I finally got some time to answer Simon's posting: Simon P-J: >| Between google searching and looking through the activity >| report, I take it that no one has really developed serious >| libraries for matrix manipulations, diff eqs, etc. >| >| Are there any practical reasons for this or is it just a >| matter of the haskell community being small and there not >| being many people interested in something so specialized? > >The latter I think, but it's just the sort of thing that a functional >language should be good at. Two other difficulties > >(a) It's hard to compete with existing libraries. The obvious thing is >not to compete; instead, just call them. But somehow that doesn't seem >to be as motivating. Perhaps some bindings exist though?
Hard to compete, yes. But on the other hand, the rewards can be high. Numerical library code (especially matrix libraries) tends to be highly optimized for the hardware architecture at hand. As a consequence a small change in, say, the cache architecture, might require a thorough rewrite of the library code to regain high utilisation of the hardware. This is since languages like Fortran and C force you to code so close to the hardware. A high-level language, with good optimizing compilation, would make also library code more portable across hardware architectures. N.b. these optimizations are non-trivial to say the least. >(b) A concern about efficiency, because numerical computation is >typically an area where people really care about how many instructions >you take. It's a legitimate concern, but I don't think that it'll turn >out to be justified. With unboxed arrays, and/or calling external >libraries for the inner loops -- and the potential for aggressive fusion >and/or parallelism, there is plenty of upward potential. I also want to >work on nested data parallelism (a la NESL, and NEPAL) which fits right >in here. The number of instructions is only one side of the coin. For high-performance computing, memory issues are at least as important: both the amount of memory used (e.g., will the computation fit into memory at all), and how the memory hierarchy is utilized (caches, TLB:s, virtual memory, ...). This is a really sweet spot of functional languages, and laziness adds to it. On the other hand, the increased abstraction of functional languages gives an optimizing compiler larger freedom to reorder computations and choose memory layouts of data structures like matrices. This is potentially very useful, since optimizing for memory hierarchy utilization typically involves both data layout and order of memory accesses. However, to achieve a good result, the compiler must be able to predict a great deal of the computing and the memory usage. For instance, dynamic memory handling of numeric data structures will surely kill any serious attempt to predict the cache behavior. To achieve good optimizing compilation, we need either very good program analyses, or a library of recursion patterns or templates for which the compiler knows how to allocate memory statically and order the computations well, or possibly both. Some encouraging examples: Sven-Bodo Scholz has achieved very good performance for the restricted functional matrix language SAC, using optimizations for cache. My former student Peter Drakenberg invented a restricted functional matrix language, with analyses to infer matrix sizes statically, and sharing analysis, to find opportunities to allocate memory statically and update in-place. He also got some good experimental figures. This leads me to believe that compilers in more general languages could do something similar, by recognizing certain patterns or through advanced program analyses. However, both these languages are strict, and I am not sure at all how to do this in a lazy language. In any case, this is nontrivial compiler work and considerable research efforts would be needed. Unfortunately, I don't see how to fund such research, since the high-performance computing community largely seems to have given up on functional languages since the demise of the data-flow languages. >I'd love to see a little community of matrix manipulators spinning up. Yes. There might me a niche for high-level numerical coding, somehwere where MATLAB is today. MATLAB isn't exactly blazingly fast, still very widespread. On the other hand, MATLAB is already in that niche. The question to answer is what advantages a functional language like Haskell could offer in this niche. We need to come up with these answers, and then convince enough people outside our own community. Björn Lisper _______________________________________________ Haskell-Cafe mailing list Haskell-Cafe@haskell.org http://www.haskell.org/mailman/listinfo/haskell-cafe
