I have to exploit PpenMP in some algorithm and for this purpose I need some mathematical functions, like eig or svd as it is available in MATLAB and it is quite fast in MATLAB. I already tried the following libraries with OpenMP
GSL - GNU Scientific Library
Eigen C++ template library
but I don't know why my OpenMP parallelised code is much slower than the serial code, may be there is some thing wrong in the library, or that the function random, eig or svd are blocking? I have no idea how to figure it out, can some body suggest me which is most compatible math library with OpenMP.
I can recommend Intel's MKL; note that it costs money which may affect your decision. I neither know nor care what language(s) it is written in, just so long as it provides APIs callable from my chosen language. Mine is Fortran, but it has bindings for C too
If you look around SO you'll find many questions from people whose first (or second or third) OpenMP programs were actually slower than their serial versions. Look at some of the answers. Don't conclude that there is a magic bullet, in the shape of a library, to make your code faster. Instead, realise that it is most likely that you've written a poorly-parallelised program and fix that.
Finally, if you have an installation of Matlab, don't expect to be able to write your own routines to outperform Matlab's. I won't say it can't be done, but I think you'll find it very difficult.
GSL is compatible with OpenMP. You can try with Intel Math Kernel Library which comes as a trial version for free.
If the speed up is not so much, then probably the code is not much parallelizable. You may want to debug and see the details of the running threads in Intel Thread Checker, that could be helpful to see where the bottlenecks are.
I think you just want to find a fast implementation of lapack (or related routines) which is already threaded, but it's a little hard to tell from your question. High Performance Mark suggests MKL, which is an excellent example; others include ATLAS or FLAME which are open source but take some doing to build.
Related
I have to prove to my client that Fortran is faster than Matlab/Simulink. He is considering migrating a code from fortran to Matlab. The code is mainly logic and "procedural" subroutines. It does not use any native matrix operations or mathematical functions (eigenvalues, non linear equations, etc)
I think that the question of who is faster is already answered considering several references over the internet and the "intrinsic characteristics" of each language, but I need concrete data.
All charts that I found compare Matlab/Simulink x Fortran but do not specify if the Matlab code is compiled or not (using matlab coder toolbox). I think that it is a critical issue.
I´m not saying that compiling the code will make matlab faster than fortran, but in order to really convince someone I would like to see the results.
A good start would be:
Performance - Matlab (.m) compiled (Matlab coder toolbox) X Intel Fortran
Performance - Simulink compiled (Realtime toolbox) X Intel Fortran
Does anyone have already tested this scenario?
Matlab code that I recently "compiled" using the Matlab Coder produced a speed-up of x20 (!). The actual expected speedup depends on many things. If your Matlab code is highly vectorized and uses mainly linear-algebra routines, then the Coder is unlikely to produce much speedup. But if you have multiple loops and conditionals in your algorithm then you can indeed achieve order-of-magnitude speedup as in my example above.
Under the hood, Matlab's linear-algebra uses BLAS/LAPACK (via the MKL/ACML libraries), that use highly-optimized Fortran code. So unless you write extremely efficient Fortran, it is not likely that you will be able to outperform Matlab (despite the function-call overheads) for highly-vectorized Matlab linear-algebra/math algos. However, if your code uses conditionals/loops and similar non-math programming constructs, then the picture might change. In short, there's no simple answer - it depends on your specific algorithm/program.
Putting performance aside for the moment, Matlab has numerous other benefits over Fortran, including a vast array of tested built-in functions and enabling a rapid development cycle.
You would need to ask a more tightly defined question - there's no single answer to whether Fortran is faster than MATLAB/Simulink.
First of all, it's easy to write terrible, slow algorithms in either language. So you'd need to specify particular, well-written algorithms.
Secondly, there are many things for which MATLAB will be faster than even very well-written Fortran (or C). For example, if you want to multiply two big matrices together, or calculate some eigenvalues, or other linear algebra that is in MATLAB's sweet spot, you won't beat it. On the other hand if you're doing something with a lot more logic, that can't be vectorised, Fortran is likely to be faster (as long as it's written well).
