Is there a way using C or assembler or maybe even C# to get an accurate measure of how long it takes to execute a ADD instruction?
Yes, sort of, but it's non-trivial and produces results that are almost meaningless, at least on most reasonably modern processors.
On relatively slow processors (e.g., up through the original Pentium in the Intel line, still true on most small embedded processors) you can just look in the processor's data sheet and it'll (normally) tell you how many clock ticks to expect. Quick, simple, and easy.
On a modern desktop machine (e.g., Pentium Pro or newer), life isn't nearly that simple. These CPUs can execute a number of instructions at a time, and execute them out of order as long as there aren't any dependencies between them. This means the whole concept of the time taken by a single instruction becomes almost meaningless. The time taken to execute one instruction can and will depend on the instructions that surround it.
That said, yes, if you really want to, you can (usually -- depending on the processor) measure something, though it's open to considerable question exactly how much it'll really mean. Even getting a result like this that's only close to meaningless instead of completely meaningless isn't trivial though. For example, on an Intel or AMD chip, you can use RDTSC to do the timing measurement itself. That, unfortunately, can be executed out of order as described above. To get meaningful results, you need to surround it by an instruction that can't be executed out of order (a "serializing instruction"). The most common choice for that is CPUID, since it's one of the few serializing instructions that's available to "user mode" (i.e., ring 3) programs. That adds a bit of a twist itself though: as documented by Intel, the first few times the processor executes CPUID, it can take longer than subsequent times. As such, they recommend that you execute it three times before you use it to serialize your timing. Therefore, the general sequence runs something like this:
.align 16
CPUID
CPUID
CPUID
RDTSC
; sequence under test
Add eax, ebx
; end of sequence under test
CPUID
RDTSC
Then you compare that to a result from doing the same, but with the sequence under test removed. That's leaving out quite a fe details, of course -- at minimum you need to:
set the registers up correctly before each CPUID
save the value in EAX:EDX after the first RDTSC
subtract result from the second RDTSC from the first
Also note the "align" directive I've inserted -- instruction alignment can and will affect timing as well, especially if a loop is involved.
Construct a loop that executes 10 million times, with nothing in the loop body, and time that. Keep that time as the overhead required for looping.
Then execute the same loop again, this time with the code under test in the body. Time for this loop, minus the overhead (from the empty loop case) is the time due to the 10 million repetitions of your code under test. So, divide by the number of iterations.
Obviously this method needs tuning with regard to the number of iterations. If what you're measuring is small, like a single instruction, you might even want to run upwards of a billion iterations. If its a significant chunk of code, a few 10's of thousands might suffice.
In the case of a single assembly instruction, the assembler is probably the right tool for the job, or perhaps C, if you are conversant with inline assembly. Others have posted more elegant solutions for how to get a measurement w/o the repetition, but the repetition technique is always available, for example, an embedded processor that doesn't have the nice timing instructions mentioned by others.
Note however, that on modern pipeline processors, instruction level parallelism may confound your results. Because more than one instruction is running through the execution pipeline at a time, it is no longer true that N repetitions of an given instruction take N times as long as a single one.
Okay, the problem that you are going to encounter if you are using an OS like Windows, Linux, Unix, MacOS, AmigaOS and all those others that there are lots of processes already running on your machine in the background which will impact performance. The only real way of calculating actual time of an instruction is to disassemble your motherboard and test each component using external hardware. It depends whether you absolutely want to do this yourself, or simply find out how fast a typical revision of your processor actually runs. Companies such as Intel and Motorola test their chips extensively before release, and these results are available to the public. All you need to do is ask them and they'll send you a free CD-ROM (it might be a DVD - nonsense pedantry) with the results contained. You can do it yourself, but be warned that especially Intel processors contain many redundant instructions that are no longer desirable, let alone necessary. This will take up a lot of your time, but I can absolutely see the fun in doing this. PS. If its purely to help push your own machine's hardware to its theoretical maximum in a personal project that you're doing the Just Jeff's answer above is excellent for generating tidy instruction-speed-averages under real-world conditions.
