![]() ![]() Supports multi-threaded and multi-process applications - such as those with MPI or OpenMP parallelism and scales to very high node counts.Ĭ, C++,Objective C. Shows I/O, communication, floating point operation usage and memory access costs. Performance profiler and memory/resource debugging toolset.Ĭ, C++, Fortran/Fortran90 and Python applications. NET 1.0 to 4.0 applications (including ASP.NET applications), Silverlight 4.0 applications, Windows 32- and 64-bit applications including C, C++, Delphi for Win32 and VBScript and JScript functions NET, Java, PHP, HTML5, ObjectiveC/iOS, Java/Android, C/C++, Apache, Nginx, Cassandra, DataBases Linux, Windows, iOS, Android, Azure, AWS, AIX The following tools work for multiple languages or binaries. Supports collecting hardware counters when built with PAPI support.Supports sampling procfs on supporting systems to report metrics such as page-based resident set size, virtual memory size, read-bytes, and write-bytes, etc.Supports reporting peak resident set size, major and minor page faults, priority and voluntary context switches via getrusage.timem (Unix) - can be used to determine the wall-clock time, CPU time, and CPU utilization similar to time (Unix) but supports numerous extensions.time (Unix) - can be used to determine the run time of a program, separately counting user time vs.The following tools work based on log files that can be generated from various systems. This is a list of performance analysis tools for use in software development. JSTOR ( November 2011) ( Learn how and when to remove this template message).Unsourced material may be challenged and removed.įind sources: "List of performance analysis tools" – news Please help improve this article by adding citations to reliable sources. For the sake of this example, we generate the Model objects that contain the Attributes ourselves.This article needs additional citations for verification. In our example, we want to load all Attributes of different Models of some kind. To get an idea of how a real memory leak looks like, we create an example on the basis of the Baeldung memory leak article. ![]() ![]() So, if we did not wait long enough for the garbage collector to run, we could come to the false conclusion that a memory leak occurred. Only at the end of the startup period we can see a spike in the garbage collector graph and a dramatic decrease in used memory. ![]() But if we look at the GC Activity graph during the startup period the memory increases, we observe that the garbage collector did not run (activity at 0%). Depending on the options we select, when starting the profiling session, it shows us the memory, garbage collector activity, recorded throughput, CPU load and many more.įor example, this is the memory graph for the startup of a service.įor someone who has never dealt with a memory leak, looking at this graph, he might think that a memory leak occurred as the memory usage is increasing continuously. JProfiler displays several graphs after it has started up. We will take a look at a memory leak and investigate how it would be possible to detect using the tools that JProfiler offers. There are many blog posts and articles about different kind of memory leaks and how their memory footprint looks like, unfortunately, it is very hard to find the memory leak based only on the memory graph. JProfiler brings many more features, like database profiling but these are out of the scope of this blog post. It provides many useful tools such as memory profiling, a way to analyze heap snapshots and a live memory view that shows all objects that are currently in use. JProfiler is a java profiler developed by ej-technologies that helps developers resolve performance bottlenecks, find memory leaks and understand threading issues. ![]()
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