When you introduce MATLAB Coder into the picture, these latter things are the ones that are most likely to benefit from a speedup by converting to C code (mostly because the former things really can't be sped up much, which is why you wouldn't beat them). But the speedup is variable - I've seen over 10-15x, but also sometimes only 1-2x.
You don't mention where you found the charts you have comparing MATLAB to Fortran, but if you've found them on the internet I would think it's a pretty safe assumption that they don't involve C code generation with MATLAB Coder, and represent the performance of just MATLAB.
Finally - one other method of speeding up MATLAB is to parallelize it with Parallel Computing Toolbox (which enables you to parallelize things over the cores on your local machine) and possibly also with Distributed Computing Server (parallelization on cluster). It's typically a lot easier to do this with MATLAB code than it is to speed up by using MATLAB Coder to produce C code - so if you think it's critical to consider MATLAB Coder in your comparisons, you should probably also consider this as well.
MATLAB Compiler will not make your code faster, it is intended for distributing your code to third party users that do not have MATLAB. You need to provide, along with your compiled code, the MCR or MATLAB Component Runtime, which is essentially a headless version of MATLAB, and which you can distribute freely if you have a license of MATLAB Compiler.
Now, if you use MATLAB Coder (or Simulink Coder for Simulink) to generate C code from your MATLAB code, then it is likely that you will get a speed up compared to interpreted MATLAB code. Even then, that depends on the code in question. Also, this only supports a subset of the MATLAB language, that is compatible with C code generation.
I have an application that requires millions of subtractions and remainders, i originally programmed this algorithm inside of C#.Net but it takes five minutes to process this information and i need it faster than that.
I have considered perl and that seems to be the best alternative now. Vb.net was slower in testing. C++ may be better also. Any advice would be greatly appreciated.
You need a compiled language like Fortran, C, or C++. Other languages are designed to give you flexibility, object-orientation, or other advantages, and assume absolutely fastest performance is not your highest priority.
Know how to get maximum performance out of a single thread, and after you have done so investigate sharing the work across multiple cores, for example with MPI. To get maximum performance in a single thread, one thing I do is single-step it at the machine instruction level, to make sure it's not dawdling about in stuff that could be removed.
Some calculations are regular enough to take profit of GPGPUs: recent graphic cards are essentially specialized massively parallel numerical co-processors. For instance, you could code your numerical kernels in OpenCL. Otherwise, learn C++11 (not some earlier version of the C++ standard) or C. And in many cases Ocaml could be nearly as fast as C++ but much easier to code with.
Perhaps your problem can be handled by scilab or R, I did not understand it enough to help more.
And you might take advantage of your multi-core processor by e.g. using Pthreads or MPI
At last, the Linux operating system is perhaps better to deal with massive calculations. It is significant that most super computers use it today.
If execution speed is the highest priority, that usually means Fortran.
Try Julia: its killing feature is being easy to code in a high level concise way, while keeping performances at the same order of magnitude of Fortran/C.
PARI/GP is the best I have used so far. It's written in C.
Try to look at DMelt mathematical program. The program calls Java libraries. Java virtual machine can optimize long mathematical calculations for you.
The standard tool for mathmatic numerical operations in engineering is often Matlab (or as free alternatives octave or the already mentioned scilab).
Here is a good linear solver named GotoBLAS. It is available for download and runs on most computing platforms. My question is, is there an easy way to link this solver with the Mathematica kernel, so that we can call it like LinearSolve? One thing most of you may agree on for sure is that if we have a very large Linear system then we better get it solved by some industry standard Linear solver. The inbuilt solver is not meant for really large problems.
Now that Mathematica 8 has come up with better compilation and library link capabilities we can expect to use some of those solvers from within Mathematica. The question is does that require little tuning of the source code, or you need to be an advanced wizard to do it. Here in this forum we may start linking some excellent open source programs like GotoBLAS with Mathematica and exchange our views. Less experienced people can get some insight from the pro users and at the end we get a much stronger Mathematica. It will be an open project for the ever increasing Mathematica community and a platform where these newly introduced capabilities of Mathematica 8 could be transparently documented for future users.
