Say that we have two C++ code segments, for doing the same task. How can we determine which code will run faster?
As an example lets say there is this global array "some_struct_type numbers[]". Inside a function, I can read a location of this array in two ways(I do not want to alter the content of the array)
some_struct_type val = numbers[i];
some_struct_type* val = &numbers[i]
I assume the second one is faster. but I can't measure the time to make sure because it will be a negligible difference.
So in this type of a situation, how do I figure out which code segment runs faster? Is there a way to compile a single line of code or set of lines and view
how many lines of assembly instructions are there?
I would appreciate your thoughts on this matter.
The basics are to run the piece of code so many times that it takes a few seconds at least to complete, and measure the time.
But it's hard, very hard, to get any meaningful figures this way, for many reasons:
Todays compilers are very good at optimizing code, but the optimizations depend on the context. It often does not make sense to look at a single line and try to optimize it. When the same line appears in a different context, the optimizations applied may be different.
Short pieces of code can be much faster than the surrounding looping code.
Not only the compiler makes optimizations, the processor has a cache, an instruction pipeline, and tries to predict branching code. A value which has been read before will be read much faster the next time, for example.
...
Because of this, it's usually better to leave the code in its place in your program, and use a profiling tool to see which parts of your code use the most processing resources. Then, you can change these parts and profile again.
While writing new code, prefer readable code to seemingly optimal code. Choose the right algorithm, this also depends on your input sizes. For example, insertion sort can be faster than quicksort, if the input is very small. But don't write your own sorting code, if your input is not special, use the libraries available in general. And don't optimize prematurely.
Eugene Sh. is correct that these two lines aren't doing the same thing - the first one copies the value of numbers[i] into a local variable, whereas the second one stores the address of numbers[i] into a pointer local variable. If you can do what you need using just the address of numbers[i] and referring back to numbers[i], it's likely that will be faster than doing a wholesale copy of the value, although it depends on a lot of factors like the size of the struct, etc.
Regarding the general optimization question, here are some things to consider...
Use a Profiler
The best way to measure the speed of your code is to use a profiling tool. There are a number of different tools available, depending on your target platform - see (for example) How can I profile C++ code running in Linux? and What's the best free C++ profiler for Windows?.
You really want to use a profiler for this because it's notoriously difficult to tell just from looking what the costliest parts of a program will be, for a number of reasons...
# of Instructions != # of Processor Cycles
One reason to use a profiler is that it's often difficult to tell from looking at two pieces of code which one will run faster. Even in assembly code, you can't simply count the number of instructions, because many instructions take multiple processor cycles to complete. This varies considerably by target platform. For example, on some platforms the fastest way to load the value 1 to a CPU register is something straightforward like this:
MOV r0, #1
Whereas on other platforms the fastest approach is actually to clear the register and then increment it, like this:
CLR r0
INC r0
The second case has more instruction lines, but that doesn't necessarily mean that it's slower.
Other Complications
Another reason that it's difficult to tell which pieces of code will most need optimizing is that most modern computers employ fairly sophisticated caches that can dramatically improve performance. Executing a cached loop several times is often less expensive than loading a single piece of data from a location that isn't cached. It can be very difficult to predict exactly what will cause a cache miss, but when using a profiler you don't have to predict - it makes the measurements for you.
Avoid Premature Optimization
For most projects, optimizing your code is best left until relatively late in the process. If you start optimizing too early, you may find that you spend a lot of time optimizing a feature that turns out to be relatively inexpensive compared to your program's other features. That said, there are some notable counterexamples - if you're building a large-scale database tool you might reasonably expect that performance is going to be an important selling point.
My Objective is : I want to test a piece of code (or function) performance, just like how I test the correctness of that function in a unit-test, let say that the output of this benchmarking process is a "function performance index" which is "portable"
My Problem is : we usually benchmarking a code by using a timer to count elapsed time during execution of that code. and that method is depend on the hardware or O/S or other thing.
My Question is : is there a method to get a "function performance index" that is independent to the performance of the host (CPU/OS/etc..), or if not "independent" lets say it is "relative" to some fixed value. so that somehow the value of "function performance index" is still valid on any platform or hardware performance.
for example: that FPI value is could be measured in
number of arithmetic instruction needed to execute a single call
float value compared to benchmark function, for example function B has rating index of 1.345 (which is the performance is slower 1.345 times than the benchmark function)
or other value.
note that the FPI value doesn't need to be scientifically correct, exact or accurate, I just need a value to give a rough overview of that function performance compared to other function which was tested by the same method.
I think you are in search of the impossible here, because the performance of a modern computer is a complex blend of CPU, cache, memory controller, memory, etc.
So one (hypothetical) computer system might reward the use of enormous look-up tables to simplify an algorithm, so that there were very few cpu instructions processed. Whereas another system might have memory much slower relative to the CPU core, so an algorithm which did a lot of processing but touched very little memory would be favoured.
So a single 'figure of merit' for these two algorithms could not even convey which was the better one on all systems, let alone by how much it was better.
Probably what you really need is a tcov-like tool.
man tcov says:
Each basic block of code (or each
line if the -a option to tcov is specified) is prefixed with
the number of times it has been executed; lines that have
not been executed are prefixed with "#####". A basic block
is a contiguous section of code that has no branches: each
statement in a basic block is executed the same number of
times.
