Rosales, Eduardo Rosà, Andrea Binder, Walter 2020 An efficient fork/join application should maximize parallelism while minimizing overheads, and maximize locality while minimizing contention. However, there is no unique optimal implementation that best resolves such tradeoffs and failing in balancing them may lead to fork/join applications suffering from several issues (e.g., suboptimal forking, load imbalance, excessive synchronization), possibly compromising the performance gained by a task-parallel execution. Moreover, there is a lack of profilers enabling performance analysis of a fork/join application. As a result, developers are often required to implement their own tools for monitoring and collecting information and metrics on fork/join applications, which could be time-consuming, error-prone, and is often beyond the expertise of the developer. In this paper, we present FJProf, a novel profiler which accurately collects dynamic information and key metrics to facilitate characterizing several performance attributes specific to a fork/join application running on a single Java Virtual Machine (JVM) in a shared-memory multicore. FJProf reports information and graphics to developers that help them understand the details of the fork/join processing exposed by a parallel application running on the JVM. We show how FJProf supports performance analysis by characterizing a fork/join application from the Renaissance benchmark suite. application/pdf https://n2t.net/ark:/12658/srd1319023 https://susi.usi.ch/global/documents/319023 https://susi.usi.ch/documents/319023/files/Rosales_2020.pdf eng info:eu-repo/semantics/altIdentifier/ark/12658/srd1319023 info:eu-repo/semantics/openAccess License undefined Performance analysis Fork/join parallelism Task granularity Java virtual machine info:eu-repo/classification/udc/004 FJProf : profiling fork/join applications on the Java virtual machine http://purl.org/coar/resource_type/c_816b