No, but you can calculate it based upon the number of clock cycles the add instruction requires multiplied by the clock rate of the CPU. Different types of arguments to an ADD may result in more or fewer cycles but, for a given argument list, the instruction always takes the same number of cycles to complete.
That said, why do you care?
Related
I am evaluating a network+rendering workload for my project.
The program continuously runs a main loop:
while (true) {
doSomething()
drawSomething()
doSomething2()
sendSomething()
}
The main loop runs more than 60 times per second.
I want to see the performance breakdown, how much time each procedure takes.
My concern is that if I print the time interval for every entrance and exit of each procedure,
It would incur huge performance overhead.
I am curious what is an idiomatic way of measuring the performance.
Printing of logging is good enough?
Generally: For repeated short things, you can just time the whole repeat loop. (But microbenchmarking is hard; easy to distort results unless you understand the implications of doing that; for very short things, throughput and latency are different, so measure both separately by making one iteration use the result of the previous or not. Also beware that branch prediction and caching can make something look fast in a microbenchmark when it would actually be costly if done one at a time between other work in a larger program.
e.g. loop unrolling and lookup tables often look good because there's no pressure on I-cache or D-cache from anything else.)
Or if you insist on timing each separate iteration, record the results in an array and print later; you don't want to invoke heavy-weight printing code inside your loop.
This question is way too broad to say anything more specific.
Many languages have benchmarking packages that will help you write microbenchmarks of a single function. Use them. e.g. for Java, JMH makes sure the function under test is warmed up and fully optimized by the JIT, and all that jazz, before doing timed runs. And runs it for a specified interval, counting how many iterations it completes. See How do I write a correct micro-benchmark in Java? for that and more.
Beware common microbenchmark pitfalls
Failure to warm up code / data caches and stuff: page faults within the timed region for touching new memory, or code / data cache misses, that wouldn't be part of normal operation. (Example of noticing this effect: Performance: memset; or example of a wrong conclusion based on this mistake)
Never-written memory (obtained fresh from the kernel) gets all its pages copy-on-write mapped to the same system-wide physical page (4K or 2M) of zeros if you read without writing, at least on Linux. So you can get cache hits but TLB misses. e.g. A large allocation from new / calloc / malloc, or a zero-initialized array in static storage in .bss. Use a non-zero initializer or memset.
Failure to give the CPU time to ramp up to max turbo: modern CPUs clock down to idle speeds to save power, only clocking up after a few milliseconds. (Or longer depending on the OS / HW).
related: on modern x86, RDTSC counts reference cycles, not core clock cycles, so it's subject to the same CPU-frequency variation effects as wall-clock time.
Most integer and FP arithmetic asm instructions (except divide and square root which are already slower than others) have performance (latency and throughput) that doesn't depend on the actual data. Except for subnormal aka denormal floating point being very slow, and in some cases (e.g. legacy x87 but not SSE2) also producing NaN or Inf can be slow.
On modern CPUs with out-of-order execution, some things are too short to truly time meaningfully, see also this. Performance of a tiny block of assembly language (e.g. generated by a compiler for one function) can't be characterized by a single number, even if it doesn't branch or access memory (so no chance of mispredict or cache miss). It has latency from inputs to outputs, but different throughput if run repeatedly with independent inputs is higher. e.g. an add instruction on a Skylake CPU has 4/clock throughput, but 1 cycle latency. So dummy = foo(x) can be 4x faster than x = foo(x); in a loop. Floating-point instructions have higher latency than integer, so it's often a bigger deal. Memory access is also pipelined on most CPUs, so looping over an array (address for next load easy to calculate) is often much faster than walking a linked list (address for next load isn't available until the previous load completes).