No, there is no such thing. Different hardware performs differently. You can have two different pieces of code X and Y such that hardware A runs X faster than Y but hardware B runs Y faster than X. There is no absolute scale of performance, it depends entirely on the hardware (not to mention other things like the operating system and other environmental considerations).
It sounds like what you want is a program that calculates the Big-O Notation of a piece of code. I don't know if it's possible to do that in an automated fashion (Halting problem, etc).
Like others have mentioned this is not a trivial task and may be impossible to get any sort of accurate results from. Considering a few methods:
Benchmark Functions -- While this seems promising I think you'll find that it won't work well as you try to compare different types of functions. For example, if your benchmark function is 100% CPU bound (as in some complex math computation) then it will compare/scale well with other CPU bound functions but fail when compared with, say, I/O or memory bound functions. Carefully matching a benchmark function to a small set of similar functions may work but is tedious/time consuming.
Number of Instructions -- For a very simple processor it may be possible to count the cycles of each instruction and get a reasonable value for the total number of cycles a block of code will take but with today's modern processors are anything but "simple". With branch prediction and parallel pipelines you can can't just add up instruction cycles and expect to get an accurate result.
Manual Counting -- This might be your best bet and while it is not automatic it may give better results faster than the other methods. Just look at things like the O() order of the code, how much memory the function reads/writes, how many file bytes are input/output etc.... By having a few stats like this for each function/module you should be able to get a rough comparison of their complexity.
I'm working on scientific code that is very performance-critical. An initial version of the code has been written and tested, and now, with profiler in hand, it's time to start shaving cycles from the hot spots.
It's well-known that some optimizations, e.g. loop unrolling, are handled these days much more effectively by the compiler than by a programmer meddling by hand. Which techniques are still worthwhile? Obviously, I'll run everything I try through a profiler, but if there's conventional wisdom as to what tends to work and what doesn't, it would save me significant time.
I know that optimization is very compiler- and architecture- dependent. I'm using Intel's C++ compiler targeting the Core 2 Duo, but I'm also interested in what works well for gcc, or for "any modern compiler."
Here are some concrete ideas I'm considering:
Is there any benefit to replacing STL containers/algorithms with hand-rolled ones? In particular, my program includes a very large priority queue (currently a std::priority_queue) whose manipulation is taking a lot of total time. Is this something worth looking into, or is the STL implementation already likely the fastest possible?
Along similar lines, for std::vectors whose needed sizes are unknown but have a reasonably small upper bound, is it profitable to replace them with statically-allocated arrays?
I've found that dynamic memory allocation is often a severe bottleneck, and that eliminating it can lead to significant speedups. As a consequence I'm interesting in the performance tradeoffs of returning large temporary data structures by value vs. returning by pointer vs. passing the result in by reference. Is there a way to reliably determine whether or not the compiler will use RVO for a given method (assuming the caller doesn't need to modify the result, of course)?
How cache-aware do compilers tend to be? For example, is it worth looking into reordering nested loops?
Given the scientific nature of the program, floating-point numbers are used everywhere. A significant bottleneck in my code used to be conversions from floating point to integers: the compiler would emit code to save the current rounding mode, change it, perform the conversion, then restore the old rounding mode --- even though nothing in the program ever changed the rounding mode! Disabling this behavior significantly sped up my code. Are there any similar floating-point-related gotchas I should be aware of?
One consequence of C++ being compiled and linked separately is that the compiler is unable to do what would seem to be very simple optimizations, such as move method calls like strlen() out of the termination conditions of loop. Are there any optimization like this one that I should look out for because they can't be done by the compiler and must be done by hand?
On the flip side, are there any techniques I should avoid because they are likely to interfere with the compiler's ability to automatically optimize code?
Lastly, to nip certain kinds of answers in the bud:
I understand that optimization has a cost in terms of complexity, reliability, and maintainability. For this particular application, increased performance is worth these costs.
I understand that the best optimizations are often to improve the high-level algorithms, and this has already been done.
Is there any benefit to replacing STL containers/algorithms with hand-rolled ones? In particular, my program includes a very large priority queue (currently a std::priority_queue) whose manipulation is taking a lot of total time. Is this something worth looking into, or is the STL implementation already likely the fastest possible?
I assume you're aware that the STL containers rely on copying the elements. In certain cases, this can be a significant loss. Store pointers and you may see an increase in performance if you do a lot of container manipulation. On the other hand, it may reduce cache locality and hurt you. Another option is to use specialized allocators.
Certain containers (e.g. map, set, list) rely on lots of pointer manipulation. Although counterintuitive, it can often lead to faster code to replace them with vector. The resulting algorithm might go from O(1) or O(log n) to O(n), but due to cache locality it can be much faster in practice. Profile to be sure.
You mentioned you're using priority_queue, which I would imagine pays a lot for rearranging the elements, especially if they're large. You can try switching the underlying container (maybe deque or specialized). I'd almost certainly store pointers - again, profile to be sure.
Along similar lines, for a std::vectors whose needed sizes are unknown but have a reasonably small upper bound, is it profitable to replace them with statically-allocated arrays?
Again, this may help a small amount, depending on the use case. You can avoid the heap allocation, but only if you don't need your array to outlive the stack... or you could reserve() the size in the vector so there is less copying on reallocation.
