We need to numerically minimize a function that takes a three dimensional vector as input. The function is smooth, so a gradient algorithm would be a good choice.
However, I am used to GSL which unfortunately requires gcc. We have to work on Windows using VC++ 2010, though. I found a GSL Port for windows, but the last commit is from 2006 and I doubt that it will work with our setup.
Which libraries are there on windows and are recommended? We only have to solve this one problem, so the more specialized the library is, the better.
We are using C++, so there should be a C or C++ interface available.
This implementation of the L-BFGS method seems to suit your puprposes. It has also a VS2010 solution file, which makes it easy to include it into your project.
Cygwin has GSL. Depending on the nature of your restriction to Windows, perhaps that is feasible.
Related
In other programming languages like Julia, we can define a Gurobi environment object first (i.e., Gurobi.Env() in Julia) and then use them again in solving linear programs.
However, I am not aware of such a feature in R. It seems that I need to pull the Gurobi license every time when I call the gurobi command in R. This cause some troubles when I am running things in parallel on the server because it seems that the Gurobi license is not released immediately after a linear program is solved.
May I know is there a way to define a Gurobi environment object in R first and then use them again? Or is there any ways to release the Gurobi license after running a linear program in R?
Any thoughts are appreciated, thanks!
Both the R and the Matlab interfaces of Gurobi do not work as the other object-oriented APIs like Python, C++, or Java. You can only define/build the model and solve it in one go. You would need to use a different API to have more fine-grained control of the environment and the model.
I recently looked into the usage of GPU computation, where the usage of package seemed to be confusing.
For example, CuArrays and ArrayFire seemed to be doing the same thing, where ArrayFire seemed to be the "official" package on Nvidia developers' webpage.(https://devblogs.nvidia.com/gpu-computing-julia-programming-language )
Also, there were CUDAdrv and CUDAnative Packages..., which seemed to be confusing, as their functionality seemed to be not as straightforward as the others.
What does these packages do? Is there any difference between CuArrays and ArrayFire?
As explained in the blog post you shared, it is quite simply as given below
The Julia package ecosystem already contains quite a few GPU-related
packages, targeting different levels of abstraction as Figure 1 shows.
At the highest abstraction level, domain-specific packages like
MXNet.jl and TensorFlow.jl can transparently use the GPUs in your
system. More generic development is possible with ArrayFire.jl, and if
you need a specialized CUDA implementation of a linear algebra or deep
neural network algorithm you can use vendor-specific packages like
cuBLAS.jl or cuDNN.jl. All these packages are essentially wrappers
around native libraries, making use of Julia’s foreign function
interfaces (FFI) to call into the library’s API with minimal overhead.
CUDAdrv and CUDAnative packages are meant for directly using CUDA runtime API and writing kernels from Julia itself. I believe that is where CuArray come in handy - wrapping native Julia objects into CUDA accessible format, roughly speaking.
ArrayFire on the other hand is a generic library that wraps around all(cuBLAS, cuSparse, cuSolve, cuFFT) CUDA provided domain specific libraries into nice interface(functions). Apart from the interface to CUDA's domain specific libraries, ArrayFire by itself provides lot of other functions in the areas of statistics, image processing, computer vision etc. It has nice JIT feature where user's code is compiled to a runtime kernel - simply put. ArrayFire.jl is an language binding with some extra Julia specific improvements at wrapper level.
That's the general difference. From a developers perspective, using a library(like ArrayFire) basically takes out the burden of keeping up with CUDA API and maintaining/tweaking the kernels for optimum performance which I think takes lot of time.
PS. I am a member of ArrayFire development team.
I have written a piece of R-code that performs a numerical computation. Now, I want to implement it into a nice GUI. I know that there are some R-packages, that allow to create GUIs from within R (e.g. gWidgets, RGtk2, ...). However, they seem to be rather limited in the capabilities and complicate to build. So I thought about going the other way round and writing a windowed-program that incorporates my R-code.
Is it possible to write a nice GUI (for example in Visual Basic.NET or Java) that allows to gather some user inputs, call the R-computations and display the results?
I ask for Visual Basic because there is this new R-Open that comes along with Visual Studio which makes me think the two must offer natural ways of collaboration with each other. I also hope that I would be able to compile an exe with it in the end.
Thank you very much for you Help!
Bernd
You can embed R in C++ code. There are examples in the R source code and documentation.
Very briefly, you'll need to build a shared DLL version of R (i.e with the --enable-R-shlib option) from the source code, using the Windows Tools. This is how GUIs like RStudio function.
The R Admin manuals have detailed instructions. The RInside package might make this a bit easier.
With the shared DLL you could probably embed R in other languages (it works for R in Python).
I would want to compile existing software into presentation that can later be run on different architectures (and OS).
For that I need a (byte)code that can be easily run/emulated on another arch/OS (LLVM IR? Some RISC assemby?)
