        "Pedestal (1000 frames): 0.515s\n"
                     ]
                 }
             ],
@@ -185,7 +185,7 @@
                     "name": "stdout",
                     "output_type": "stream",
                     "text": [
-                        "Reading 88999 frames:        30.101s  (2957 FPS, 902.311 GB/s)\n"
+                        "Reading 88999 frames:        4.574s  (19458 FPS, 5938.097 GB/s)\n"
                     ]
                 }
             ],
@@ -216,7 +216,7 @@
                     "name": "stdout",
                     "output_type": "stream",
                     "text": [
-                        "CPU clustering:          835.664s (107 FPS, 90799856 clusters, 1020.23/frame)\n"
+                        "CPU clustering:          837.700s (106 FPS, 90799856 clusters, 1020.23/frame)\n"
                     ]
                 }
             ],
@@ -257,7 +257,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 11,
+            "execution_count": 19,
             "id": "4f94cf35-5796-463d-bed8-f44a02d91fc6",
             "metadata": {},
             "outputs": [],
@@ -267,13 +267,13 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 18,
+            "execution_count": 20,
             "id": "50848f8c-2e66-45b8-b6d5-ca80f368e6ba",
             "metadata": {},
             "outputs": [],
             "source": [
                 "if(BATCHED):\n",
-                "    BATCH_SIZE = 2000\n",
+                "    BATCH_SIZE = 5000\n",
                 "\n",
                 "    # Warmup: first kernel launch pays CUDA context + pedestal H2D upload cost\n",
                 "    _ = cf_cuda.find_clusters_batched(data[0:BATCH_SIZE], first_frame=0)\n",
@@ -322,7 +322,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 19,
+            "execution_count": 21,
             "id": "502d0d3b-6b1e-4cc9-91df-9d998bd849b5",
             "metadata": {},
             "outputs": [
@@ -332,7 +332,7 @@
                             "(9, 9)"
                         ]
                     },
-                    "execution_count": 19,
+                    "execution_count": 21,
                     "metadata": {},
                     "output_type": "execute_result"
                 }
@@ -343,7 +343,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 25,
+            "execution_count": 22,
             "id": "2e3e4b9c-7f23-4fb7-bc3e-fa3d766d51fc",
             "metadata": {},
             "outputs": [
@@ -351,7 +351,7 @@
                     "name": "stdout",
                     "output_type": "stream",
                     "text": [
-                        "CPU clustering:          835.664s (107 FPS, 90799856 clusters, 1020.23/frame)\n"
+                        "CPU clustering:          837.700s (106 FPS, 90799856 clusters, 1020.23/frame)\n"
                     ]
                 }
             ],
@@ -362,7 +362,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 24,
+            "execution_count": 23,
             "id": "4b8df93b-9a1b-41a5-9fed-9cda295fb523",
             "metadata": {},
             "outputs": [
@@ -370,10 +370,10 @@
                     "name": "stdout",
                     "output_type": "stream",
                     "text": [
-                        "CUDA clustering:          8.812s  (10100 FPS, 89995010 clusters, 1011.19/frame)\n",
-                        "  Kernel only:            0.039 ms/frame\n",
-                        "  PCIe + overhead:        0.060 ms/frame\n",
-                        "Speedup (CPU / CUDA):     94.83×\n"
+                        "CUDA clustering:          6.261s  (14214 FPS, 88537254 clusters, 994.81/frame)\n",
+                        "  Kernel only:            0.040 ms/frame\n",
+                        "  PCIe + overhead:        0.030 ms/frame\n",
+                        "Speedup (CPU / CUDA):     133.79×\n"
                     ]
                 }
             ],
@@ -397,7 +397,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 21,
+            "execution_count": 24,
             "id": "d3a850df-7df0-485b-971d-381cfdc6be81",
             "metadata": {},
             "outputs": [
@@ -405,7 +405,7 @@
                     "name": "stdout",
                     "output_type": "stream",
                     "text": [
-                        "Cluster count diff:       804846 (0.89%)\n"
+                        "Cluster count diff:       2262602 (2.49%)\n"
                     ]
                 }
             ],
@@ -425,7 +425,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 22,
+            "execution_count": 25,
             "id": "aff8db7e-8332-4228-857f-a9875cf940d3",
             "metadata": {},
             "outputs": [
@@ -445,14 +445,14 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 23,
+            "execution_count": 26,
             "id": "9adeea2f-9309-4c0a-905b-7d1203ba1f4e",
             "metadata": {},
             "outputs": [
                 {
                     "data": {
                         "image/png":
-                            "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 truncated
+                            "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 truncated
                         "text/plain": [
                             "<Figure size 800x600 with 2 Axes>"
                         ]