I've found that dynamic memory allocation is often a severe bottleneck, and that eliminating it can lead to significant speedups. As a consequence I'm interesting in the performance tradeoffs of returning large temporary data structures by value vs. returning by pointer vs. passing the result in by reference. Is there a way to reliably determine whether or not the compiler will use RVO for a given method (assuming the caller doesn't need to modify the result, of course)?
You could look at the generated assembly to see if RVO is applied, but if you return pointer or reference, you can be sure there's no copy. Whether this will help is dependent on what you're doing - e.g. can't return references to temporaries. You can use arenas to allocate
and reuse objects, so not to pay a large heap penalty.
How cache-aware do compilers tend to be? For example, is it worth looking into reordering nested loops?
I've seen dramatic (seriously dramatic) speedups in this realm. I saw more improvements from this than I later saw from multithreading my code. Things may have changed in the five years since - only one way to be sure - profile.
On the flip side, are there any techniques I should avoid because they are likely to interfere with the compiler's ability to automatically optimize code?
Use explicit on your single argument constructors. Temporary object construction and destruction may be hidden in your code.
Be aware of hidden copy constructor calls on large objects. In some cases, consider replacing with pointers.
Profile, profile, profile. Tune areas that are bottlenecks.
Take a look at the excellent Pitfalls of Object-Oriented Programming slides for some info about restructuring code for locality. In my experience getting better locality is almost always the biggest win.
General process:
Learn to love the Disassembly View in your debugger, or have your build system generate the intermediate assembly files (.s) if at all possible. Keep an eye on changes or for things that look egregious -- even without familiarity with a given instruction set architecture, you should be able to see some things fairly clearly! (I sometimes check in a series of .s files with corresponding .cpp/.c changes, just to leverage the lovely tools from my SCM to watch the code and corresponding asm change over time.)
Get a profiler that can watch your CPU's performance counters, or can at least guess at cache misses. (AMD CodeAnalyst, cachegrind, vTune, etc.)
Some other specific things:
Understand strict aliasing. Once you do, make use of restrict if your compiler has it. (Examine the disasm here too!)
Check out different floating point modes on your processor and compiler. If you don't need the denormalized range, choosing a mode without this can result in better performance. (It sounds like you've already done some things in this area, based on your discussion of rounding modes.)
Definitely avoid allocs: call reserve on std::vector when you can, or use std::array when you know the size at compile-time.
Use memory pools to increase locality and decrease alloc/free overhead; also to ensure cacheline alignment and prevent ping-ponging.
Use frame allocators if you're allocating things in predictable patterns, and can afford to deallocate everything in one go.
Do be aware of invariants. Something you know is invariant may not be to the compiler, for example a use of a struct or class member in a loop. I find the single easiest way to fall into the correct habit here is to give a name to everything, and prefer to name things outside of loops. E.g. const int threshold = m_currentThreshold; or perhaps Thing * const pThing = pStructHoldingThing->pThing; Fortunately you can usually see things that need this treatment in the disassembly view. This also helps with debugging later (makes the watch/locals window behave much more nicely in debug builds)!
Avoid writes in loops if possible -- accumulate first, then write, or batch a few writes together. YMMV, of course.
WRT your std::priority_queue question: inserting things into a vector (the default backend for a priority_queue) tends to move a lot of elements around. If you can break up into phases, where you insert data, then sort it, then read it once it's sorted, you'll probably be a lot better off. Although you'll definitely lose locality, you may find a more self-ordering structure like a std::map or std::set worth the overhead -- but this is really dependent on your usage patterns.
Is there any benefit to replacing STL containers/algorithms with hand-rolled ones?
I would only consider this as a last option. The STL containers and algorithms have been thoroughly tested. Creating new ones are expensive in terms of development time.
Along similar lines, for std::vectors whose needed sizes are unknown but have a reasonably small upper bound, is it profitable to replace them with statically-allocated arrays?
First, try reserving space for the vectors. Check out the std::vector::reserve method. A vector that keeps growing or changing to larger sizes is going to waste dynamic memory and execution time. Add some code to determine a good value for an upper bound.
I've found that dynamic memory allocation is often a severe bottleneck, and that eliminating it can lead to significant speedups. As a consequence I'm interesting in the performance tradeoffs of returning large temporary data structures by value vs. returning by pointer vs. passing the result in by reference. Is there a way to reliably determine whether or not the compiler will use RVO for a given method (assuming the caller doesn't need to modify the result, of course)?
As a matter of principle, always pass large structures by reference or pointer. Prefer passing by constant reference. If you are using pointers, consider using smart pointers.
How cache-aware do compilers tend to be? For example, is it worth looking into reordering nested loops?
Modern compilers are very aware of instruction caches (pipelines) and try to keep them from being reloaded. You can always assist your compiler by writing code that uses less branches (from if, switch, loop constructs and function calls).
You may see more significant performance gain by adjusting your program to optimize the data cache. Search the web for Data Driven Design. There are many excellent articles on this topic.
Given the scientific nature of the program, floating-point numbers are used everywhere. A significant bottleneck in my code used to be conversions from floating point to integers: the compiler would emit code to save the current rounding mode, change it, perform the conversion, then restore the old rounding mode --- even though nothing in the program ever changed the rounding mode! Disabling this behavior significantly sped up my code. Are there any similar floating-point-related gotchas I should be aware of?
For accuracy, keep everything as a double. Adjust for rounding only when necessary and perhaps before displaying. This falls under the optimization rule, Use less code, eliminate extraneous or deadwood code.
