Over the past years, parallel Java applications received a substantial boost in the research field of High Performance Computing, especially in the field of Big Data Analytics by the development of Java-based frameworks, i. e., Apache Hadoop, Flink or Spark, amongst others, for processing large-scale datasets. Analyzing the performance of said Big Data frameworks in particular, and multi-threaded Java applications in general, is indispensable for efficient execution. Due to the high number of threads, this requires a scalable runtime performance measurement infrastructure. The established, open-source tracing framework Score-P provides such an infrastructure, but did not support (parallel) Java applications, previously. We added support for tracing multi-threaded Java applications to Score-P by implementing two instrumentation approaches. The first instrumentation approach is based on the Java Virtual Machine tool interface (JVMTI) and allows to easily trace an application without source code modifications. The second instrumentation approach allows to manually modify sources via API functions such that only those parts of an application are recorded which the user is interested in. Both instrumentation approaches were successfully applied to the LU kernel of the established Java benchmark suite SPECjvm2008 at a modern HPC machine. We show the quality of the implementations by determining the tracing overheads of the instrumented versions for different test scenarios using varying numbers of Java threads, and thus, varying numbers of recorded events.