I hope some of you here will give solid ideas on how we can get GotoBLAS running from within Mathematica. As the newer compilation and library link capabilities are usually not very well documented, they are not used by the common users very often. This question can act as a toy example to document these new capabilities of Mathematica. Help in this direction by the experienced forum members will really lift the motivation of new users like me as well as it will teach us a very useful thing to extend Mathematica's number crunching arsenal.
The short answer, I think, is that this is not something you really want to do.
GotoBLAS, as I understand it, is a specific implementation of BLAS, which stands for Basic Linear Algebra Subroutines. "Basic" really means quite basic here - multiply a matrix times a vector, for example. Thus, BLAS is not a solver that a function like LinearSolve would call. LinearSolve would (depending on the exact form of the arguments) call a LAPACK command, which is a higher level package built on top of BLAS. Thus, to really link GotoBLAS (or any BLAS) into Mathematica, one would really need to recompile the whole kernel.
Of course, one could write a C/Fortran program that was compiled against GotoBLAS and then link that into Mathematica. The resulting program would only use GotoBLAS when running whatever specific commands you've linked into Mathematica, however, which rather misses the whole point of BLAS.
The Wolfram Kernel (Mathematica) is already linked to the highly-optimized Intel Math Kernel Library, and is distributed with Mathematica. The MKL is multithreaded and vectorized, so I'm not sure what GotoBLAS would improve upon.
Forgive me if this is a silly question, but I'm wondering if/how LLVM could be used to obtain a higher performance Z-Machine VM for interactive fiction. (If it could be used, I'm just looking for some high-level ideas or suggestions, not a detailed solution.)
It might seem odd to desire higher performance for a circa-1978 technology, but apparently Z-Machine games produced by the modern Inform 7 IDE can have performance issues due to the huge number of rules that need to be evaluated with each turn.
Thanks!
FYI: The Z-machine architecture was reverse-engineered by Graham Nelson and is documented at http://www.inform-fiction.org/zmachine/standards/z1point0/overview.html
Yes, it could be. A naïve port of the interpreter to the a compiler could be done relatively easily.
That said, it wouldn't be a big performance win. The problem with any compiler for ZCode or Glulx is that they're both relatively low-level. For instance, Glulx supports indirect jumps and self-modifying code. There's no way to statically compile that into efficient native code. Making it truly fast would require a trace compilation or something similar.
It would certainly be possible (but difficult) to use LLVM as a kind of JIT for Z-machine code, but wouldn't it be easier to simply compile the Inform source directly to a faster language? Eg, C for maximum speed, or .NET or Java if you prefer portability. I would suspect this route would be a lot easier, and better performing, than just jerry-rigging a JIT onto the side of the interpreter.
Fortran's performances on Computer Language Benchmark Game are surprisingly bad. Today's result puts Fortran 14th and 11th on the two quad-core tests, 7th and 10th on the single cores.
Now, I know benchmarks are never perfect, but still, Fortran was (is?) often considered THE language for high performance computing and it seems like the type of problems used in this benchmark should be to Fortran's advantage. In an recent article on computational physics, Landau (2008) wrote:
However, [Java] is not as efficient or
as well supported for HPC and parallel
processing as are FORTRAN and C, the
latter two having highly developed
compilers and many more scientific
subroutine libraries available.
FORTRAN, in turn, is still the
dominant language for HPC, with
FORTRAN 90/95 being a surprisingly
nice, modern, and effective language;
but alas, it is hardly taught by any
CS departments, and compilers can be
expensive.
Is it only because of the compiler used by the language shootout (Intel's free compiler for Linux) ?
No, this isn't just because of the compiler.
What benchmarks like this -- where the program differs from benchmark to benchmark -- is largely the amount of effort (and quality of effort) that the programmer put into writing any given program. I suspect that Fortran is at a significant disadvantage in that particular metric -- unlike C and C++, the pool of programmers who'd want to try their hand at making the benchmark program better is pretty small, and unlike most anything else, they likely don't feel like they have something to prove either. So, there's no motivation for someone to spend a few days poring over generated assembly code and profiling the program to make it go faster.
This is fairly clear from the results that were obtained. In general, with sufficient programming effort and a decent compiler, neither C, C++, nor Fortran will be significantly slower than assembly code -- certainly not more than 5-10%, at worst, except for pathological cases. The fact that the actual results obtained here are more variant than that indicates to me that "sufficient programming effort" has not been expended.
