That is the reason why code profiling is one of the most important aspects of software development, as it lets you identify bottlenecks, dead code, and even bugs. For example, run the following command to download and install gprof on Debian-based systems: sudo apt-get install binutils Requirements Before you use gprof to generate profiling data, make sure that your program executable contains extra information required by the profiler to function properly. This can be achieved by adding the -pg command line option while compiling your code, assuming that you are using the gcc compiler. If you are using separate commands for compiling and linking, add the command line option to both the commands. To generate a human readable file, run the following command: gprof test gmon.
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Run your program again, the same as before. Analyze the cumulative data using this command: gprof executable-file gmon. There is no direct information about these measurements in the profile data itself.
Instead, gprof estimates them by making an assumption about your program that might or might not be true. The assumption made is that the average time spent in each call to any function foo is not correlated with who called foo. This assumption is usually true enough, but for some programs it is far from true.
Suppose that foo returns very quickly when its argument is zero; suppose that a always passes zero as an argument, while other callers of foo pass other arguments.
In this program, all the time spent in foo is in the calls from callers other than a. But gprof has no way of knowing this; it will blindly and incorrectly charge 2 seconds of time in foo to the children of a. For the nonce, the estimated figures are usually more useful than misleading. How do I find which lines in my program were executed the most times? Compile your program with basic-block counting enabled, run it, then use the following pipeline: gprof -l -C objfile sort -k 3 -n -r This listing will show you the lines in your code executed most often, but not necessarily those that consumed the most time.
How do I find which lines in my program called a particular function? Use gprof -l and lookup the function in the call graph. The callers will be broken down by function and line number. How do I analyze a program that runs for less than a second? But there are a few differences. GNU gprof uses a new, generalized file format with support for basic-block execution counts and non-realtime histograms. A magic cookie and version number allows gprof to easily identify new style files. Old BSD-style files can still be read.
See section Profiling Data File Format. For a recursive function, Unix gprof lists the function as a parent and as a child, with a calls field that lists the number of recursive calls. GNU gprof omits these lines and puts the number of recursive calls in the primary line. In the annotated source listing, if there are multiple basic blocks on the same line, GNU gprof prints all of their counts, seperated by commas. The blurbs, field widths, and output formats are different.
GNU gprof prints blurbs after the tables, so that you can see the tables without skipping the blurbs. Implementation of Profiling Profiling works by changing how every function in your program is compiled so that when it is called, it will stash away some information about where it was called from.
From this, the profiler can figure out what function called it, and can count how many times it was called. This is typically done by examining the stack frame to find both the address of the child, and the return address in the original parent.
However, on some architectures, most notably the SPARC, using this builtin can be very computationally expensive, and an assembly language version of mcount is used for performance reasons. Number-of-calls information for library routines is collected by using a special version of the C library.
Profiling also involves watching your program as it runs, and keeping a histogram of where the program counter happens to be every now and then. Typically the program counter is looked at around times per second of run time, but the exact frequency may vary from system to system. This is done is one of two ways. Typical scaling values cause every 2 to 8 bytes of address space to map into a single array slot.
On every tick of the system clock assuming the profiled program is running , the value of the program counter is examined and the corresponding slot in the memory array is incremented. Since this is done in the kernel, which had to interrupt the process anyway to handle the clock interrupt, very little additional system overhead is required.
However, some operating systems, most notably Linux 2. On such a system, arrangements are made for the kernel to periodically deliver a signal to the process typically via setitimer , which then performs the same operation of examining the program counter and incrementing a slot in the memory array.
Since this method requires a signal to be delivered to user space every time a sample is taken, it uses considerably more overhead than kernel-based profiling. Also, due to the added delay required to deliver the signal, this method is less accurate as well. A special startup routine allocates memory for the histogram and either calls profil or sets up a clock signal handler.
This routine monstartup can be invoked in several ways. On Linux systems, a special profiling startup file gcrt0. Rather, the mcount routine, when it is invoked for the first time typically when main is called , calls monstartup. Each object file is then compiled with a static array of counts, initially zero.
In the executable code, every time a new basic-block begins i. At compile time, a paired array was constructed that recorded the starting address of each basic-block.
Taken together, the two arrays record the starting address of every basic-block, along with the number of times it was executed. Profiling is turned off, various headers are output, and the histogram is written, followed by the call-graph arcs and the basic-block counts. Therefore, the time measurements in gprof output say nothing about time that your program was not running.
For example, a part of the program that creates so much data that it cannot all fit in physical memory at once may run very slowly due to thrashing, but gprof will say it uses little time. Profiling Data File Format The old BSD-derived file format used for profile data does not contain a magic cookie that allows to check whether a data file really is a gprof file. Furthermore, it does not provide a version number, thus rendering changes to the file format almost impossible.