Obviously performance can differ between CPUs; in the big picture usually it's rare for version A to be faster on Intel, version B to be faster on AMD, but that can easily happen in the small scale. When reporting / recording benchmark numbers, always note what CPU you tested on.
Related to the above and below points: you can't "benchmark the * operator" in C in general, for example. Some use-cases for it will compile very differently from others, e.g. tmp = foo * i; in a loop can often turn into tmp += foo (strength reduction), or if the multiplier is a constant power of 2 the compiler will just use a shift. The same operator in the source can compile to very different instructions, depending on surrounding code.
You need to compile with optimization enabled, but you also need to stop the compiler from optimizing away the work, or hoisting it out of a loop. Make sure you use the result (e.g. print it or store it to a volatile) so the compiler has to produce it. For an array, volatile double sink = output[argc]; is a useful trick: the compiler doesn't know the value of argc so it has to generate the whole array, but you don't need to read the whole array or even call an RNG function. (Unless the compiler aggressively transforms to only calculate the one output selected by argc, but that tends not to be a problem in practice.)
For inputs, use a random number or argc or something instead of a compile-time constant so your compiler can't do constant-propagation for things that won't be constants in your real use-case. In C you can sometimes use inline asm or volatile for this, e.g. the stuff this question is asking about. A good benchmarking package like Google Benchmark will include functions for this.
If the real use-case for a function lets it inline into callers where some inputs are constant, or the operations can be optimized into other work, it's not very useful to benchmark it on its own.
Big complicated functions with special handling for lots of special cases can look fast in a microbenchmark when you run them repeatedly, especially with the same input every time. In real life use-cases, branch prediction often won't be primed for that function with that input. Also, a massively unrolled loop can look good in a microbenchmark, but in real life it slows everything else down with its big instruction-cache footprint leading to eviction of other code.
Related to that last point: Don't tune only for huge inputs, if the real use-case for a function includes a lot of small inputs. e.g. a memcpy implementation that's great for huge inputs but takes too long to figure out which strategy to use for small inputs might not be good. It's a tradeoff; make sure it's good enough for large inputs (for an appropriate definition of "enough"), but also keep overhead low for small inputs.
Litmus tests:
If you're benchmarking two functions in one program: if reversing the order of testing changes the results, your benchmark isn't fair. e.g. function A might only look slow because you're testing it first, with insufficient warm-up. example: Why is std::vector slower than an array? (it's not, whichever loop runs first has to pay for all the page faults and cache misses; the 2nd just zooms through filling the same memory.)
Increasing the iteration count of a repeat loop should linearly increase the total time, and not affect the calculated time-per-call. If not, then you have non-negligible measurement overhead or your code optimized away (e.g. hoisted out of the loop and runs only once instead of N times).
Vary other test parameters as a sanity check.
For C / C++, see also Simple for() loop benchmark takes the same time with any loop bound where I went into some more detail about microbenchmarking and using volatile or asm to stop important work from optimizing away with gcc/clang.
currently I am working on setting up benchmark between microcontrollers (based on Powerpc). So I would like to know, if anyone can provide me some documentation showing in detail, what factors are most important to be considered for benchmarking?
In other words I am looking for documentation which provides detailed information about factors that should be considered for enhancement in the performance of
Core
Peripherals,
Memory banks
Plus, if someone could provide algorithms that will be lot helpful.
There is only one useful way and that is to write your application for both and time your application. Benchmarks are for the most part bogus there are too many factors and it is quite trivial to craft a benchmark that takes advantage of the differences, or even takes advantage of the common features in a way to make two things look different.
