Windows 8.1 came with some changes to the underlying system that the VS profiler uses when collecting information. If you update to Visual Studio 2012 Update 4 (or newer) and profile again, symbols should be resolved correctly.

The profiler modules are designed to provide an execution profile for a givenprogram, not for benchmarking purposes (for that, there is timeit forreasonably accurate results). This particularly applies to benchmarkingPython code against C code: the profilers introduce overhead for Python code,but not for C-level functions, and so the C code would seem faster than anyPython one.

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The file selected by the above constructor must have been created by thecorresponding version of profile or cProfile. To be specific,there is no file compatibility guaranteed with future versions of thisprofiler, and there is no compatibility with files produced by otherprofilers, or the same profiler run on a different operating system. Ifseveral files are provided, all the statistics for identical functions willbe coalesced, so that an overall view of several processes can be consideredin a single report. If additional files need to be combined with data in anexisting Stats object, the add() methodcan be used.

This method for the Stats class prints a list of all functionsthat called each function in the profiled database. The ordering isidentical to that provided by print_stats(), and thedefinition of the restricting argument is also identical. Each caller isreported on its own line. The format differs slightly depending on theprofiler that produced the stats:

The problem is more important with profile than with the lower-overheadcProfile. For this reason, profile provides a means ofcalibrating itself for a given platform so that this error can beprobabilistically (on the average) removed. After the profiler is calibrated, itwill be more accurate (in a least square sense), but it will sometimes producenegative numbers (when call counts are exceptionally low, and the gods ofprobability work against you :-). ) Do not be alarmed by negative numbers inthe profile. They should only appear if you have calibrated your profiler,and the results are actually better than without calibration.

The profiler of the profile module subtracts a constant from each eventhandling time to compensate for the overhead of calling the time function, andsocking away the results. By default, the constant is 0. The followingprocedure can be used to obtain a better constant for a given platform (seeLimitations).

Be warned that you should calibrate the profiler class for the timer functionthat you choose (see Calibration). For most machines, a timerthat returns a lone integer value will provide the best results in terms oflow overhead during profiling. (os.times() is pretty bad, as itreturns a tuple of floating point values). If you want to substitute abetter timer in the cleanest fashion, derive a class and hardwire areplacement dispatch method that best handles your timer call, along with theappropriate calibration constant.

If you don't want to use the default credentials to run the profiler, you can provide custom credentials by using following code. For more information about custom credentials, see Supplying and Retrieving AWS Credentials.

Profile implements what is known as a "sampling" or statistical profiler. It works by periodically taking a backtrace during the execution of any task. Each backtrace captures the currently-running function and line number, plus the complete chain of function calls that led to this line, and hence is a "snapshot" of the current state of execution.

A sampling profiler does not provide complete line-by-line coverage, because the backtraces occur at intervals (by default, 1 ms on Unix systems and 10 ms on Windows, although the actual scheduling is subject to operating system load). Moreover, as discussed further below, because samples are collected at a sparse subset of all execution points, the data collected by a sampling profiler is subject to statistical noise.

The allocation profiler does have significant overhead, so a sample_rate argument can be passed to speed it up by making it skip some allocations. Passing sample_rate=1.0 will make it record everything (which is slow); sample_rate=0.1 will record only 10% of the allocations (faster), etc.

The current implementation of the Allocations Profiler does not capture types for all allocations. Allocations for which the profiler could not capture the type are represented as having type Profile.Allocs.UnknownType.

You can enable or disable the Profiler using the Project > Profile Code menu. A check mark appears next to the menu when profiling is enabled. After enabling Profile Code, you run and test your apps as you normally would. The Code Profiler silently gathers information about the methods that are called and how long each one takes to execute. Be aware that your application runs slower than usual when profiling is enabled due to the overhead of tracking and timing everything to get the profiler data.