GNU gprof uses a new file format that provides these features. For backward compatibility, GNU gprof continues to support the old BSD-derived format, but not all features are supported with it. For example, basic-block execution counts cannot be accommodated by the old file format. It consists of a header containing the magic cookie and a version number, as well as some spare bytes available for future extensions.
All data in a profile data file is in the native format of the host on which the profile was collected. GNU gprof adapts automatically to the byte-order in use. In the new file format, the header is followed by a sequence of records. Currently, there are three different record types: histogram records, call-graph arc records, and basic-block execution count records. Each file can contain any number of each record type. When reading a file, GNU gprof will ensure records of the same type are compatible with each other and compute the union of all records.
For example, for basic-block execution counts, the union is simply the sum of all execution counts for each basic-block. Histogram Records Histogram records consist of a header that is followed by an array of bins. The header contains the text-segment range that the histogram spans, the size of the histogram in bytes unlike in the old BSD format, this does not include the size of the header , the rate of the profiling clock, and the physical dimension that the bin counts represent after being scaled by the profiling clock rate.
The physical dimension is specified in two parts: a long name of up to 15 characters and a single character abbreviation.
For example, a histogram representing real-time would specify the long name as "seconds" and the abbreviation as "s". This feature is useful for architectures that support performance monitor hardware which, fortunately, is becoming increasingly common.
In this case, the dimension in the histogram header could be set to "i-cache misses" and the abbreviation could be set to "1" because it is simply a count, not a physical dimension. Also, the profiling rate would have to be set to 1 in this case. Histogram bins are bit numbers and each bin represent an equal amount of text-space. For example, if the text-segment is one thousand bytes long and if there are ten bins in the histogram, each bin represents one hundred bytes.
It consists of an arc in the call graph and a count indicating the number of times the arc was traversed during program execution. When performing profiling at the function level, these addresses can point anywhere within the respective function. This will ensure that the line-level call-graph is able to identify exactly which line of source code performed calls to a function. The header simply specifies the length of the sequence. Any address within the basic-address can be used.
Next, the BFD library is called to open the object file, verify that it is an object file, and read its symbol table core. For normal profiling, the BFD canonical symbol table is scanned. For line-by-line profiling, every text space address is examined, and a new symbol table entry gets created every time the line number changes. In either case, two passes are made through the symbol table - one to count the size of the symbol table required, and the other to actually read the symbols.
In between the two passes, a single array of type Sym is created of the appropiate length. Finally, symtab. The symbol table must be a contiguous array for two reasons. First, the qsort library function which sorts an array will be used to sort the symbol table. Also, the symbol lookup routine symtab. Line number symbols have no special flags set.
Remember that a single symspec can match multiple symbols. An array of symbol tables syms is created, each entry of which is a symbol table of Syms to be included or excluded from a particular listing.
The master symbol table and the symspecs are examined by nested loops, and every symbol that matches a symspec is inserted into the appropriate syms table. This is done twice, once to count the size of each required symbol table, and again to build the tables, which have been malloced between passes.
From now on, to determine whether a symbol is on an include or exclude symspec list, gprof simply uses its standard symbol lookup routine on the appropriate table in the syms array. New-style histogram records are read by hist. For the first histogram record, allocate a memory array to hold all the bins, and read them in. When multiple profile data files or files with multiple histogram records are read, the starting address, ending address, number of bins and sampling rate must match between the various histograms, or a fatal error will result.
If everything matches, just sum the additional histograms into the existing in-memory array. Again, if multiple basic-block records are present for the same address, the call counts are cumulative.
GPROF Tutorial – How to use Linux GNU GCC Profiling Tool
Run your program again, the same as before. Analyze the cumulative data using this command: gprof executable-file gmon. There is no direct information about these measurements in the profile data itself. Instead, gprof estimates them by making an assumption about your program that might or might not be true. The assumption made is that the average time spent in each call to any function foo is not correlated with who called foo. This assumption is usually true enough, but for some programs it is far from true. Suppose that foo returns very quickly when its argument is zero; suppose that a always passes zero as an argument, while other callers of foo pass other arguments.
Tutorial: Using GNU Profiling (gprof) with ARM Cortex-M
Additionally I explain the inner workings to generate the data necessary for gprof. This post is about Application Profiling with gprof. As it is a longer article with many details, it took me a while to get it written down. I show how to use it in command line mode or using gprof in Eclipse e. What is profiling? It is about to know where the application spends most of its time so I can optimize it.
How to Profile a C program in Linux using GNU gprof
GPROF output consists of two parts: the flat profile and the call graph. The flat profile gives the total execution time spent in each function and its percentage of the total running time. Function call counts are also reported. Output is sorted by percentage, with hot spots at the top of the list.