Basic C Libraries

[Nolan Guyz]

WhenI use command line it's ok (all necessary jdk and jre libraries are installed). But if I try to write just a simple "Hello World" with eclipse I see that it doesn't know anything about Object class, String class, e.t.c.

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I'm looking for a software library with a scope similar to "numerical recipes", but implemented in a modern programming language. "Modern" in this context means to me: object oriented (not C or Fortran), running in a virtual machine with garbage collection (not C++), and with support for functional programming.Examples therefore would be: F#, C# from the .NET framework, Scala and to a lesser extend Java (no functional programming yet).

"Scope" means that it should cover all algorithms that are taught in, say, a general, one or two year(s) long introduction to numerical analysis, starting with linear algebra, up to partial differential equations and stochastic processes.

In addition I am interested if any research group using high performance computing uses a language that fits the description, and if not, why not. It is "common knowledge" in the software industry that one should no longer worry about performance problems of languages running in a virtual machine compared to languages compiled directly to machine code, e.g. Java versus C++. Any experience from high performance computing that affirms or contradicts this would be interesting to me, too.

Addendum after reading the first three answers (rather than commenting them individually): My motivation for asking about these "modern" programming language is this: It takes some effort to learn a programming language, much commercial software is written in Java, often not because Java is designed for the kind of problem people have to solve, but because there are many programmers out there that know Java, there is a big open source community, and, finally, "nobody gets fired for choosing Java" (paraphrasing "nobody gets fired for buying IBM"). This outweighs the fact that Java is often clearly not the best choice from a pure language point of view.A software library in a "modern" programming language could attract more people (maybe more contributors if it is open source), for this reason, than a library in a much better suited, but less known, programming language.

I think, your idea of "modern" is not very practical, when it comes to computational mathematics. To people, who do research on algorithms, the one crucial measurement of their success is running time. This is also very important to most mathematicians, because hypotheses are tested for very large numbers/objects. Your "modern" languages are mainly geared at speeding up the design and coding process and not the running time of the programs. Implementing mathematical algorithms in C# would have more of an educational purpose than a practical one, and if someone tries to sell you such a thing, you shouldn't buy it.

I don't know much about the "common knowledge" that you are referring to, or in fact about the software industry, but fact is that when people write computer algebra packages, they still sometimes implement the crucial and most used parts in Assembler! Your comparison "Java versus C++" misses the point. Already C++ produces so much more overhead than C, say, that it would rarely be the language of choice for computer algebra packages, where efficiency is crucial. Don't get me wrong, I recognise the huge advantages of object oriented programming languages, but they are at their best if they are used for the things they were design for.

This is still something of an open problem. The reason is that achieving high performance on numerical codes takes fairly close attention to things like data locality and the details of the cache hierarchy -- failing to do so can easily cost a factor of 10 or 20 in performance. So compiling high-performance numerical code from high-level programs requires fairly sophisticated language and compiler technology.

The state of the art at the moment for high-level numerical programming is probably something like SAC, which is a purely functional language which uses ideas from functional programming, linear types, and nested data parallelism to efficiently parallelize and SIMD-ize high level specifications of array processing programs. This builds on ideas from older research languages like Sisal and NESL.

Another example is the ATS project, which is a functional language which uses dependent type theory and linear types to support generating very efficient code. I don't think they have explicitly focused on numerical codes, though they have investigated high-performance systems programming more generally.

Another commonly-used approach is to write a high-level program which generates the low-level code, rather than writing it by hand. This is used by the FFTW package ("Fastest Fourier Transform in the West"), which is an Ocaml program (written in a very higher-order functional style) which generates the C code in the FFTW package.

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