I perform this stunt on a regular basis, most recently this code
.globl ASMDELAY
ASMDELAY:
subs r0,r0,#1
bne ASMDELAY
bx lr
Run on a raspberry pi (bare metal) the same raspberry pi not comparing two just comparing it to itself, clearly assembly so not even taking into account compiler features/tricks that you can encode in the benchmark intentionally or accidentally. Two of those three instructions matter for benchmarking purposes, have the loop run many tens of thousands of times I think I used 0x100000. The punchline to that performance was those two instructions in a loop ran as fast as 93662 timer ticks and as slow as 4063837 timer ticks for 0x10000 loops. Certainly i cache and branch prediction were turned on and off for various tests. But even with both branch prediction on and the i cache on, these two instructions will vary in speed depending on where they lie within the fetch line and the cache line.
A microcontroller makes this considerably worse depending on what you are comparing, some have flashes that can use the same wait state for a wide range of clock speeds, some are speed limited and for every N Mhz you have to add another wait state, so depending on where you set your clock it affects performance across that range and definitely just below and just above the boundary where you add a wait state (24Mhz minus a smidge and 24Mhz with an extra wait state if it was from 2-3 wait states then fetching just got 50% slower 36Mhz minus a smidge it may still be at the 3 wait states but 3 wait states at 36minus a smidge is faster than 24mhz 3 wait states). if you run the same code in sram vs flash for those platforms there usually isnt a wait state issue the sram can usually match the cpu clock and so that code at any speed may be faster than the same code run from flash.
If you are comparing two microcontrollers from the same vendor and family then it is usually pointless, the internals are the same they usually just vary by how many, how many flash banks how many sram banks how many uarts, how many timers, how many pins, etc.
One of my points being if you dont know the nuances of the overall architecture, you can possibly make the same code you are running now on the same board a few percent to tens of times faster by simply understanding how things work. Enabling features you didnt know where there, proper alignment of the code that is exercised often (simply re-arranging your functions within a C file can/will affect performance) adding one or more nops in the bootstrap to change the alignment of the whole program can and will change performance.
Then you get into compiler differences and compiler options, you can play with those and also get some to several to dozens of times improvement (or loss).
So at the end of the day the only thing that matters is I have an application it is the final binary and how fast does it run on A, then I ported that application and the final binary for B is done and how fast does it run there. Everything else can be manipulated, the results cant be trusted.
I recently read the question here Why is it faster to process a sorted array than an unsorted array? and found the answer to be absolutely fascinating and it has completely changed my outlook on programming when dealing with branches that are based on Data.
I currently have a fairly basic, but fully functioning interpreted Intel 8080 Emulator written in C, the heart of the operation is a 256 long switch-case table for handling each opcode. My initial thought was this would obviously be the fastest method of working as opcode encoding isn't consistent throughout the 8080 instruction set and decoding would add a lot of complexity, inconsistency and one-off cases. A switch-case table full of pre-processor macros is a very neat and easy to maintain.
Unfortunately, after reading the aforementioned post it occurred to me that there's absolutely no way the branch predictor in my computer can predict the jumping for the switch case. Thus every time the switch-case is navigated the pipeline would have to be completely wiped, resulting in a several cycle delay in what should otherwise be an incredibly quick program (There's not even so much as multiplication in my code).
I'm sure most of you are thinking "Oh, the solution here is simple, move to dynamic recompilation". Yes, this does seem like it would cut out the majority of the switch-case and increase speed considerably. Unfortunately my primary interest is emulating older 8-bit and 16-bit era consoles (the intel 8080 here is only an example as it's my simplest piece of emulated code) where cycle and timing keeping to the exact instruction is important as the Video and Sound must be processed based on these exact timings.
When dealing with this level of accuracy performance becomes an issue, even for older consoles (Look at bSnes for example). Is there any recourse or is this simply a matter-of-fact when dealing with processors with long pipelines?
On the contrary, switch statements are likely to be converted to jump tables, which means they perform possibly a few ifs (for range checking), and a single jump. The ifs shouldn't cause a problem with branch prediction because it is unlikely you will have a bad op-code. The jump is not so friendly with the pipeline, but in the end, it's only one for the whole switch statement..
