[2025-06-26T17:37:43.187Z] Running test renaissance-movie-lens_0 ... [2025-06-26T17:37:43.187Z] =============================================== [2025-06-26T17:37:43.187Z] renaissance-movie-lens_0 Start Time: Thu Jun 26 17:37:42 2025 Epoch Time (ms): 1750959462249 [2025-06-26T17:37:43.187Z] variation: NoOptions [2025-06-26T17:37:43.187Z] JVM_OPTIONS: [2025-06-26T17:37:43.187Z] { \ [2025-06-26T17:37:43.187Z] echo ""; echo "TEST SETUP:"; \ [2025-06-26T17:37:43.187Z] echo "Nothing to be done for setup."; \ [2025-06-26T17:37:43.187Z] mkdir -p "/home/jenkins/workspace/Test_openjdk21_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17509580495448/renaissance-movie-lens_0"; \ [2025-06-26T17:37:43.187Z] cd "/home/jenkins/workspace/Test_openjdk21_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17509580495448/renaissance-movie-lens_0"; \ [2025-06-26T17:37:43.187Z] echo ""; echo "TESTING:"; \ [2025-06-26T17:37:43.187Z] "/home/jenkins/workspace/Test_openjdk21_hs_extended.perf_ppc64le_linux/jdkbinary/j2sdk-image/bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/home/jenkins/workspace/Test_openjdk21_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk21_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17509580495448/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \ [2025-06-26T17:37:43.187Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk21_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk21_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17509580495448/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \ [2025-06-26T17:37:43.187Z] echo ""; echo "TEST TEARDOWN:"; \ [2025-06-26T17:37:43.187Z] echo "Nothing to be done for teardown."; \ [2025-06-26T17:37:43.187Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk21_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17509580495448/TestTargetResult"; [2025-06-26T17:37:43.187Z] [2025-06-26T17:37:43.187Z] TEST SETUP: [2025-06-26T17:37:43.187Z] Nothing to be done for setup. [2025-06-26T17:37:43.187Z] [2025-06-26T17:37:43.187Z] TESTING: [2025-06-26T17:37:48.561Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads. [2025-06-26T17:37:55.282Z] 17:37:53.917 WARN [dispatcher-event-loop-2] org.apache.spark.scheduler.TaskSetManager - Stage 8 contains a task of very large size (1401 KiB). The maximum recommended task size is 1000 KiB. [2025-06-26T17:37:56.237Z] Got 100004 ratings from 671 users on 9066 movies. [2025-06-26T17:37:57.197Z] Training: 60056, validation: 20285, test: 19854 [2025-06-26T17:37:57.197Z] ====== movie-lens (apache-spark) [default], iteration 0 started ====== [2025-06-26T17:37:57.197Z] GC before operation: completed in 146.826 ms, heap usage 382.005 MB -> 75.864 MB. [2025-06-26T17:38:02.628Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-06-26T17:38:05.649Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-06-26T17:38:09.378Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-06-26T17:38:11.361Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-06-26T17:38:13.318Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-06-26T17:38:15.274Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-06-26T17:38:17.228Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-06-26T17:38:19.183Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-06-26T17:38:19.183Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2025-06-26T17:38:19.183Z] The best model improves the baseline by 14.52%. [2025-06-26T17:38:19.183Z] Top recommended movies for user id 72: [2025-06-26T17:38:19.183Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2025-06-26T17:38:19.183Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2025-06-26T17:38:19.183Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2025-06-26T17:38:19.183Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2025-06-26T17:38:19.183Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2025-06-26T17:38:19.183Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (22433.414 ms) ====== [2025-06-26T17:38:19.183Z] ====== movie-lens (apache-spark) [default], iteration 1 started ====== [2025-06-26T17:38:19.183Z] GC before operation: completed in 139.982 ms, heap usage 133.110 MB -> 86.395 MB. [2025-06-26T17:38:22.237Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-06-26T17:38:25.256Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-06-26T17:38:28.281Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-06-26T17:38:30.298Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-06-26T17:38:32.262Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-06-26T17:38:33.215Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-06-26T17:38:35.178Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-06-26T17:38:37.133Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-06-26T17:38:37.133Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2025-06-26T17:38:37.133Z] The best model improves the baseline by 14.52%. [2025-06-26T17:38:37.133Z] Top recommended movies for user id 72: [2025-06-26T17:38:37.133Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2025-06-26T17:38:37.133Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2025-06-26T17:38:37.133Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2025-06-26T17:38:37.133Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2025-06-26T17:38:37.133Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2025-06-26T17:38:37.133Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (17670.411 ms) ====== [2025-06-26T17:38:37.133Z] ====== movie-lens (apache-spark) [default], iteration 2 started ====== [2025-06-26T17:38:37.133Z] GC before operation: completed in 141.300 ms, heap usage 104.636 MB -> 88.584 MB. [2025-06-26T17:38:40.152Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-06-26T17:38:42.111Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-06-26T17:38:45.135Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-06-26T17:38:47.092Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-06-26T17:38:49.049Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-06-26T17:38:50.002Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-06-26T17:38:51.954Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-06-26T17:38:53.909Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-06-26T17:38:53.909Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2025-06-26T17:38:53.909Z] The best model improves the baseline by 14.52%. [2025-06-26T17:38:53.909Z] Top recommended movies for user id 72: [2025-06-26T17:38:53.909Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2025-06-26T17:38:53.909Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2025-06-26T17:38:53.909Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2025-06-26T17:38:53.909Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2025-06-26T17:38:53.909Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2025-06-26T17:38:53.909Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (16690.220 ms) ====== [2025-06-26T17:38:53.909Z] ====== movie-lens (apache-spark) [default], iteration 3 started ====== [2025-06-26T17:38:53.909Z] GC before