Also see the section above about reserving space in containers before using them.
Some processors can load and store floating point numbers either faster or as fast as integers. This would require gathering profile data before optimizing. However, if you know there is minimal resolution, you could use integers and change your base to that minimal resolution . For example, when dealing with U.S. money, integers can be used to represent 1/100 or 1/1000 of a dollar.
One consequence of C++ being compiled and linked separately is that the compiler is unable to do what would seem to be very simple optimizations, such as move method calls like strlen() out of the termination conditions of loop. Are there any optimization like this one that I should look out for because they can't be done by the compiler and must be done by hand?
This an incorrect assumption. Compilers can optimize based on the function's signature, especially if the parameters correctly use const. I always like to assist the compiler by moving constant stuff outside of the loop. For an upper limit value, such as a string length, assign it to a const variable before the loop. The const modifier will assist the Optimizer.
There is always the count-down optimization in loops. For many processors, a jump on register equals zero is more efficient than compare and jump if less than.
On the flip side, are there any techniques I should avoid because they are likely to interfere with the compiler's ability to automatically optimize code?
I would avoid "micro optimizations". If you have any doubts, print out the assembly code generated by the compiler (for the area you are questioning) under the highest optimization setting. Try rewriting the code to express the compiler's assembly code. Optimize this code, if you can. Anything more requires platform specific instructions.
Optimization Ideas & Concepts
1. Computers prefer to execute sequential instructions.
Branching upsets them. Some modern processors have enough instruction cache to contain code for small loops. When in doubt, don't cause branches.
2. Eliminate Requirements
Less code, more performance.
3. Optimize designs before code
Often times, more performance can be gained by changing the design versus changing the implementation of the design. Less design promotes less code, generates more performance.
4. Consider data organization
Optimize the data.
Organize frequently used fields into substructures.
Set data sizes to fit into a data cache line.
Remove constant data out of data structures.
Use const specifier as much as possible.
5. Consider page swapping
Operating systems will swap out your program or task for another one. Often times into a 'swap file' on the hard drive. Breaking up the code into chunks that contain heavily executed code and less executed code will assist the OS. Also, coagulate heavily used code into tighter units. The idea is to reduce the swapping of code from the hard drive (such as fetching "far" functions). If code must be swapped out, it should be as one unit.
6. Consider I/O optimizations
(Includes file I/O too).
Most I/O prefers fewer large chunks of data to many small chunks of data. Hard drives like to keep spinning. Larger data packets have less overhead than smaller packets.
Format data into a buffer then write the buffer.
7. Eliminate the competition
Get rid of any programs and tasks that are competing against your application for the processor(s). Such tasks as virus scanning and playing music. Even I/O drivers want a piece of the action (which is why you want to reduce the number or I/O transactions).
These should keep you busy for a while. :-)
Use of memory buffer pools can be of great performance benefit vs. dynamic allocation. More so if they reduce or prevent heap fragmentation over long execution runs.
Be aware of data location. If you have a significant mix of local vs. global data you may be overworking the cache mechanism. Try to keep data sets in close proximity to make maximum use of cache line validity.
Even though compilers do a wonderful job with loops, I still scrutinize them when performance tuning. You can spot architectural flaws that yield orders of magnitude where the compiler may only trim percentages.
If a single priority queue is using a lot of time in its operation, there may be benefit to creating a battery of queues representing buckets of priority. It would be complexity being traded for speed in this case.
I notice you didn't mention the use of SSE type instructions. Could they be applicable to your type of number crunching?
Best of luck.
Here is a nice paper on the subject.
About STL containers.
Most people here claim STL offers one of the fastest possible implementations of the container algorithms. And I say the opposite: for the most real-world scenarios the STL containers taken as-is yield a really catastrophic performance.
People argue about the complexity of the algorithms used in STL. Here STL is good: O(1) for list/queue, vector (amortized), and O(log(N)) for map. But this is not the real bottleneck of the performance for a typical application! For many applications the real bottleneck is the heap operations (malloc/free, new/delete, etc.).
A typical operation on the list costs just a few CPU cycles. On a map - some tens, may be more (this depends on the cache state and log(N) of course). And typical heap operations cost from hunders to thousands (!!!) of CPU cycles. For multithreaded applications for instance they also require synchronization (interlocked operations). Plus on some OSs (such as Windows XP) the heap functions are implemented entirely in the kernel mode.
So that the actual performance of the STL containers in a typical scenario is dominated by the amount of heap operations they perform. And here they're disastrous. Not because they're implemented poorly, but because of their design. That is, this is the question of the design.
On the other hand there're other containers which are designed differently.
Once I've designed and written such containers for my own needs:
http://www.codeproject.com/KB/recipes/Containers.aspx
And it proved for me to be superior from the performance point of view, and not only.
But recently I've discovered I'm not the only one who thought about this.
boost::intrusive is the container library that is implemented in the manner similar to what I did then.
I suggest you try it (if you didn't already)
Is there any benefit to replacing STL containers/algorithms with hand-rolled ones?
Generally, not unless you're working with a poor implementation. I wouldn't replace an STL container or algorithm just because you think you can write tighter code. I'd do it only if the STL version is more general than it needs to be for your problem. If you can write a simpler version that does just what you need, then there might be some speed to gain there.
One exception I've seen is to replace a copy-on-write std::string with one that doesn't require thread synchronization.
for std::vectors whose needed sizes are unknown but have a reasonably small upper bound, is it profitable to replace them with statically-allocated arrays?