I don't believe you can convert a long switch statement of op-codes into any other form that would result in better performance. This is of course, if your compiler is smart enough to convert it to a jump table. If not, you can do so manually.
If in doubt, implement other methods and measure performance.
Edit
First of all, make sure you don't confuse branch prediction and branch target prediction.
Branch prediction solely works on branch statements. It decides whether a branch condition would fail or succeed. They have nothing to do with the jump statement.
Branch target prediction on the other hand tries to guess where the jump will end up in.
So, your statement "there's no way the branch predictor can predict the jump" should be "there's no way the branch target predictor can predict the jump".
In your particular case, I don't think you can actually avoid this. If you had a very small set of operations, perhaps you could come up with a formula that covers all your operations, like those made in logic circuits. However, with an instruction set as big as a CPU's, even if it were RISC, the cost of that computation is much higher than the penalty of a single jump.
As the branches on your 256-way switch statement are densely packed the compiler will implement this as a jump table, so you're correct in that you'll trigger a single branch mispredict every time you pass through this code (as the indirect jump won't display any kind of predictable behaviour). The penalty associated with this will be around 15 clock cycles on a modern CPU (Sandy Bridge), or maybe up to 25 on older microarchitectures that lack a micro-op cache. A good reference for this sort of thing is "Software optimisation resources" on agner.org. Page 43 in "Optimizing software in C++" is a good place to start.
http://www.agner.org/optimize/?e=0,34
The only way you could avoid this penalty is by ensuring that the same instructions are execution regardless of the value of the opcode. This can often be done by using conditional moves (which add a data dependency so are slower than a predictable branch) or otherwise looking for symmetry in your code paths. Considering what you're trying to do this is probably not going to be possible, and if it was then it would almost certainly add a overhead greater than the 15-25 clock cycles for the mispredict.
In summary, on a modern architecture there's not much you can do that'll be more efficient than a switch/case, and the cost of mispredicting a branch isn't as much as you might expect.
The indirect jump is probably the best thing to do for instruction decoding.
On older machines, like say the Intel P6 from 1997, the indirect jump would probably get a branch misprediction.
On modern machines, like say Intel Core i7, there is an indirect jump predictor that does a fairly good job of avoiding the branch misprediction.
But even on the older machines that do not have an indirect branch predictor, you can play a trick. This trick is (was), by the way, documented in the Intel Code Optimization Guide from way back in the Intel P6 days:
Instead of generating something that looks like
loop:
load reg := next_instruction_bits // or byte or word
load reg2 := instruction_table[reg]
jmp [reg]
label_instruction_00h_ADD: ...
jmp loop
label_instruction_01h_SUB: ...
jmp loop
...
generate the code as
loop:
load reg := next_instruction_bits // or byte or word
load reg2 := instruction_table[reg]
jmp [reg]
label_instruction_00h_ADD: ...
load reg := next_instruction_bits // or byte or word
load reg2 := instruction_table[reg]
jmp [reg]
label_instruction_01h_SUB: ...
load reg := next_instruction_bits // or byte or word
load reg2 := instruction_table[reg]
jmp [reg]
...
i.e. replace the jump to the top of the instruction fetch/decode/execute loop
by the code at the top of the loop at each place.
It turns out that this has much better branch prediction, even in the absence of an indirect predictor. More precisely, a conditional, single target, PC indexed BTB will be quite a lot better in this latter, threaded, code, than on the original with only a single copy of the indirect jump.
Most instruction sets have special patterns - e.g. on Intel x86, a compare instruction is nearly always followed by a branch.
Good luck and have fun!
(In case you care, the instruction decoders used by instruction set simulators in industry nearly always do a tree of N-way jumps, or the data-driven dual, navigate a tree of N-way tables, with each entry in the tree pointing to other nodes, or to a function to evaluate.
Oh, and perhaps I should mention: these tables, these switch statements or data structures, are generated by special purpose tools.
