[2026-03-01T06:29:23.785Z] Running test renaissance-movie-lens_0 ... [2026-03-01T06:29:23.785Z] =============================================== [2026-03-01T06:29:23.785Z] renaissance-movie-lens_0 Start Time: Sun Mar 1 06:29:19 2026 Epoch Time (ms): 1772346559925 [2026-03-01T06:29:23.785Z] variation: NoOptions [2026-03-01T06:29:23.785Z] JVM_OPTIONS: [2026-03-01T06:29:23.785Z] { \ [2026-03-01T06:29:23.785Z] echo ""; echo "TEST SETUP:"; \ [2026-03-01T06:29:23.785Z] echo "Nothing to be done for setup."; \ [2026-03-01T06:29:23.785Z] mkdir -p "/home/jenkins/workspace/Test_openjdk25_hs_extended.perf_riscv64_linux_testList_2/aqa-tests/TKG/../TKG/output_1772343882276/renaissance-movie-lens_0"; \ [2026-03-01T06:29:23.785Z] cd "/home/jenkins/workspace/Test_openjdk25_hs_extended.perf_riscv64_linux_testList_2/aqa-tests/TKG/../TKG/output_1772343882276/renaissance-movie-lens_0"; \ [2026-03-01T06:29:23.785Z] echo ""; echo "TESTING:"; \ [2026-03-01T06:29:23.785Z] "/home/jenkins/workspace/Test_openjdk25_hs_extended.perf_riscv64_linux_testList_2/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_openjdk25_hs_extended.perf_riscv64_linux_testList_2/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk25_hs_extended.perf_riscv64_linux_testList_2/aqa-tests/TKG/../TKG/output_1772343882276/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \ [2026-03-01T06:29:23.785Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk25_hs_extended.perf_riscv64_linux_testList_2/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk25_hs_extended.perf_riscv64_linux_testList_2/aqa-tests/TKG/../TKG/output_1772343882276/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \ [2026-03-01T06:29:23.785Z] echo ""; echo "TEST TEARDOWN:"; \ [2026-03-01T06:29:23.785Z] echo "Nothing to be done for teardown."; \ [2026-03-01T06:29:23.785Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk25_hs_extended.perf_riscv64_linux_testList_2/aqa-tests/TKG/../TKG/output_1772343882276/TestTargetResult"; [2026-03-01T06:29:23.785Z] [2026-03-01T06:29:23.785Z] TEST SETUP: [2026-03-01T06:29:23.785Z] Nothing to be done for setup. [2026-03-01T06:29:23.785Z] [2026-03-01T06:29:23.785Z] TESTING: [2026-03-01T06:29:23.785Z] WARNING: A terminally deprecated method in sun.misc.Unsafe has been called [2026-03-01T06:29:23.785Z] WARNING: sun.misc.Unsafe::objectFieldOffset has been called by scala.runtime.LazyVals$ (file:/home/jenkins/workspace/Test_openjdk25_hs_extended.perf_riscv64_linux_testList_2/aqa-tests/TKG/output_1772343882276/renaissance-movie-lens_0/launcher-062920-12194424033709885325/renaissance-harness_3/lib/scala3-library_3-3.3.4.jar) [2026-03-01T06:29:23.785Z] WARNING: Please consider reporting this to the maintainers of class scala.runtime.LazyVals$ [2026-03-01T06:29:23.785Z] WARNING: sun.misc.Unsafe::objectFieldOffset will be removed in a future release [2026-03-01T06:29:47.053Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads. [2026-03-01T06:30:14.991Z] 06:30:14.413 WARN [dispatcher-event-loop-1] org.apache.spark.scheduler.TaskSetManager - Stage 8 contains a task of very large size (1401 KiB). The maximum recommended task size is 1000 KiB. [2026-03-01T06:30:23.988Z] Got 100004 ratings from 671 users on 9066 movies. [2026-03-01T06:30:26.320Z] Training: 60056, validation: 20285, test: 19854 [2026-03-01T06:30:26.320Z] ====== movie-lens (apache-spark) [default], iteration 0 started ====== [2026-03-01T06:30:26.661Z] GC before operation: completed in 608.987 ms, heap usage 490.026 MB -> 76.036 MB. [2026-03-01T06:30:54.590Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2026-03-01T06:31:05.722Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2026-03-01T06:31:18.983Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2026-03-01T06:31:29.921Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2026-03-01T06:31:37.307Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2026-03-01T06:31:43.295Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2026-03-01T06:31:49.694Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2026-03-01T06:31:55.680Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2026-03-01T06:31:56.508Z] 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. [2026-03-01T06:31:56.847Z] The best model improves the baseline by 14.52%. [2026-03-01T06:31:57.573Z] Top recommended movies for user id 72: [2026-03-01T06:31:57.573Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2026-03-01T06:31:57.573Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2026-03-01T06:31:57.573Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2026-03-01T06:31:57.573Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2026-03-01T06:31:57.573Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2026-03-01T06:31:57.573Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (90885.813 ms) ====== [2026-03-01T06:31:57.573Z] ====== movie-lens (apache-spark) [default], iteration 1 started ====== [2026-03-01T06:31:58.773Z] GC before operation: completed in 1008.471 ms, heap usage 1.213 GB -> 94.194 MB. [2026-03-01T06:32:09.703Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2026-03-01T06:32:20.637Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2026-03-01T06:32:29.645Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2026-03-01T06:32:38.643Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2026-03-01T06:32:43.474Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2026-03-01T06:32:49.526Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2026-03-01T06:32:55.526Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2026-03-01T06:33:00.366Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2026-03-01T06:33:01.541Z] 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. [2026-03-01T06:33:01.541Z] The best model improves the baseline by 14.52%. [2026-03-01T06:33:02.277Z] Top recommended movies for user id 72: [2026-03-01T06:33:02.278Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2026-03-01T06:33:02.278Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2026-03-01T06:33:02.278Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2026-03-01T06:33:02.278Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2026-03-01T06:33:02.278Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2026-03-01T06:33:02.278Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (63630.426 ms) ====== [2026-03-01T06:33:02.278Z] ====== movie-lens (apache-spark) [default], iteration 2 started ====== [2026-03-01T06:33:03.476Z] GC before operation: completed in 928.707 ms, heap usage 173.874 MB -> 88.612 MB. [2026-03-01T06:33:12.492Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2026-03-01T06:33:21.488Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2026-03-01T06:33:30.474Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2026-03-01T06:33:39.608Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2026-03-01T06:33:43.466Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2026-03-01T06:33:48.295Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2026-03-01T06:33:54.276Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2026-03-01T06:33:59.089Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2026-03-01T06:33:59.429Z] 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. [2026-03-01T06:33:59.766Z] The best model improves the baseline by 14.52%. [2026-03-01T06:34:00.499Z] Top recommended movies for user id 72: [2026-03-01T06:34:00.499Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2026-03-01T06:34:00.499Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2026-03-01T06:34:00.499Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2026-03-01T06:34:00.499Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2026-03-01T06:34:00.499Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2026-03-01T06:34:00.499Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (57314.205 ms) ====== [2026-03-01T06:34:00.499Z] ====== movie-lens (apache-spark) [default], iteration 3 started ====== [2026-03-01T06:34:01.240Z] GC before operation: completed in 880.449 ms, heap usage 132.120 MB -> 89.309 MB. [2026-03-01T06:34:10.235Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2026-03-01T06:34:19.266Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2026-03-01T06:34:28.241Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2026-03-01T06:34:37.232Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2026-03-01T06:34:42.060Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2026-03-01T06:34:46.898Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2026-03-01T06:34:52.913Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2026-03-01T06:34:57.740Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2026-03-01T06:34:58.468Z] 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. [2026-03-01T06:34:58.810Z] The best model improves the baseline by 14.52%. [2026-03-01T06:34:59.147Z] Top recommended movies for user id 72: [2026-03-01T06:34:59.147Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2026-03-01T06:34:59.147Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2026-03-01T06:34:59.147Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2026-03-01T06:34:59.147Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2026-03-01T06:34:59.147Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2026-03-01T06:34:59.147Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (57838.545 ms) ====== [2026-03-01T06:34:59.147Z] ====== movie-lens (apache-spark) [default], iteration 4 started ====== [2026-03-01T06:34:59.877Z] GC before operation: completed in 772.290 ms, heap usage 395.057 MB -> 89.893 MB. [2026-03-01T06:35:09.008Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2026-03-01T06:35:18.005Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2026-03-01T06:35:26.985Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2026-03-01T06:35:36.007Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2026-03-01T06:35:40.837Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2026-03-01T06:35:46.843Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2026-03-01T06:35:51.708Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2026-03-01T06:35:57.695Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2026-03-01T06:35:58.035Z] 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. [2026-03-01T06:35:58.035Z] The best model improves the baseline by 14.52%. [2026-03-01T06:35:58.776Z] Top recommended movies for user id 72: [2026-03-01T06:35:58.776Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2026-03-01T06:35:58.776Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2026-03-01T06:35:58.776Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2026-03-01T06:35:58.776Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2026-03-01T06:35:58.776Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2026-03-01T06:35:58.776Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (58711.522 ms) ====== [2026-03-01T06:35:58.776Z] ====== movie-lens (apache-spark) [default], iteration 5 started ====== [2026-03-01T06:35:59.518Z] GC before operation: completed in 799.132 ms, heap usage 361.428 MB -> 89.919 MB. [2026-03-01T06:36:10.447Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2026-03-01T06:36:17.810Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2026-03-01T06:36:26.791Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2026-03-01T06:36:35.780Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2026-03-01T06:36:35.780Z] 06:36:34.367 ERROR [Executor task launch worker for task 0.0 in stage 8590.0 (TID 8429)] org.apache.spark.executor.Executor - Exception in task 0.0 in stage 8590.0 (TID 8429) [2026-03-01T06:36:35.780Z] java.lang.ArrayStoreException: null [2026-03-01T06:36:35.780Z] 06:36:34.469 WARN [task-result-getter-0] org.apache.spark.scheduler.TaskSetManager - Lost task 0.0 in stage 8590.0 (TID 8429) (62.210.163.135 executor driver): java.lang.ArrayStoreException [2026-03-01T06:36:35.780Z] [2026-03-01T06:36:35.780Z] 06:36:34.481 ERROR [task-result-getter-0] org.apache.spark.scheduler.TaskSetManager - Task 0 in stage 8590.0 failed 1 times; aborting job [2026-03-01T06:36:35.780Z] ====== movie-lens (apache-spark) [default], iteration 5 failed (SparkException) ====== [2026-03-01T06:36:35.780Z] Benchmark 'movie-lens' failed with exception: [2026-03-01T06:36:35.780Z] org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 8590.0 failed 1 times, most recent failure: Lost task 0.0 in stage 8590.0 (TID 8429) (62.210.163.135 executor driver): java.lang.ArrayStoreException [2026-03-01T06:36:35.780Z] [2026-03-01T06:36:35.780Z] Driver stacktrace: [2026-03-01T06:36:35.780Z] at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2856) [2026-03-01T06:36:35.780Z] at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2792) [2026-03-01T06:36:35.780Z] at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2791) [2026-03-01T06:36:35.780Z] at scala.collection.immutable.List.foreach(List.scala:334) [2026-03-01T06:36:35.780Z] at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2791) [2026-03-01T06:36:35.780Z] at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1247) [2026-03-01T06:36:35.780Z] at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1247) [2026-03-01T06:36:35.780Z] at scala.Option.foreach(Option.scala:437) [2026-03-01T06:36:35.780Z] at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1247) [2026-03-01T06:36:35.780Z] at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:3060) [2026-03-01T06:36:35.780Z] at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2994) [2026-03-01T06:36:35.780Z] at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2983) [2026-03-01T06:36:35.780Z] at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49) [2026-03-01T06:36:35.780Z] at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:989) [2026-03-01T06:36:35.780Z] at org.apache.spark.SparkContext.runJob(SparkContext.scala:2393) [2026-03-01T06:36:35.780Z] at org.apache.spark.SparkContext.runJob(SparkContext.scala:2414) [2026-03-01T06:36:35.780Z] at org.apache.spark.SparkContext.runJob(SparkContext.scala:2433) [2026-03-01T06:36:35.780Z] at org.apache.spark.rdd.RDD.$anonfun$take$1(RDD.scala:1492) [2026-03-01T06:36:35.780Z] at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) [2026-03-01T06:36:35.780Z] at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112) [2026-03-01T06:36:35.780Z] at org.apache.spark.rdd.RDD.withScope(RDD.scala:410) [2026-03-01T06:36:35.780Z] at org.apache.spark.rdd.RDD.take(RDD.scala:1465) [2026-03-01T06:36:35.780Z] at org.apache.spark.rdd.RDD.$anonfun$isEmpty$1(RDD.scala:1602) [2026-03-01T06:36:35.780Z] at scala.runtime.java8.JFunction0$mcZ$sp.apply(JFunction0$mcZ$sp.scala:17) [2026-03-01T06:36:35.780Z] at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) [2026-03-01T06:36:35.780Z] at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112) [2026-03-01T06:36:35.780Z] at org.apache.spark.rdd.RDD.withScope(RDD.scala:410) [2026-03-01T06:36:35.780Z] at org.apache.spark.rdd.RDD.isEmpty(RDD.scala:1602) [2026-03-01T06:36:35.780Z] at org.apache.spark.mllib.recommendation.ALS.run(ALS.scala:241) [2026-03-01T06:36:35.780Z] at org.renaissance.apache.spark.MovieLens$MovieLensHelper.trainModel(MovieLens.scala:264) [2026-03-01T06:36:35.780Z] at org.renaissance.apache.spark.MovieLens$MovieLensHelper.$anonfun$trainModels$1(MovieLens.scala:240) [2026-03-01T06:36:35.780Z] at org.renaissance.apache.spark.MovieLens$MovieLensHelper.$anonfun$trainModels$1$adapted(MovieLens.scala:239) [2026-03-01T06:36:35.780Z] at scala.collection.IterableOnceOps.foreach(IterableOnce.scala:619) [2026-03-01T06:36:35.780Z] at scala.collection.IterableOnceOps.foreach$(IterableOnce.scala:617) [2026-03-01T06:36:35.780Z] at scala.collection.AbstractIterable.foreach(Iterable.scala:935) [2026-03-01T06:36:35.780Z] at org.renaissance.apache.spark.MovieLens$MovieLensHelper.trainModels(MovieLens.scala:239) [2026-03-01T06:36:35.780Z] at org.renaissance.apache.spark.MovieLens.run(MovieLens.scala:348) [2026-03-01T06:36:35.780Z] at org.renaissance.harness.ExecutionDriver.executeOperation(ExecutionDriver.java:137) [2026-03-01T06:36:35.780Z] at org.renaissance.harness.ExecutionDriver.executeBenchmark(ExecutionDriver.java:93) [2026-03-01T06:36:35.780Z] at org.renaissance.harness.RenaissanceSuite$.runBenchmarks$$anonfun$1(RenaissanceSuite.scala:172) [2026-03-01T06:36:35.780Z] at scala.runtime.function.JProcedure1.apply(JProcedure1.java:15) [2026-03-01T06:36:35.780Z] at scala.runtime.function.JProcedure1.apply(JProcedure1.java:10) [2026-03-01T06:36:35.780Z] at scala.collection.immutable.List.foreach(List.scala:334) [2026-03-01T06:36:35.780Z] at org.renaissance.harness.RenaissanceSuite$.runBenchmarks(RenaissanceSuite.scala:161) [2026-03-01T06:36:35.780Z] at org.renaissance.harness.RenaissanceSuite$.main(RenaissanceSuite.scala:130) [2026-03-01T06:36:35.780Z] at org.renaissance.harness.RenaissanceSuite.main(RenaissanceSuite.scala) [2026-03-01T06:36:35.780Z] at java.base/jdk.internal.reflect.DirectMethodHandleAccessor.invoke(DirectMethodHandleAccessor.java:104) [2026-03-01T06:36:35.780Z] at java.base/java.lang.reflect.Method.invoke(Method.java:565) [2026-03-01T06:36:35.780Z] at org.renaissance.core.Launcher.loadAndInvokeHarnessClass(Launcher.java:129) [2026-03-01T06:36:35.780Z] at org.renaissance.core.Launcher.launchHarnessClass(Launcher.java:78) [2026-03-01T06:36:35.780Z] at org.renaissance.core.Launcher.main(Launcher.java:43) [2026-03-01T06:36:35.780Z] Caused by: java.lang.ArrayStoreException [2026-03-01T06:36:35.780Z] The following benchmarks failed: movie-lens [2026-03-01T06:36:36.506Z] ----------------------------------- [2026-03-01T06:36:36.506Z] renaissance-movie-lens_0_FAILED [2026-03-01T06:36:36.506Z] ----------------------------------- [2026-03-01T06:36:36.506Z] [2026-03-01T06:36:36.506Z] TEST TEARDOWN: [2026-03-01T06:36:36.506Z] Nothing to be done for teardown. [2026-03-01T06:36:36.506Z] renaissance-movie-lens_0 Finish Time: Sun Mar 1 06:36:36 2026 Epoch Time (ms): 1772346996432