Some random ideas:
Compiling into JVM bytecode and running with java. Too restricting? C-compilers available?
MS CIL. C-Compilers available?
LLVM? Can Intermediate representation be run later?
Compiling into RISC arch such as MMIX. What about system calls?
Then there is the system call mapping thing, but e.g. BSD have system call translation layers.
Are there any already working systems that compile C/C++ into something that can later be run with an interpreter on another architecture?
Edit
Could I compile existing unix software into not-so-lowlevel binary, which could be "emulated" more easily than running full x86 emulator? Something more like JVM than XEN HVM.
There are several C to JVM compilers listed on Wikipedia's JVM page. I've never tried any of them, but they sound like an interesting exercise to build.
Because of its close association with the Java language, the JVM performs the strict runtime checks mandated by the Java specification. That requires C to bytecode compilers to provide their own "lax machine abstraction", for instance producing compiled code that uses a Java array to represent main memory (so pointers can be compiled to integers), and linking the C library to a centralized Java class that emulates system calls. Most or all of the compilers listed below use a similar approach.
C compiled to LLVM bit code is not platform independent. Have a look at Google portable native client, they are trying to address that.
Adobe has alchemy which will let you compile C to flash.
There are C to Java or even JavaScript compilers. However, due to differences in memory management, they aren't very usable.
Web Assembly is trying to address that now by creating a standard bytecode format for the web, but unlike the JVM bytecode, Web Assembly is more low level, working at the abstraction level of C/C++, and not Java, so it's more like what's typically called an "assembly language", which is what C/C++ code is normally compiled to.
LLVM is not a good solution for this problem. As beautiful as LLVM IR is, it is by no means machine independent, nor was it intended to be. It is very easy, and indeed necessary in some languages, to generate target dependent LLVM IR: sizeof(void*), for example, will be 4 or 8 or whatever when compiled into IR.
LLVM also does nothing to provide OS independence.
One interesting possibility might be QEMU. You could compile a program for a particular architecture and then use QEMU user space emulation to run it on different architectures. Unfortunately, this might solve the target machine problem, but doesn't solve the OS problem: QEMU Linux user mode emulation only works on Linux systems.
JVM is probably your best bet for both target and OS independence if you want to distribute binaries.
As Ankur mentions, C++/CLI may be a solution. You can use Mono to run it on Linux, as long as it has no native bits. But unless you already have a code base you are trying to port at minimal cost, maybe using it would be counter productive. If it makes sense in your situation, you should go with Java or C#.
Most people who go with C++ do it for performance reasons, but unless you play with very low level stuff, you'll be done coding earlier in a higher level language. This in turn gives you the time to optimize so that by the time you would have been done in C++, you'll have an even faster version in whatever higher level language you choose to use.
The real problem is that C and C++ are not architecture independent languages. You can write things that are reasonably portable in them, but the compiler also hardcodes aspects of the machine via your code. Think about, for example, sizeof(long). Also, as Richard mentions, there's no OS independence. So unless the libraries you use happen to have the same conventions and exist on multiple platforms then it you wouldn't be able to run the application.
Your best bet would be to write your code in a more portable language, or provide binaries for the platforms you care about.
I am looking for a JIT compiler or a small compiler library that can be embedded in my program. I indent to use it to compile dynamically generated code that perform complex number arithmetics. The generated code are very simple in structure: no loops, no conditionals, but they can be quite long (a few MB when compiled by GCC). The performance of the resulting machine code is important, while I don't really care about the speed of compilation itself. Which JIT compiler is best for my purpose? Thanks!
Detailed requirements
Support double precision complex number arithmetics
Support basic optimization
Support many CPUs (x86 and x86-64 at least)
Make use of SSE on supported CPUs
Support stack or a large set of registers for local variables
ANSI-C or C++ interface
Cross platform (mainly Linux, Unix)
You might want to take a look at LLVM.
Cint is a c++(ish) environment that offers the ability to mix compiled code and interpreted code. There is a set of optimization tools for the interpreter. ROOT extends this even further by supporting compile and link at run-time at run-time (see the last section of http://root.cern.ch/drupal/content/cint-prompt), though it appears to use the system compiler and thus may not help. All the code is open source.
I make regular use of all these features as part of my work.
I don't know if it makes active use of SIMD instructions, but it seems to meet all your other requirements.
As I see that you are currently using the compile to dynamic library at link on the fly methond, you might consider TCC, though I don't believe that it does much optimization and suspect that it does not support SIMD.
Sounds like you want to be able to compile on the fly and then dynamically load the compiled library (.DLL or .so). This would give you the best performance, with an ANSI-C or C++ interface. So, forget about JITing and consider spawning a C/C++ compiler to do the compilation.
This of course assumes that a compiler can be installed at the point where the dynamically generated code is actually generated.