A tree of N-way jumps, because there are problems when the number of cases in the jump table gets very large - in the tool, mkIrecog (make instruction recognizer) that I wrote in the 1980s, I usually did jump tables up to 64K entries in size, i.e. jumping on 16 bits. The compilers of the time broke when the jump tables exceeded 16M in size (24 bits).
Data driven, i.e. a tree of nodes pointing to other nodes because (a) on older machines indirect jumps may not be predicted well, and (b) it turns out that much of the time there is common code between instructions - instead of having a branch misprediction when jumping to the case per instruction, then executing common code, then switching again, and getting a second mispredict, you do the common code, with slightly different parameters (like, how many bits of the instruction stream do you consume, and where the next set of bits to branch on is (are).
I was very aggressive in mkIrecog, as I say allowing up to 32 bits to be used in a switch, although practical limitations nearly always stopped me at 16-24 bits. I remember that I often saw the first decode as a 16 or 18 bit switch (64K-256K entries), and all other decodes were much smaller, no bigger than 10 bits.
Hmm: I posted mkIrecog to Usenet back circa 1990. ftp://ftp.lf.net/pub/unix/programming/misc/mkIrecog.tar.gz
You may be able to see the tables used, if you care.
(Be kind: I was young then. I can't remember if this was Pascal or C. I have since rewritten it many times - although I have not yet rewritten it to use C++ bit vectors.)
Most of the other guys I know who do this sort of thing do things a byte at a time - i.e. an 8 bit, 256 way, branch or table lookup.)
I thought I'd add something since no one mentioned it.
Granted, the indirect jump is likely to be the best option.
However, should you go with the N-compare way, there are two things that come to my mind:
First, instead of doing N equality compares, you could do log(N) inequality compares, testing your instructions based on their numerical opcode by dichotomy (or test the number bit by bit if the value space is near to full) .This is a bit like a hashtable, you implement a static tree to find the final element.
Second, you could run an analysis on the binary code you want to execute.
You could even do that per binary, before execution, and runtime-patch your emulator.
This analysis would build a histogram representing the frequency of instructions, and then you would organize your tests so that the most frequent instructions get predicted correctly.
But I cant see this being faster than a medium 15 cycles penalty, unless you have 99% of MOV and you put an equality for the MOV opcode before the other tests.
I'm trying to find the find the relative merits of 2 small functions in C. One that adds by loop, one that adds by explicit variables. The functions are irrelevant themselves, but I'd like someone to teach me how to count cycles so as to compare the algorithms. So f1 will take 10 cycles, while f2 will take 8. That's the kind of reasoning I would like to do. No performance measurements (e.g. gprof experiments) at this point, just good old instruction counting.
Is there a good way to do this? Are there tools? Documentation? I'm writing C, compiling with gcc on an x86 architecture.
http://icl.cs.utk.edu/papi/
PAPI_get_real_cyc(3) - return the total number of cycles since some arbitrary starting point
Assembler instruction rdtsc (Read Time-Stamp Counter) retun in EDX:EAX registers the current CPU ticks count, started at CPU reset. If your CPU runing at 3GHz then one tick is 1/3GHz.
EDIT:
Under MS windows the API call QueryPerformanceFrequency return the number of ticks per second.
Unfortunately timing the code is as error prone as visually counting instructions and clock cycles. Be it a debugger or other tool or re-compiling the code with a re-run 10000000 times and time it kind of thing, you change where things land in the cache line, the frequency of the cache hits and misses, etc. You can mitigate some of this by adding or removing some code upstream from the module of code being tested, (to cause a few instructions added and removed changing the alignment of your program and sometimes of your data).
With experience you can develop an eye for performance by looking at the disassembly (as well as the high level code). There is no substitute for timing the code, problem is timing the code is error prone. The experience comes from many experiements and trying to fully understand why adding or removing one instruction made no or dramatic differences. Why code added or removed in a completely different unrelated area of the module under test made huge performance differences on the module under test.
