Having done this, I put the 'wildcard' suppressions into a different file and used it to run 'minimal' and regenerate the suppression file. Then looked for more near duplicates After several such iterations, I just appended the remaining suppressions, and ended up with a file that suppressed robustly all the 'minimal' false positives.
I've posted the resulting file here. The current one was created using wxGTK If you don't want to write your own, try it. I don't know how well it'll work for you, but it should at least provide a start.
Update: It's now and many more people are using wx2. It's designed to be used in addition to the 2. The next step is to repeat the process while using the 'minimal' suppression file with the sample s most relevant to your app.
This results in a second lot of suppressions which can either be added to the first file or used as a separate file, perhaps pointed to in. Now when you valgrind your app, the false positives should be reduced to reasonable levels. There will probably still be some though, as your app will make calls that the sample didn't.
If you want to get rid of them, make yet another suppression file. Aggressive use of wildcards in suppressions obviously risks filtering out true positives too. This is described in [1] and [2].
You can replace this with an informative name if you wish, but it's not necessary. The second line will start with Memcheck:. Immediately following will be the type of error. Unfortunately most false positives seem to produce most or all of these error types, and there's no way of combining the types in a single suppression; so each error needs 3 or 4 otherwise-identical suppressions.
The remaining lines start either with fun: for function calls, or obj: for the names of shared objects. However I found the frame-level wildcard ' Dangling pointers are those which point to a memory that is already freed. Valgrind's Memcheck can easily detect these types of issues. So you can see that the code above tries to access the memory pointed by 'ptr' even after it is freed.
Here is how Memcheck detects this bug :. So you can see that the tool detected the write operation on the dangling pointer and labeled it as invalid.
Sometimes, code contains a bug where-in a pointer tries to access out of bound heap memory location. Memcheck can easily detect these kinds of memory related bugs. In the code above, you can see that the pointer 'ptr' is trying to access a memory location that is out of its bounds.
Here is how memcheck detects this problem :. As already mentioned, you can use Kcachegrind. This is a fairly standard GUI application for browsing the performance results. Figure 5 is a screen shot showing, on the left, percent of total time per function in descending order. The top 3, as expected, are the awful isPrime , sqrt and main with Most of the navigating can be done by clicking the list of functions in the left pane. The two right panes have similar displays, roughly for displaying information about callers and callees that is, functions called by the current function.
For an example using callgrind, I downloaded and built a debug version of Icarus Verilog [ Icarus ] and compiled and simulated a Fibonacci calculator [ Fibonacci ] with small modifications to make the simulation last longer and exit on completion.
You can navigate through the functions by double clicking in the call graph. In the last snapshot, I show the funky callee map which I expect will look better in the PDF version of the magazine in colour than in the black and white print magazine. The areas of the rectangles is proportional to the time spent in the function.
Both the map and the list are dynamic, and you can click in one and the area or list item will be highlighted in the other. You can also double click to jump to a different function. This is worse for 32bit x86, where by default code gets generated to use the venerable x87 floating point coprocessor.
This uses 10byte 80bit precision internally. Valgrind performs the calculations at 64bit precision, so some of the intermediate precision is lost. Another problem with big real-world programs is speed. Like all of the Valgrind tools, there is a big overhead in performing the measurement. You can mitigate this by controlling when callgrind performs data collection.
You can statically control callgrind using Valgrind macros defined in callgrind. You will probably need to do some profiling of the entire application to get an idea of where you want to concentrate your efforts.
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