Unlikely. But if you're using a lot of time allocating up to a certain size, it might be profitable to add a reserve() call.
performance tradeoffs of returning large temporary data structures by value vs. returning by pointer vs. passing the result in by reference.
When working with containers, I pass iterators for the inputs and an output iterator, which is still pretty general.
How cache-aware do compilers tend to be? For example, is it worth looking into reordering nested loops?
Not very. Yes. I find that missed branch predictions and cache-hostile memory access patterns are the two biggest killers of performance (once you've gotten to reasonable algorithms). A lot of older code uses "early out" tests to reduce calculations. But on modern processors, that's often more expensive than doing the math and ignoring the result.
A significant bottleneck in my code used to be conversions from floating point to integers
Yup. I recently discovered the same issue.
One consequence of C++ being compiled and linked separately is that the compiler is unable to do what would seem to be very simple optimizations, such as move method calls like strlen() out of the termination conditions of loop.
Some compilers can deal with this. Visual C++ has a "link-time code generation" option that effective re-invokes the compiler to do further optimization. And, in the case of functions like strlen, many compilers will recognize that as an intrinsic function.
Are there any optimization like this one that I should look out for because they can't be done by the compiler and must be done by hand? On the flip side, are there any techniques I should avoid because they are likely to interfere with the compiler's ability to automatically optimize code?
When you're optimizing at this low level, there are few reliable rules of thumb. Compilers will vary. Measure your current solution, and decide if it's too slow. If it is, come up with a hypothesis (e.g., "What if I replace the inner if-statements with a look-up table?"). It might help ("eliminates stalls due to failed branch predictions") or it might hurt ("look-up access pattern hurts cache coherence"). Experiment and measure incrementally.
I'll often clone the straightforward implementation and use an #ifdef HAND_OPTIMIZED/#else/#endif to switch between the reference version and the tweaked version. It's useful for later code maintenance and validation. I commit each successful experiment to change control, and keep a log (spreadsheet) with the changelist number, run times, and explanation for each step in optimization. As I learn more about how the code behaves, the log makes it easy to back up and branch off in another direction.
You need a framework for running reproducible timing tests and to compare results to the reference version to make sure you don't inadvertently introduce bugs.
If I were working on this, I would expect an end-stage where things like cache locality and vector operations would come into play.
However, before getting to the end stage, I would expect to find a series of problems of different sizes having less to do with compiler-level optimization, and more to do with odd stuff going on that could never be guessed, but once found, are simple to fix. Usually they revolve around class overdesign and data structure issues.
Here's an example of this kind of process.
I have found that generalized container classes with iterators, which in principle the compiler can optimize down to minimal cycles, often are not so optimized for some obscure reason. I've also heard other cases on SO where this happens.
Others have said, before you do anything else, profile. I agree with that approach except I think there's a better way, and it's indicated in that link. Whenever I find myself asking if some specific thing, like STL, could be a problem, I just might be right - BUT - I'm guessing. The fundamental winning idea in performance tuning is find out, don't guess. It is easy to find out for sure what is taking the time, so don't guess.
here is some stuff I had used:
templates to specialize innermost loops bounds (makes them really fast)
use __restrict__ keywords for alias problems
reserve vectors beforehand to sane defaults.
avoid using map (it can be really slow)
vector append/ insert can be significantly slow. If that is the case, raw operations may make it faster
N-byte memory alignment (Intel has pragma aligned, http://www.intel.com/software/products/compilers/docs/clin/main_cls/cref_cls/common/cppref_pragma_vector.htm)
trying to keep memory within L1/L2 caches.
compiled with NDEBUG
profile using oprofile, use opannotate to look for specific lines (stl overhead is clearly visible then)
here are sample parts of profile data (so you know where to look for problems)
* Output annotated source file with samples
* Output all files
*
* CPU: Core 2, speed 1995 MHz (estimated)
--
* Total samples for file : "/home/andrey/gamess/source/blas.f"
*
* 1020586 14.0896
--
* Total samples for file : "/home/andrey/libqc/rysq/src/fock.cpp"
*
* 962558 13.2885
--
* Total samples for file : "/usr/include/boost/numeric/ublas/detail/matrix_assign.hpp"
*
* 748150 10.3285
--
* Total samples for file : "/usr/include/boost/numeric/ublas/functional.hpp"
*
* 639714 8.8315
--
* Total samples for file : "/home/andrey/gamess/source/eigen.f"
*
* 429129 5.9243
--
* Total samples for file : "/usr/include/c++/4.3/bits/stl_algobase.h"
*
* 411725 5.6840
--
example of code from my project
template<int ni, int nj, int nk, int nl>
inline void eval(const Data::density_type &D, const Data::fock_type &F,
const double *__restrict Q, double scale) {
const double * __restrict Dij = D[0];
...
double * __restrict Fij = F[0];
...
for (int l = 0, kl = 0, ijkl = 0; l < nl; ++l) {
for (int k = 0; k < nk; ++k, ++kl) {
for (int j = 0, ij = 0; j < nj; ++j, ++jk, ++jl) {
for (int i = 0; i < ni; ++i, ++ij, ++ik, ++il, ++ijkl) {
And I think the main hint anyone could give you is: measure, measure, measure. That and improving your algorithms.
