![]() ![]() Use the heap as a performance baseline for further optimizations. In some cases, to increase throughput, you may want to disable this option and set the heap size manually. To achieve pause, throughput, and footprint goals, the GC can resize heap generations based on GC statistics. Heap Tuning - By default, JVMs adapt their heap based on runtime heuristics. ![]() If throughput is your goal, specify -XX:+UseParallelOldGC. Garbage collection can then work more efficiently because you have reduced the overall full GC pause. Garbage Collection (GC) Algorithm - When you enable the UseParallelOldGC option, you can collect old and young generation collections in parallel.Make sure to tune these parameters for optimal performance of your Java application:.In accordance with popular optimization practices and guidance, start optimizing the application with -XX:+UseParallelOldGC and -XX:-UseAdaptiveSizePolicy JVM options.Change these properties in the config/specjbb2015.props file: _customers=1 For the purpose of this recipe, let us first shorten the runtime of SPECjbb2015.When you visualize the application stacks in a flame graph, you may find it easier to identify hot paths for the application and its mixed stacks (Java and built-in). The slowdown is not always obvious to analyze or explain. Typically, a poor selection of parameters (either sub-optimal or incorrect) for the Java Virtual Machine (JVM) can result in slow application performance. This recipe uses a Java application as an example. You can use flame graphs when you run the hotspots analysis with stacks on any of these workloads: Therefore, the hottest functions in your application occupy the widest portions on the flame graph. The width of a function displayed in the graph is an indication of the amount of time it engaged the CPU. Functions are stacked in order of ancestry, with parent functions directly below child functions. The graph plots all of the functions in your application on the X-axis and displays the stack depth on the Y-axis. NET* Core Application Profiling Applications in Amazon Web Services* (AWS) EC2 Instances Enabling Performance Profiling in GitLab* CI Configuring a Hyper-V* Virtual Machine for Hardware-Based Hotspots Analysis Profiling an Application for Performance Anomalies (NEW) Profiling an OpenMP* Offload Application running on a GPU (NEW) Profiling a SYCL* Application running on a GPU Profiling an FPGA-driven SYCL* Application Profiling Hardware Without Intel Sampling Drivers Profiling MPI Applications Profiling Docker* Containers Profiling a Remote Target Through a Proxy Server (NEW) Profiling in a Singularity* Container Profiling Linux*, Android*, and QNX* System Boot Time Using Intel® VTune™ Profiler Server with Visual Studio Code and Intel® DevCloud for oneAPI (NEW) Using Intel® VTune™ Profiler Server in HPC Clusters Using the Command-Line Interface to Analyze the Performance of a SYCL* Application running on a GPU (NEW)Ī flame graph is a visual representation of the stacks and stack frames in your application. Profiling Machine Learning Applications (NEW) Profiling Single-Node Kubernetes* Applications (NEW) Analyzing Hot Code Paths Using Flame Graphs (NEW) Ingredients Create a Baseline Run Hotspots Analysis Analyze Hotspots Information Identify Hot Code Paths in the Flame Graph Change JVM Options See Also Improving Hotspot Observability in a C++ Application Using Flame Graphs Measuring Performance Impact of NUMA in Multi-Processor Systems Profiling Games built with Unity* (NEW) Profiling Games built with Unreal Engine* (NEW) Profiling Java Applications as a Remote User (NEW) Profiling JavaScript* Code in Node.js* Analyzing CPU and FPGA (Intel® Arria® 10 GX) Interaction Profiling a.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |