Say I am writing a modular arithmetic type, after each arithmetic operation a mod P operation (% P) is applied.
I would like the new type to be usable with STL's algorithms, as well as execution policies.
It seems that the par policy should work, but what about unseq vectorization? Is it possible to integrate vectorization capabilities of the standard library with a custom numeric type?
How would I add the numeric traits to the custom type?
BTW, I know that there are modular arithmetic libraries. This is an exercise with modern C++.
You're still going to rely on template instantiation followed by compilation. unseq vectorization should come from the compiler noticing that there can't be aliasing, allowing it to choose SIMD instructions. That means you're not writing the SIMD instructions manually.
However, you should give the compiler a fair chance to see all the operations together. If your operator+ is only seen by the linker, it would be compiled as a scalar operation . That means you'll need to make all the operations inline so the compiler can combine multiple calls.
I want to do x = a + b + c on ~2048 bit signed integers. Currently my code looks like
mpz_add(x, a, b);
mpz_add(x, x, c);
Is there a single function to do this? This happens many many times in my application. I have profiled my code and the 3-way add step is taking up a significant portion of the runtime. If there's an alternative way to do this in a single pass that might help.
I've used MPFR extensively and I've looked through nearly every part of the documentation. I'm almost certain that nothing like this exists in MPFR and as such, I'm almost certain nothing like this exists in GMP.
One solution could be to switch to MPFR and use Pavel Holoborodko's MPFR C++ which adds operators for MPFR functions. I can't imagine that this would help performance (although it probably wouldn't affect it much), it's under a GPL, and you'd have to install another library, but it would combine the operations.
I don't know of any fast algorithms that when adding three numbers don't just add two of them and then add the last number in behind the scenes. I don't think that combining these two operations into one operation using any library in any language would help performance. Arbritrary precision is just slow, even using GNU MP. I've got a comparison of the speeds here on Code Review if that helps.
Calling twice the functions won't incur in a noticeable overhead. However, the reallocation of x, if needed, can be very slow. To avoid this, you can measure the sizes of a, b and c and make sure x has allocated the maximum size of the three numbers plus 2 (1 bit for addition, in the worst case). You can use
mpz_init2(x, maxsize+2);
or, if x was already initialized,
if (mpz_sizeinbase(x, 2) < maxsize+2)
mpz_realloc2(x, maxsize+2);
I am starting with dsp programming right now and am writing my first low level classes and functions.
Since I want the functions to be fast (or at last not inefficient), I often wonder what I should use and what I should avoid in functions which get called per sample.
I know that the speed of an instruction varies quite a bit but I think that some of you at least can share a rule of thumb or just experience. :)
conditional statements
If I have to use conditions, switch should be faster than an if / else if block, right?
Are there differences between using two if-statements or an if-else? Somewhere I read that else should be avoided but I don't know why.
Also, compared to a multiplication, is there a rude estimation how much more time an if-block takes? Because in some cases, using multiplications by zero could be used instead of if-statements:
//something could be an int either 1 or 0:
if(something) {
signal += something_else;
}
// or:
signa+ += something*something_else;
functions and function-pointers
Instead of using conditional statements, you could use function-pointer. Instead of using conditions in every call, the pointer could be redirected to a specific function. However, for every call, the pointer had to be interpreted in order to call the right function. So I don't know if this would help or not.
What I also wonder is if calling functions have an impact. If so, boxing functions should be avoided, right?
variables
I would think that defining and using many variables in a function doesn't realy have an impact, at least relative to calculations. Is this true? If not, reusing declared variables would be better than more declaration.
calculations
Is there an order of calculation-types in term of the time they take to execute? I am sure that this highly depends on the context but a rule of thumb would be nice. I often read that people only count the multiplication in an algorithm. Is this because additions are realtively fast?
Does it make a difference between multiplication and division? (*0.5 or /2.0)
I hope that you can share soem experience.
Cheers
here are part of the answers:
calculations (talking about native precision of the processor for example 32bits):
Most DSP microprocessors have single cycle multipliers, that means a
multiply costs exactly the same as an addition in term of cycles.
and multiplication it generally faster then division.
conditional statements:
if/else - when looking in the assembly code you can see that the memory of the if condition is usually loaded by default, so when using if else make sure that the condition that will happen more frequently will be in the if.
but generally if possible you should avoid if/else in a loop to improve the pipe lining.
good luck.
DSP compilers are typically good at optimizing for loops that do not contain function-calls.
Therefore, try to inline every function that you call from within a time-critical for loop.
If your DSP is a fixed-point processor, then floating-point operations are implemented by SW.
This means that every such operation is essentially replaced by the compiler with a library function.
So you should basically avoid performing floating-point operations inside time-critical for loops.
The preprocessor should provide a special #pragma for the number of iterations of a for loop:
Minimum number of iterations
Maximum number of iterations
Multiplicity of the number of iterations
Use this #pragma where possible, in order to help the compiler to perform loop-unrolling where possible.
Finally, DSPs usually support a set of unique operations for enhanced performance.
As an example, consider _dotpu4 on Texas Instruments C64xx, which computes the scalar-product of two integers src1 and src2: For each pair of 8-bit values in src1 and src2, the 8-bit value from src1 is multiplied with the 8-bit value from src2, and the four products are summed together.
Check the data-sheet of your DSP, and see if you can make use of any of these operations.
The compiler should generate an intermediate file, which you can explore in order to analyze the expected performance of each of the optimized for loops in your code.
Based on that, you can try different assembly operations that might yield better results.
I'm using LPSolve IDE to solve a LP problem. I have to test the model against about 10 or 20 sets of different parameters and compare them.
Is there any way for me to keep the general model, but to specify the constants as I wish? For example, if I have the following constraint:
A >= [c]*B
I want to test how the model behaves when [c] = 10, [c] = 20, and so on. For now, I'm simply preparing different .lp files via search&replace, but:
a) it doesn't seem too efficient
b) at some point, I need to consider the constraint of the form A >= B/[c] // =(1/[c]*B). It seems, however, that LPSolve doesn't recogize the division operator. Is specifying 1/[c] directly each time the only option?
It is not completely clear what format you use with lp_solve. With the cplex lp format for example, there is no better way: you cannot use division for the coefficient (or even multiplication for that matter) and there is no function to 'include' another file or introduce a symbolic names for a parameter. It is a very simple language, and not suitable for any complex task.
There are several solutions for your problem; it depends if you are interested in something fast to implement, or 'clean', reusable and with a short runtime (of course this is a compromise).
You have the possibility to generate your lp files from another language, e.g. python, bash, etc. This is a 'quick and dirty' solution: very slow at runtime, but probably the faster to implement.
As every lp solver I know, lp_solve comes with several modelling interfaces: you can for example use the GNU mp format instead of the current one. It recognizes multiplication, divisions, conditionals, etc. (everything you are looking for, see the section 3.1 'numeric expressions')
Finally, you have the possibility to use directly the lp_solve interface from another programming language (e.g. C) which will be the most flexible option, but it may require a little bit more work.
See the lp_solve documentation for more details on the supported input formats and the API reference.
I am writing a C++ number crunching application, where the bottleneck is a function that has to calculate for double:
template<class T> inline T sqr(const T& x){return x*x;}
and another one that calculates
Base dist2(const Point& p) const
{ return sqr(x-p.x) + sqr(y-p.y) + sqr(z-p.z); }
These operations take 80% of the computation time. I wonder if you can suggest approaches to make it faster, even if there is some sort of accuracy loss
Thanks
First, make sure dist2 can be inlined (it's not clear from your post whether or not this is the case), having it defined in a header file if necessary (generally you'll need to do this - but if your compiler generates code at link time, then that's not necessarily the case).
Assuming x86 architecture, be sure to allow your compiler to generate code using SSE2 instructions (an example of an SIMD instruction set) if they are available on the target architecture. To give the compiler the best opportunity to optimize these, you can try to batch your sqr operations together (SSE2 instructions should be able to do up to 4 float or 2 double operations at a time depending on the instruction.. but of course it can only do this if you have the inputs to more than one operation on the ready). I wouldn't be too optimistic about the compiler's ability to figure out that it can batch them.. but you can at least set up your code so that it would be possible in theory.
If you're still not satisfied with the speed and you don't trust that your compiler is doing it best, you should look into using compiler intrinsics which will allow you to write potential parallel instructions explicitly.. or alternatively, you can go right ahead and write architecture-specific assembly code to take advantage of SSE2 or whichever instructions are most appropriate on your architecture. (Warning: if you hand-code the assembly, either take extra care that it still gets inlined, or make it into a large batch operation)
To take it even further, (and as glowcoder has already mentioned) you could perform these operations on a GPU. For your specific case, bear in mind that GPU's often don't support double precision floating point.. though if it's a good fit for what you're doing, you'll get orders of magnitude better performance this way. Google for GPGPU or whatnot and see what's best for you.
What is Base?