operation: completed in 150.161 ms, heap usage 481.862 MB -> 89.704 MB. [2025-06-26T17:38:56.601Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-06-26T17:38:59.651Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-06-26T17:39:01.625Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-06-26T17:39:04.663Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-06-26T17:39:05.615Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-06-26T17:39:07.569Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-06-26T17:39:09.528Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-06-26T17:39:10.480Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-06-26T17:39:11.434Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2025-06-26T17:39:11.434Z] The best model improves the baseline by 14.52%. [2025-06-26T17:39:11.434Z] Top recommended movies for user id 72: [2025-06-26T17:39:11.434Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2025-06-26T17:39:11.434Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2025-06-26T17:39:11.434Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2025-06-26T17:39:11.434Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2025-06-26T17:39:11.434Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2025-06-26T17:39:11.434Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (17055.191 ms) ====== [2025-06-26T17:39:11.434Z] ====== movie-lens (apache-spark) [default], iteration 4 started ====== [2025-06-26T17:39:11.434Z] GC before operation: completed in 142.602 ms, heap usage 342.975 MB -> 89.665 MB. [2025-06-26T17:39:13.388Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-06-26T17:39:16.600Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-06-26T17:39:18.557Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-06-26T17:39:20.516Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-06-26T17:39:22.478Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-06-26T17:39:24.433Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-06-26T17:39:25.386Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-06-26T17:39:26.338Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-06-26T17:39:27.289Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2025-06-26T17:39:27.289Z] The best model improves the baseline by 14.52%. [2025-06-26T17:39:27.289Z] Top recommended movies for user id 72: [2025-06-26T17:39:27.289Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2025-06-26T17:39:27.289Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2025-06-26T17:39:27.289Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2025-06-26T17:39:27.289Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2025-06-26T17:39:27.289Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2025-06-26T17:39:27.289Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (15879.434 ms) ====== [2025-06-26T17:39:27.289Z] ====== movie-lens (apache-spark) [default], iteration 5 started ====== [2025-06-26T17:39:27.289Z] GC before operation: completed in 139.571 ms, heap usage 243.533 MB -> 89.579 MB. [2025-06-26T17:39:29.260Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-06-26T17:39:32.290Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-06-26T17:39:34.254Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-06-26T17:39:36.215Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-06-26T17:39:37.168Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-06-26T17:39:39.131Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-06-26T17:39:40.095Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-06-26T17:39:41.517Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-06-26T17:39:41.517Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2025-06-26T17:39:41.517Z] The best model improves the baseline by 14.52%. [2025-06-26T17:39:41.517Z] Top recommended movies for user id 72: [2025-06-26T17:39:41.517Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2025-06-26T17:39:41.517Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2025-06-26T17:39:41.517Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2025-06-26T17:39:41.517Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2025-06-26T17:39:41.517Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2025-06-26T17:39:41.517Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (14294.309 ms) ====== [2025-06-26T17:39:41.517Z] ====== movie-lens (apache-spark) [default], iteration 6 started ====== [2025-06-26T17:39:41.517Z] GC before operation: completed in 202.742 ms, heap usage 294.441 MB -> 89.999 MB. [2025-06-26T17:39:44.542Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-06-26T17:39:46.495Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-06-26T17:39:48.451Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-06-26T17:39:50.450Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-06-26T17:39:51.405Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-06-26T17:39:53.364Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-06-26T17:39:54.315Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-06-26T17:39:55.268Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-06-26T17:39:55.268Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2025-06-26T17:39:56.219Z] The best model improves the baseline by 14.52%. [2025-06-26T17:39:56.219Z] Top recommended movies for user id 72: [2025-06-26T17:39:56.219Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2025-06-26T17:39:56.219Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2025-06-26T17:39:56.219Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2025-06-26T17:39:56.219Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2025-06-26T17:39:56.219Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2025-06-26T17:39:56.219Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (14017.845 ms) ====== [2025-06-26T17:39:56.219Z] ====== movie-lens (apache-spark) [default], iteration 7 started ====== [2025-06-26T17:39:56.219Z] GC before operation: completed in 132.257 ms, heap usage 211.022 MB -> 89.866 MB. [2025-06-26T17:39:58.176Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-06-26T17:40:00.133Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-06-26T17:40:02.122Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-06-26T17:40:04.075Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-06-26T17:40:06.031Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-06-26T17:40:06.985Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. Calling Pipeline was cancelled [2025-06-26T17:40:07.335Z] Sending interrupt signal to process [2025-06-26T17:40:08.033Z] ====== movie-lens (apache-spark) [default], iteration 7 failed (SparkException) ====== [2025-06-26T17:40:08.033Z] ----------------------------------- [2025-06-26T17:40:08.033Z] renaissance-movie-lens_0_FAILED [2025-06-26T17:40:08.033Z] ----------------------------------- [2025-06-26T17:40:08.033Z] [2025-06-26T17:40:08.033Z] TEST TEARDOWN: [2025-06-26T17:40:08.033Z] Nothing to be done for teardown. [2025-06-26T17:40:08.033Z] renaissance-movie-lens_0 Finish Time: Thu Jun 26 17:40:07 2025 Epoch Time (ms): 1750959607708