As GJ has written in another answer I also recommend using the "rdtsc" instruction (rather than calling some operating system function which looks right).
I've written quite a few answers on this topic. Rdtsc allows you to calculate the elapsed clock cycles in the code's "natural" execution environment rather than having to resort to calling it ten million times which may not be feasible as not all functions are black boxes.
If you want to calculate elapsed time you might want to shut off energy-saving on the CPUs. If it's only a matter of clock cycles this is not necessary.
If you are trying to compare the performance, the easiest way is to put your algorithm in a loop and run it 1000 or 1000000 times.
Once you are running it enough times that the small differences can be seen, run time ./my_program which will give you the amount of processor time that it used.
Do this a few times to get a sampling and compare the results.
Trying to count instructions won't help you on x86 architecture. This is because different instructions can take significantly different amounts of time to execute.
I would recommend using simulators. Take a look at PTLsim it will give you the number of cycles, other than that maybe you would like to take a look at some tools to count the number of times each assembly line is executing.
Use gcc -S your_program.c. -S tells gcc to generate the assembly listing, that will be named your_program.s.
There are plenty of high performance clocks around. QueryPerformanceCounter is microsofts. The general trick is to run the function 10s of thousands of time and time how long it takes. Then divide the time taken by the number of loops. You'll find that each loop takes a slightly different length of time so this testing over multiple passes is the only way to truly find out how long it takes.
This is not really a trivial question. Let me try to explain:
There are several tools on different OS to do exactly what you want, but those tools are usually part of a bigger environment. Every instruction is translated into a certain number of cycles, depending on the CPU the compiler ran on, and the CPU the program was executed.
I can't give you a definitive answer, because I do not have enough data to pass my judgement on, but I work for IBM in the database area and we use tools to measure cycles and instructures for our code and those traces are only valid for the actual CPU the program was compiled and was running on.
Depending on the internal structure of your CPU's piplining and on the effeciency of your compiler, the resulting code will most likely still have cache misses and other areas you have to worry about. (In that case you may want to look into FDPR...)
If you want to know how many cycles your program needs to run on your CPU (which was compiled with your compiler), you have to understand how the CPU works and how the compiler generarated the code.
I'm sorry, if the answer was not sufficient enough to solve your problem at hand. You said you are using gcc on an x86 arch. I would work with getting the assembly code mapped to your CPU.
I'm sure you will find some areas, where gcc could have done a better job...
I'm writing some micro-benchmarking code for some very short operations in C. For example, one thing I'm measuring is how many cycles are needed to call an empty function depending on the number of arguments passed.
Currently, I'm timing using an RDTSC instruction before and after each operation to get the CPU's cycle count. However, I'm concerned that instructions issued before the first RDTSC may slow down the actual instructions I'm measuring. I'm also worried that the full operation may not be complete before the second RDTSC gets issued.
Does anyone know of an x86 instruction that forces all in-flight instructions to commit before any new instructions are issued? I've been told CPUID might do this, but I've been unable to find any documentation that says so.
To my knowledge, there is no instruction which specifically "drains" the pipeline. This can be easily accomplished though using a serialising instruction.
CPUID is a serializing instruction, which means exactly what you're looking for. Every instruction issues before it is guaranteed to execute before the CPUID instruction.
So doing the following should get the desired effect:
cpuid
rdtsc
# stuff
cpuid
rdtsc
But, as an aside, I don't recommend that you do this. Your "stuff" can still be effected by a lot of other things outside of your control (such as CPU caches, other processes running on the system, etc), and you'll never be able to eliminate them all. The best way to get accurate performance statistics is to perform the operation(s) you want to measure at least several million times and average out the execution time of the batch.
Edit: Most instruction references for CPUID will mention its serializing properties, such as the NASM manual appendix B .
Edit 2: Also might want to take a look at this related question.