Is it a class with a non-explicit constructor? It's possible that you're creating a fair amount of temporary Base objects. That could be a big CPU hog.
template<class T> inline T sqr(const T& x){return x*x;}
Base dist2(const Point& p) const {
return sqr(x-p.x) + sqr(y-p.y) + sqr(z-p.z);
}
If p's member variables are of type Base, you could be calling sqr on Base objects, which will be creating temporaries for the subtracted coordinates, in sqr, and then for each added component.
(We can't tell without the class definitions)
You could probably speed it up by forcing the sqr calls to be on primitves and not using Base until you get to the return type of dist2.
Other performance improvement opportunities are to:
Use non-floating point operations, if you're ok with less precision.
Use algorithms which don't need to call dist2 so much, possibly caching or using the transitive property.
(this is probably obvious, but) Make sure you're compiling with optimization turned on.
I think optimising these functions might be difficult, you might be better off optimising the code that calls these functions to call them less, or to do things differently.
You don't say whether the calls to dist2 can be parallelised or not. If they can, then you could build a thread pool and split this work up into smaller chunks per thread.
What does your profiler tell you is happening inside dist2. Are you actually using 100% CPU all the time or are you cache missing and waiting for data to load?
To be honest, we really need more details to give you a definitive answer.
If sqr() is being used only on primitive types, you might try taking the argument by value instead of reference. That would save you an indirection.
If you can organise your data suitably then you may well be able to use SIMD optimisation here. For an efficient implementation you would probably want to pad your Point struct so that it has 4 elements (i.e. add a fourth dummy element for padding).
If you have a number of these to do, and you're doing graphics or "graphic like" tasks (thermal modeling, almost any 3d modeling) you might consider using OpenGL and offloading the tasks to a GPU. This would allow the computations to run in parallel, with highly optimized operational capacity. After all, you would expect something like distance or distancesq to have its own opcode on a GPU.
A researcher at a local univeristy offload almost all of his 3d-calculations for AI work to the GPU and achieved much faster results.
There are a lot of answers mentioning SSE already… but since nobody has mentioned how to use it, I'll throw another in…
Your code has most everything a vectorizer needs to work, except two constraints: aliasing and alignment.
Aliasing is the problem of two names referring two the same object. For example, my_point.dist2( my_point ) would operate on two copies of my_point. This messes with the vectorizer.
C99 defines the keyword restrict for pointers to specify that the referenced object is referenced uniquely: there will be no other restrict pointer to that object in the current scope. Most decent C++ compilers implement C99 as well, and import this feature somehow.
GCC calls it __restrict__. It may be applied to references or this.
MSVC calls it __restrict. I'd be surprised if support were any different from GCC.
(It is not in C++0x, though.)
#ifdef __GCC__
#define restrict __restrict__
#elif defined _MSC_VER
#define restrict __restrict
#endif
Base dist2(const Point& restrict p) const restrict
Most SIMD units require alignment to the size of the vector. C++ and C99 leave alignment implementation-defined, but C++0x wins this race by introducing [[align(16)]]. As that's still a bit in the future, you probably want your compiler's semi-portable support, a la restrict:
#ifdef __GCC__
#define align16 __attribute__((aligned (16)))
#elif defined _MSC_VER
#define align16 __declspec(align (16))
#endif
struct Point {
double align16 xyz[ 3 ]; // separate x,y,z might work; dunno
…
};
This isn't guaranteed to produce results; both GCC and MSVC implement helpful feedback to tell you what wasn't vectorized and why. Google your vectorizer to learn more.
If you really need all the dist2 values, then you have to compute them. It's already low level and cannot imagine speedups apart from distributing on multiple cores.
On the other side, if you're searching for closeness, then you can supply to the dist2() function your current miminum value. This way, if sqr(x-p.x) is already larger than your current minimum, you can avoid computing the remaining 2 squares.
Furthermore, you can avoid the first square by going deeper in the double representation. Comparing directly on the exponent value with your current miminum can save even more cycles.
Are you using Visual Studio? If so you may want to look at specifying the floating point unit control using /fp fast as a compile switch. Have a look at The fp:fast Mode for Floating-Point Semantics. GCC has a host of -fOPTION floating point optimisations you might want to consider (if, as you say, accuracy is not a huge concern).
I suggest two techniques:
Move the structure members into
local variables at the beginning.
Perform like operations together.
These techniques may not make a difference, but they are worth trying. Before making any changes, print the assembly language first. This will give you a baseline for comparison.