The way you use certain language features, the compiler version, std lib implementation, platform, machine - all ply their role in performance and you haven't mentioned many of those and no one of us ever had your exact setup.
Regarding replacing std::vector: use a drop-in replacement (e.g., this one) and just try it out.
How cache-aware do compilers tend to be? For example, is it worth looking into reordering nested loops?
I can't speak for all compilers, but my experience with GCC shows that it will not heavily optimize code with respect to the cache. I would expect this to be true for most modern compilers. Optimization such as reordering nested loops can definitely affect performance. If you believe that you have memory access patterns that could lead to many cache misses, it will be in your interest to investigate this.
Is there any benefit to replacing STL
containers/algorithms with hand-rolled
ones? In particular, my program
includes a very large priority queue
(currently a std::priority_queue)
whose manipulation is taking a lot of
total time. Is this something worth
looking into, or is the STL
implementation already likely the
fastest possible?
The STL is generally the fastest, general case. If you have a very specific case, you might see a speed-up with a hand-rolled one. For example, std::sort (normally quicksort) is the fastest general sort, but if you know in advance that your elements are virtually already ordered, then insertion sort might be a better choice.
Along similar lines, for std::vectors
whose needed sizes are unknown but
have a reasonably small upper bound,
is it profitable to replace them with
statically-allocated arrays?
This depends on where you are going to do the static allocation. One thing I tried along this line was to static allocate a large amount of memory on the stack, then re-use later. Results? Heap memory was substantially faster. Just because an item is on the stack doesn't make it faster to access- the speed of stack memory also depends on things like cache. A statically allocated global array may not be any faster than the heap. I assume that you have already tried techniques like just reserving the upper bound. If you have a lot of vectors that have the same upper bound, consider improving cache by having a vector of structs, which contain the data members.
I've found that dynamic memory
allocation is often a severe
bottleneck, and that eliminating it
can lead to significant speedups. As a
consequence I'm interesting in the
performance tradeoffs of returning
large temporary data structures by
value vs. returning by pointer vs.
passing the result in by reference. Is
there a way to reliably determine
whether or not the compiler will use
RVO for a given method (assuming the
caller doesn't need to modify the
result, of course)?
I personally normally pass the result in by reference in this scenario. It allows for a lot more re-use. Passing large data structures by value and hoping that the compiler uses RVO is not a good idea when you can just manually use RVO yourself.
How cache-aware do compilers tend to
be? For example, is it worth looking
into reordering nested loops?
I found that they weren't particularly cache-aware. The issue is that the compiler doesn't understand your program and can't predict the vast majority of it's state, especially if you depend heavily on heap. If you have a profiler that ships with your compiler, for example Visual Studio's Profile Guided Optimization, then this can produce excellent speedups.
Given the scientific nature of the
program, floating-point numbers are
used everywhere. A significant
bottleneck in my code used to be
conversions from floating point to
integers: the compiler would emit code
to save the current rounding mode,
change it, perform the conversion,
then restore the old rounding mode ---
even though nothing in the program
ever changed the rounding mode!
Disabling this behavior significantly
sped up my code. Are there any similar
floating-point-related gotchas I
should be aware of?
There are different floating-point models - Visual Studio gives an fp:fast compiler setting. As for the exact effects of doing such, I can't be certain. However, you could try altering the floating point precision or other settings in your compiler and checking the result.
One consequence of C++ being compiled
and linked separately is that the
compiler is unable to do what would
seem to be very simple optimizations,
such as move method calls like
strlen() out of the termination
conditions of loop. Are there any
optimization like this one that I
should look out for because they can't
be done by the compiler and must be
done by hand?
I've never come across such a scenario. However, if you're genuinely concerned about such, then the option remains to do it manually. One of the things that you could try is calling a function on a const reference, suggesting to the compiler that the value won't change.
One of the other things that I want to point out is the use of non-standard extensions to the compiler, for example provided by Visual Studio is __assume. http://msdn.microsoft.com/en-us/library/1b3fsfxw(VS.80).aspx
There's also multithread, which I would expect you've gone down that road. You could try some specific opts, like another answer suggested SSE.
Edit: I realized that a lot of the suggestions I posted referenced Visual Studio directly. That's true, but, GCC almost certainly provides alternatives to the majority of them. I just have personal experience with VS most.
The STL priority queue implementation is fairly well-optimized for what it does, but certain kinds of heaps have special properties that can improve your performance on certain algorithms. Fibonacci heaps are one example. Also, if you're storing objects with a small key and a large amount of satellite data, you'll get a major improvement in cache performance if you store that data separately, even if it means storing one extra pointer per object.
As for arrays, I've found std::vector to even slightly out-perform compile-time-constant arrays. That said, its optimizations are general, and specific knowledge of your algorithm's access patterns may allow you to optimize further for cache locality, alignment, coloring, etc. If you find that your performance drops significantly past a certain threshold due to cache effects, hand-optimized arrays may move that problem size threshold by as much as a factor of two in some cases, but it's unlikely to make a huge difference for small inner loops that fit easily within the cache, or large working sets that exceed the size of any CPU cache. Work on the priority queue first.
Most of the overhead of dynamic memory allocation is constant with respect to the size of the object being allocated. Allocating one large object and returning it by a pointer isn't going to hurt much as much as copying it. The threshold for copying vs. dynamic allocation varies greatly between systems, but it should be fairly consistent within a chip generation.