There are exceptions when you allow the assembly to use vector instructions, but don't allow the C/C++/Fortran to use corresponding compiler intrinsics -- automatic vectorization is not even a close approximation of perfect and probably never will be. I don't know how much those are likely to apply here.
Similarly, an exception is in things like string handling, where you depend heavily on the runtime library (which may be of varying quality; Fortran is rarely a case where a fast string library will make money for the compiler vendor!), and on the basic definition of a "string" and how that's represented in memory.
Some random thoughts:
Fortran used to do very well because it was easier to identify loop invariants which made some optimizations easier for the compiler. Since then
Compilers have gotten much more sophisticated. Enormous effort has been put into c and c++ compilers in particular. Have the fortran compilers kept up? I suppose the gfortran uses the same back end of gcc and g++, but what of the intel compiler? It used to be good, but is it still?
Some languages have gotten a lot specialized keywords and syntax to help the compiler (restricted and const int const *p in c, and inline in c++). Not knowing fortran 90 or 95 I can't say if these have kept pace.
I've looked at these tests. It's not like the compiler is wrong or something. In most tests Fortran is comparable to C++ except some where it gets beaten by a factor of 10. These tests just reflect what one should know from the beggining - that Fortran is simply NOT an all-around interoperable programming language - it is suited for efficient computation, has good list operations & stuff but for example IO sucks unless you are doing it with specific Fortran-like methods - like e.g. 'unformatted' IO.
Let me give you an example - the 'reverse-complement' program that is supposed to read a large (of order of 10^8 B) file from stdin line-by-line, does something with it & prints the resulting large file to stdout. The pretty straighforward Fortran program is about 10 times slower on a single core (~10s) than a HEAVILY optimized C++ (~1s). When you try to play with the program, you'll see that only simple formatted read & write take more than 8 seconds. In a Fortran way, if you care for efficiency, you'd just write an unformatted structure to a file & read it back in no time (which is totally non-portable & stuff but who cares anyway - an efficient code is supposed to be fast & optimized for a specific machine, not able to run everywhere).
So the short answer is - don't worry, just do your job - and if you want to write a super-efficient operating system, than sorry - Fortran is just not the way for that kind of performance.
This benchmark is stupid at all.
For example, they measure CPU-time for the whole program to run. As mcmint stated (and it might be actually true) Fortran I/O sucks*. But who cares? In real-world tasks one read input for some seconds than do calculations for hours/days/months and finally write output for the seconds. Thats why in most benchmarks I/O operations are excluded from time measurements (if you of course do not benchmark I/O by itself).
Norber Wiener in his book God & Golem, Inc. wrote
Render unto man the things which are man’s and unto the computer the things which are the computer’s.
In my opinion the usage of this principle while implementing algorithm in any programming language means:
Write as readable and simple code as you can and let compiler do the optimizations.
Especially it is important in real-world (huge) applications. Dirty tricks (so heavily used in many benchmarks) even if they might improve the efficiency to some extent (5%, maybe 10%) are not for the real-world projects.
/* C/C++ uses stream I/O, but Fortran traditionally uses record-based I/O. Further reading. Anyway I/O in that benchmarks are so surprising. The usage of stdin/stdout redirection might also be the source of problem. Why not simply use the ability of reading/writing files provided by the language or standard library? Once again this woud be more real-world situation.
I would like to say that even if the benchmark do not bring up the best results for FORTRAN, this language will still be used and for a long time. Reasons of use are not just performance but also some kind of thing called easyness of programmability. Lots of people that learnt to use it in the 60's and 70's are now too old for getting into new stuff and they know how to use FORTRAN pretty well. I mean, there are a lot of human factors for a language to be used. The programmer also matters.
Considering they did not publish the exact compiler options they used for the Intel Fortran Compiler, I have little faith in their benchmark.
I would also remark that both Intel's math library, MKL, and AMD's math library, ACML, use the Intel Fortran Compiler.
Edit:
I did find the compilation options when you click on the benchmark's name. The result is surprising since the optimization level seems reasonable. It may come down to the efficiency of the algorithm.