Here's the code:
Base dist2(const Point& p) const
{
// Load the cache with data values.
register x1 = p.x;
register y1 = p.y;
register z1 = p.z;
// Perform subtraction together
x1 = x - x1;
y1 = y - y1;
z1 = z - z2;
// Perform multiplication together
x1 *= x1;
y1 *= y1;
z1 *= z1;
// Perform final sum
x1 += y1;
x1 += z1;
// Return the final value
return x1;
}
The other alternative is to group by dimension. For example, perform all 'X' operations first, then Y and followed by Z. This may show the compiler that pieces are independent and it can delegate to another core or processor.
If you can't get any more performance out of this function, you should look elsewhere as other people have suggested. Also read up on Data Driven Design. There are examples where reorganizing the loading of data can speed up performance over 25%.
Also, you may want to investigate using other processors in the system. For example, the BOINC Project can delegate calculations to a graphics processor.
Hope this helps.
From an operation count, I don't see how this can be sped up without delving into hardware optimizations (like SSE) as others have pointed out. An alternative is to use a different norm, like the 1-norm is just the sum of the absolute values of the terms. Then no multiplications are necessary. However, this changes the underlying geometry of your space by rearranging the apparent spacing of the objects, but it may not matter for your application.
Floating point operations are quite often slower, maybe you can think about modifying the code to use only integer arithmetic and see if this helps?
EDIT: After the point made by Paul R I reworded my advice not to claim that floating point operations are always slower. Thanks.
Your best hope is to double-check that every dist2 call is actually needed: maybe the algorithm that calls it can be refactored to be more efficient? If some distances are computed multiple times, maybe they can be cached?
If you're sure all of the calls are necessary, you may be able to squeeze out a last drop of performance by using an architecture-aware compiler. I've had good results using Intel's compiler on x86s, for instance.
Just a few thoughts, however unlikely that I will add anything of value after 18 answers :)
If you are spending 80% time in these two functions I can imagine two typical scenarios:
Your algorithm is at least polynomial
As your data seem to be spatial maybe you can bring the O(n) down by introducing spatial indexes?
You are looping over certain set
If this set comes either from data on disk (sorted?) or from loop there might be possibility to cache, or use previous computations to calculate sqrt faster.
Also regarding the cache, you should define the required precision (and the input range) - maybe some sort of lookup/cache can be used?
(scratch that!!! sqr != sqrt )
See if the "Fast sqrt" is applicable in your case :
http://en.wikipedia.org/wiki/Fast_inverse_square_root
Look at the context. There's nothing you can do to optimize an operation as simple as x*x.
Instead you should look at a higher level: where is the function called from? How often? Why? Can you reduce the number of calls? Can you use SIMD instructions to perform the multiplication on multiple elements at a time?
Can you perhaps offload entire parts of the algorithm to the GPU?
Is the function defined so that it can be inlined? (basically, is its definition visible at the call sites)
Is the result needed immediately after the computation? If so, the latency of FP operations might hurt you. Try to arrange your code so dependency chains are broken up or interleaved with unrelated instructions.
And of course, examine the generated assembly and see if it's what you expect.
Is there a reason you are implementing your own sqr operator?
Have you tried the one in libm it should be highly optimized.
The first thing that occurs to me is memoization ( on-the-fly caching of function calls ), but both sqr and dist2 it would seem like they are too low level for the overhead associated with memoization to be made up for in savings due to memoization. However at a higher level, you may find it may work well for you.
I think a more detailed analysis of you data is called for. Saying that most of the time in the program is spent executing MOV and JUMp commands may be accurate, but it's not going to help yhou optimise much. The information is too low level. For example, if you know that integer arguments are good enough for dist2, and the values are between 0 and 9, then a pre-cached tabled would be 1000 elements--not to big. You can always use code to generate it.
Have you unrolled loops? Broken down matrix opration? Looked for places where you can get by with table lookup instead of actual calculation.
Most drastic would be to adopt the techniques described in:
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.115.8660&rep=rep1&type=pdf
though it is admittedly a hard read and you should get some help from someone who knows Common Lisp if you don't.
I'm curious why you made this a template when you said the computation is done using doubles?
Why not write a standard method, function, or just 'x * x' ?
If your inputs can be predictably constrained and you really need speed create an array that contains all the outputs your function can produce. Use the input as the index into the array (A sparse hash). A function evaluation then becomes a comparison (to test for array bounds), an addition, and a memory reference. It won't get a lot faster than that.
See the SUBPD, MULPD and DPPD instructions. (DPPD required SSE4)
Depends on your code, but in some cases a stucture-of-arrays layout might be more friendly to vectorization than an array-of-structures layout.