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When profiling .NET applications, it's essential to understand the concepts of CPU time and wall time to make informed decisions about application performance optimization. These two metrics represent different aspects of the time taken by your .NET application during execution, and each can provide valuable insights depending on your specific use case. CPU time CPU time, also known as process time, refers to the amount of time the CPU spends actively executing instructions for a specific process or thread. This metric only considers the time during which a CPU is working on behalf of the process, excluding any time spent waiting for external resources, input/output operations, or other processes. Usage: CPU time is particularly useful for identifying computationally intensive parts of your .NET application. By focusing on the sections of your code that consume the most CPU time, you can pinpoint areas where optimization may lead to improved overall performance. Wall time Wall time, also known as real time or elapsed time, is the total time taken from the start to the completion of a process or operation, as measured by a regular clock. Wall time includes all time intervals, including waiting for resources, input/output operations, or other processes, as well as active execution time. Usage: Wall time is valuable for understanding the actual user-perceived execution time of your .NET application or specific parts of it. Profiling using wall time can help identify bottlenecks due to resource contention, slow I/O, or other non-computation-related delays that impact the overall responsiveness of your application. Differences between CPU time and wall time The main difference between CPU time and wall time lies in what they measure. CPU time focuses on the active computation time of the CPU, excluding any waiting periods. In contrast, wall time considers the entire duration of a process or operation, including both active execution and waiting times. As a result, CPU time can be shorter than wall time, especially in multi-threaded or multi-process applications where processes might be waiting for resources, I/O, or other processes to complete. When to use CPU time vs. wall time 1. Use CPU time when: Profiling computationally intensive parts of your .NET application. Identifying functions or methods that consume excessive CPU cycles. Diagnosing performance issues related to algorithmic complexity, inefficient data structures, or other code-level optimizations. 2. Use wall time when: Profiling the overall user-perceived execution time of your .NET application. Identifying bottlenecks due to resource contention, slow I/O operations, or other external factors. Understanding the impact of waiting times on your application's responsiveness. CPU and wall times in YourKit .NET Profiler YourKit .NET Profiler has reasonable default settings, as well as a flexible system for configuring wall time and CPU time for all profiling modes: 1. CPU sampling measure CPU time by default, but it can be configured to measure wall time. 2. CPU tracing measures wall time by default, and can be configured to measure CPU time instead. You can choose between CPU and wall time measurement in CPU sampling settings and

The call flow including parameters and timings. It then outputs a visual graph showing the exact path traversal and runtime.But dynamic call graphs can provide much more detailed metrics:Call Counts: Number of invocations of each functionRecursive Data: Identify excessive recursion trees Concurrency: Distinguish parallel threads Performance: Capture runtime, memory allocation Exceptions: See traces leading to errorsFiltering: Narrow graph by package, class, etcpycallgraph offers extensive customization through its plugin architecture:from pycallgraph.output import GraphvizOutputfrom pycallgraph import Configfrom pycallgraph import GlobbingFilterconfig = Config(max_depth=5)config.trace_filter = GlobbingFilter(include=[‘mypackage.*‘])config.output = GraphvizOutput(output_file=‘filtered_graph.png‘) with PyCallGraph(config=config): main()The filtered dynamic graph provides focused profiling on areas of interest without overwhelming detail.For larger applications, sampling may be required to limit performance overhead. Advanced tools like pyff have specialized streaming modes and statistical call counts for low overhead.In summary, dynamic call graphs generated at runtime provide invaluable real-world optimization insights that static analysis alone cannot match.Call Graph ToolsBeyond the basic techniques shown above, developers can choose from many more advanced call graph utilities:Java Tools VisualVM – Open source GUI with customizable metricsJava Flight Recorder – Robust bundled with JDK profiler YourKit – Leading 3rd party Java profilerPython ToolsSnakeViz – Browser based Python visualizer Pyan – Static analysis specializing in call graphsprofiling – Built-in Python profiling module C/C++ Tools Callgrind – Precise dynamic call graphs via Valgrind gprof – GNU profiler analyzes call times Intel VTune – Powerful hotspot and concurrency analysisThese tools provide additional capabilities when basic graphs are not sufficient:Filter graphs by package, class, timeframe Analyze performance metrics like memory, runtimeProfile multi-threaded and remote applications Export graphs to various image, XML, and custom formats Learning to leverage call graphs takes programming skill to the next level. All top developers use them to analyze and enhance code. Understanding available options helps choose the right techniques.Call Graph ConventionsOver decades of evolution, best practices have

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YourKit Java Profiler collects stack samples of running Java threads at regular intervals. It allows you to analyze the frequency of function calls and their relationships, and to get an understanding of the performance of your Java program. We call it "statistical profiling". Keep in mind that statistical profiling is an estimation, and the results may not be precise. However, it can give you valuable insights into the performance characteristics of your Java application. Also, remember that the quality of the estimation depends on the number of selected samples. The wider the selection, the more samples it contains, the better the estimate. How to invoke To estimate CPU usage, please select arbitrary time range in any telemetry chart. Profiler will show merged call tree for all the stacks (CPU samples) recorded within the selected time interval, and will display number of the samples: Comparison with CPU sampling This feature is similar to CPU sampling, as it also uses sampling approach, but there are sufficient differences: Criteria CPU usage estimation CPU sampling Comments Results availability As long as stack telemetry is turned on CPU sampling should be explicitly started CPU usage estimation is ideal for analysis of anomalies such as CPU spikes, especially those which has already happened, so you do not need to turn CPU sampling or tracing on, and try to reproduce the spike. Accuracy Lower Higher CPU usage estimation is based on stack telemetry, whose frequency is normally as low as 1 sample per second, but can be changed using the startup option telemetry_period. CPU usage estimation can adequately measure events not shorter than the stack telemetry period. So, it suits for measuring events or method calls that last at least several seconds. If the measured event or method call is long enough, the estimation will do its job - locate problematic code responsible for the CPU spike. For measuring shorter events or methods, use normal CPU profiling, or decrease the telemetry period using the startup option telemetry_period. Granularity Results are available for the selected time interval Results are aggregated for the entire period of CPU profiling CPU usage estimation enables analysis of particular events or time ranges within single snapshot. CPU profiling results are aggregated since CPU profiling has been started. It is not possible to "extract" CPU profiling results for a smaller time range within one snapshot. However, you can choose which method calls to analyze.

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