Compilers are quite cache-aware when cpu-specific tuning is turned on, but they don't know the size of the cache. If you're optimizing for cache size, you may want to detect that or have the user specify it at run-time, since that will vary even between processors of the same generation.
As for floating point, you absolutely should be using SSE. This doesn't necessarily require learning SSE yourself, as there are many libraries of highly-optimized SSE code that do all sorts of important scientific computing operations. If you're compiling 64-bit code, the compiler might emit some SSE code automatically, as SSE2 is part of the x86_64 instruction set. SSE will also save you some of the overhead of x87 floating point, since it's not converting back and forth to 80-bit values internally. Those conversions can also be a source of accuracy problems, since you can get different results from the same set of operations depending on how they get compiled, so it's good to be rid of them.
If you work on big matrices for instance, consider tiling your loops to improve the locality. This often leads to dramatic improvements. You can use VTune/PTU to monitor the L2 cache misses.
One consequence of C++ being compiled and linked separately is that the compiler is unable to do what would seem to be very simple optimizations, such as move method calls like strlen() out of the termination conditions of loop. Are there any optimization like this one that I should look out for because they can't be done by the compiler and must be done by hand?
On some compilers this is incorrect. The compiler has perfect knowledge of all code across all translation units (including static libraries) and can optimize the code the same way it would do if it were in a single translation unit. A few ones that support this feature come to my mind:
Microsoft Visual C++ compilers
Intel C++ Compiler
LLVC-GCC
GCC (I think, not sure)
i'm surprised no one has mentioned these two:
Link time optimization clang and g++ from 4.5 on support link time optimizations. I've heard that on g++ case, the heuristics is still pretty inmature but it should improve quickly since the main architecture is laid out.
Benefits range from inter procedural optimizations at object file level, including highly sought stuff like inling of virtual calls (devirtualization)
Project inlining this might seem to some like very crude approach, but it is that very crudeness which makes it so powerful: this amounts at dumping all your headers and .cpp files into a single, really big .cpp file and compile that; basically it will give you the same benefits of link-time optimization in your trip back to 1999. Of course, if your project is really big, you'll still need a 2010 machine; this thing will eat your RAM like there is no tomorrow. However, even in that case, you can split it in more than one no-so-damn-huge .cpp file
If you are doing heavy floating point math you should consider using SSE to vectorize your computations if that maps well to your problem.
Google SSE intrinsics for more information about this.
Here is something that worked for me once. I can't say that it will work for you. I had code on the lines of
switch(num) {
case 1: result = f1(param); break;
case 2: result = f2(param); break;
//...
}
Then I got a serious performance boost when I changed it to
// init:
funcs[N] = {f1, f2 /*...*/};
// later in the code:
result = (funcs[num])(param);
Perhaps someone here can explain the reason the latter version is better. I suppose it has something to do with the fact that there are no conditional branches there.
My current project is a media server, with multi thread processing (C++ language). It's a time critical application, once low performance functions could cause bad results on media streaming like lost of sync, high latency, huge delays and so.
The strategy i usually use to grantee the best performance possible is to minimize the amount of heavy operational system calls that allocate or manage resources like memory, files, sockets and so.
At first i wrote my own STL, network and file manage classes.
All my containers classes ("MySTL") manage their own memory blocks to avoid multiple alloc (new) / free (delete) calls. The objects released are enqueued on a memory block pool to be reused when needed. On that way i improve performance and protect my code against memory fragmentation.
The parts of the code that need to access lower performance system resources (like files, databases, script, network write) i use separate threads for them. But not one thread for each unit (like not 1 thread for each socket), if so the operational system would lose performance while managing a high number of threads. So you can group objects of same classes to be processed on a separate thread if possible.
For example, if you have to write data to a network socket, but the socket write buffer is full, i save the data on a sendqueue buffer (which shares memory with all sockets together) to be sent on a separate thread as soon as the sockets become writeable again. At this way your main threads should never stop processing on a blocked state waiting for the operational system frees a specific resource. All the buffers released are saved and reused when needed.
After all a profile tool would be welcome to look for program bottles and shows which algorithms should be improved.
i got succeeded using that strategy once i have servers running like 500+ days on a linux machine without rebooting, with thousands users logging everyday.
[02:01] -alpha.ip.tv- Uptime: 525days 12hrs 43mins 7secs
Is there any sort of performance difference between the arithmetic operators in c++, or do they all run equally fast? E.g. is "++" faster than "+=1"? What about "+=10000"? Does it make a significant difference if the numbers are floats instead of integers? Does "*" take appreciably longer than "+"?
I tried performing 1 billion each of "++", "+=1", and "+=10000". The strange thing is that the number of clock cycles (according to time.h) is actually counterintuitive. One might expect that if any of them are the fastest, it is "++", followed by "+=1", then "+=10000", but the data shows a slight trend in the opposite direction. The difference is more pronounced on 10 billion operations. This is all for integers.
I am dabbling in scientific computing, so I wanted to test the performance of operators. If any of the operators operated in time that was linear in terms of the inputs, for example.
About your edit, the language says nothing about the architecture it's running on. Your question is platform dependent.
That said, typically all fundamental data-type operations have a one-to-one correspondence to assembly.
x86 for example has an instruction which increments a value by 1, which i++ or i += 1 would translate into. Addition and multiplication also have single instructions.
Hardware-wise, it's fairly obvious that adding or multiplying numbers is at least linear in the number of bits in the numbers. Because the hardware has a constant number of bits, it's O(1).