As you are optimizing your app, you can compare the current Profiler Data with previous Profiler Data to see if your changes are improving performance.Profiler data is not saved along with your project. Use the Save As function described earlier if you wish to retain the profiler data.

Profiler data is only collected if your app quits normally. If it quits because of a crash (or you press the Stop button in the debugger), no profiler data is collected. For web apps you can close all the browser windows/tabs and wait for the IDE to detect that the app has terminated using code like this:

Looks like the profiler is not installed by default because it is experimental on MacOS. Even the instructions on the web page seem to be out of date. It explains that I need to enable java.profile.enabled in the Experimental Features dialog.

I downloaded the Ultimate edition of IntelliJ and tried again to follow the instructions for the profiler. But there seems to still be a problem. in the mean time I filed an issue at the JetBrains issue tracker. The issue was subsequently triaged without any commentary. Anyone have any idea what that means?

The profiler displays information about the resources (CPU time, memory)consumed by specific algorithms and other parts of a computation. This helps todiagnose why a script is running slowly or failing due to memory limits. To usethe profiler, click the Run with profiler option in the dropdown on the Runbutton. As a shortcut, hold down Alt (or Option on Mac) and click Run, or pressCtrl+Alt+Enter. This activates a Profiler tab on the right side of the codeeditor. As the script runs, the Profiler tab will display a table ofresource usage from the script. Clicking the Run button (without profiling)will make the Profiler tab disappear and disable the profiler.

Researchers at the University of Massachusetts Amherst aim to change that with Scalene, a Python profiler that not only identifies inefficiencies in code but also recommends how that code can be improved, with the help of AI.

AQTime Pro lets you find performance bottlenecks in 32-bit and 64-bit C/C++, Delphi, .NET, Java, and Silverlight applications and scripts. You can use AQTime Pro as a standalone performance profiler, or integrated into Microsoft Visual Studio and Embarcadero RAD Studio IDEs for improved productivity.

The parallel profiler provides an extension of the profile command and the profile viewer specifically for workers in a parallel pool, to enable you to see how much time each worker spends evaluating each function and how much time communicating or waiting for communications with the other workers. For more information about the standard profiler and its views, see Profile Your Code to Improve Performance.

The next figure shows a summary report for the workers that spend the most versus least time for each function. A Manual Comparison Selection of max Time Aggregate against min Time >0 Aggregate generated this summary. Both aggregate settings indicate that the profiler should consider data from all workers for all functions, for both maximum and minimum. This report lists the data for codistributor1d.hMtimesImpl from workers 50 and 62, because they spent the maximum and minimum times on this function. Similarly, other functions are listed.

Please note that Nsight Compute is not a kernel debugger, but a kernel profiler. It should not be used with kernels that have correctness or stability issues, but only for performance optimization. For a kernel debugger, please use cuda-gdb, compute-sanitizer or Nsight Visual Studio Edition.

Typhon quickly identifies performance-limiting functions and excessive memory use in a software application without requiring time-consuming recompilation or source-level access. With the Typhon profiler, a user can focus data collection of particular areas of the code by selecting files, classes, and/or methods to target during profiling. This unique method of targeted profiling reduces the amount of data gathered and displayed and further reduces profiling overhead, significantly saving time and dramatically increasing reliability. The user is presented with the timing data to support decision making without being inundated with unneeded data.

Typhon enables developers to identify performance and memory problems in software prior to deployment of the software, when it is easier and requires less resources to fix any problems that are found. Using Typhon, developers can adjust their profiling configuration to investigate the performance and memory of their application broadly or in a targeted fashion. Typhon helps developers quickly identify poorly-scaling or otherwise data-dependent functions in a large code base. While existing profilers generally only show the user which functions are slow within a given performance run, Typhon provides the option to run the same code over multiple dataset sizes and instantly pinpoint functions that scale poorly based on the different inputs used over several runs. These benefits apply to both DoD and commercial entities that develop software. be457b7860

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