Analyzing the scalability of managed language applications with speedup stacks

Understanding the reasons why multi-threaded applications do not achieve perfect scaling on modern multicore hardware is challenging. Furthermore, more and more modern programs are written in managed languages, which have extra service threads (e.g., to perform memory management), which may retard scalability and complicate performance analysis. In this paper, we extend speedup stacks, a previously-presented visualization tool to analyze multi-threaded program scalability, to managed applications. Speedup stacks are comprehensive bar graphs that break down an application's execution to explain the main causes of sublinear speedup, i.e., when some threads are not allowing the application to progress, and thus increasing the execution time. We not only expand speedup stacks to analyze how the managed language's service threads affect overall scalability, but also implement speedup stacks while running on native hardware. We monitor the application and service threads' scheduling behavior using light-weight OS kernel modules, incurring under 1% overhead running unmodified Java benchmarks. We add two performance delimiters targeting managed applications: garbage collection and main initialization activities. We analyze the scalability limitations of these benchmarks and the impact of using both a stop-the-world and a concurrent garbage collector with speedup stacks. Our visualization tool facilitates the identification of scalability bottlenecks both between application threads and of service threads, pointing developers to whether optimization should be focused on the language runtime or the application. Speedup stacks provide better program understanding for both program and system designers, which can help optimize multicore processor performance.