Floats have their own processing unit, usually, which also has single instructions for operations.
Does it matter?
Why not write the code that does what you need it to do. If you want to add one, use ++. If you want to add a large number, add a large number. If you need floats, use floats. If you need to multiply two numbers, then multiply them.
The compiler will figure out the best way to do what you want, so instead of trying to be tricky, do what you need and let it do the hard work.
After you've written your working code, and you decide it's too slow, profile it and find out why. You'll find it's not silly things like multiplying versus adding, but rather going about the entire (sub-)problem in the wrong way.
Practically, all of the operators you listed will be done in a single CPU instruction anyway, on desktop platforms.
No, no, yes*, yes*, respectively.
* but do you really care?
EDIT: to give some kind of idea with a modern processor, you may be able to do 200 integer additions in the time it takes to make one memory access, and only 50 integer multiplications. If you think about it, you're still going to be bound by the memory accesses most of the time.
What you are asking is: What basic operations get transformed into which assembly instructions and what is the performance of those instructions on my specific architecture. And this is also your answer: The code they get translated to is dependant on your compiler and it's knowledge of your architecture, their performance depends on your architecture.
Mind you: in C++ operators can be overloaded for user defined types. They can behave differently from built-in types and the implementation of the overload can be non-trivial (no just one instruction).
Edit: A hint for testing. Most compilers support outputting the generated assembly code. The option for gcc is -S. If you use some other compiler have a look at their documentation.
The best answer is to time it with your compiler.
Look up the optimization manuals for your CPU. That's the only place you're going to find answers.
Get your compiler to output the generated assembly. Download the manuals for your CPU. Look up the instructions used by the compiler in the manual, and you know how they perform.
Of course, this presumes that you already know the basics of how a pipelined, superscalar out-of-order CPU operates, what branch prediction, instruction and data cache and everything else means. Do your homework.
Performance is a ridiculously complicated subject. Depending on context, floating-point code may be as fast as (or faster than) integer code, or it may be four times slower. Usually branches carry almost no penalty, but in special cases, they can be crippling. Sometimes, recomputing data is more efficient than caching it, and sometimes not.
Understand your programming language. Understand your compiler. Understand your CPU. And then examine exactly what the compiler is doing in your case, by profiling/timing, and on when necessary by examining the individual instructions. (and when timing your code, be aware of all the caveats and gotchas that can invalidate your benchmarks: Make sure optimizations are enabled, but also that the code you're trying to measure isn't optimized away. Take the cache into account (if the data is already in the CPU cache, it'll run much faster. If it has to read from physical memory to begin with, it'll take extra time. Both can invalidate your measurements if you're not careful. Keep in mind what you want to measure exactly)
For your specific examples, why should ++i be faster than i += 1? They do the exact same thing? Sometimes, it may make a difference whether you're adding a constant or a variable, but in this case, you're adding the constant one in both cases.
And in general, instructions take a fixed constant time regardless of their operands. adding one to something takes just as long as adding -2000 or 1772051912. The same goes for multiplication or division.
But if you care about performance, you need to understand how the entire technology stack works, not just rely on a few simple rules of thumb like "integer is faster than floating point, and ++ is faster than +=" (Apart from anything else, such simple rules of thumb are almost never true, at least not in every case)
Here is a twist on your evaluations: try Loop Unrolling. Loop unrolling is where you repeat the same statements in a loop to reduce the number of iterations in the loop.
Most modern processors hate branch instructions. The processors have a queue of pre-fetched instructions, which speeds up processing. They really hate branch instructions, because the processor has to clear out the queue and reload it after a branch. This takes more time than just processing sequential instructions.
When coding for processing time, try to minimize the number of branches, which can occur in loop constructs and decision constructs.
Depends on architecture, the built in operators for integer arithmetic translate directly to assembly (as I understand it) ++, +=1, and += 10000 are probably equally fast, multiplication would depend on the platform, overloaded operators would depend on you
Donald Knuth : "We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil"
unless you are writing extremely time critical software, you should probably worry about other things
Short answer: you should turn optimizations on before measuring.
The long answer: If you turned optimizations on, you're performing the operations on integers, and still you get different times for ++i; and i += 1;, then it's probably time to get a better compiler -- the two statements have exactly the same semantics and a competent compiler should translate them into the same instruction sequence.
"Does it make a significant difference if the numbers are floats instead of integers?"
-It depends on what kind of processor you are running on. Integer operations are faster on current x86 compatible CPUs.
About i++ and i+=1: there shouldn't be a difference with any good compiler, while you may expect i+=10000 to be slightly slower on x86 CPUs.
"Does "*" take appreciably longer than "+"?"
-Typically yes.
Note that you may run into all sorts of bottlenecks, in which case the speed difference between the operations doesn't show up. Eg. memory bandwidth, CPU pipeline stall due to data dependencies, etc...
The performance problems caused by C++ operators do not come from the operators and not from the operators implementation. It comes from the syntax, from hidden code being run without you knowing.
The best example, is implementing quick sort, on an object which has the operator[] implemented, but internally it's using a linked list. Now instead of O(nlogn) [1] you will get O(n^2logn).
The problem with performance is that you cannot know exactly what your code will eventually be.
[1] I know that quick sort is actually O(n^2), but it rarely gets to it, the average distribution will give you O(nlogn).