Download Advanced Functional Programming: 6th International School, by Umut A. Acar, Ruy Ley-Wild (auth.), Pieter Koopman, Rinus PDF

By Umut A. Acar, Ruy Ley-Wild (auth.), Pieter Koopman, Rinus Plasmeijer, Doaitse Swierstra (eds.)

This instructional booklet provides seven conscientiously revised lectures given on the sixth foreign tuition on useful Programming, AFP 2008, in Heijen, The Netherlands in could 2008.

The ebook provides the next seven, rigorously cross-reviewed chapters, written by means of prime gurus within the box: Self-adjusting: Computation with Delta ML, spider spinning for dummies, from reduction-based to reduction-free normalization, libraries for accepted programming in Haskell, dependently typed programming in agda, parallel and concurrent programming in Haskell and an iTask case research: a convention administration system.

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Extra info for Advanced Functional Programming: 6th International School, AFP 2008, Heijen, The Netherlands, May 2008, Revised Lectures

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Self-Adjusting Computation. PhD thesis, Department of Computer Science. Carnegie Mellon University (May 2005) 3. : Imperative self-adjusting computation. In: Proceedings of the 25th Annual ACM Symposium on Principles of Programming Languages (2008) 4. : An experimental analysis of self-adjusting computation. In: Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation (2006) 5. : Adaptive Functional Programming. In: Proceedings of the 29th Annual ACM Symposium on Principles of Programming Languages, pp.

Thus, no two prefix sums are the same and a linear number of operations (in the length of the input) will differ. , change propagation performs just as good re-computing from scratch. Figure 11 shows a logarithmic stable solution for reduce. This implementation uses the classic technique of random-sampling to compute the result. The idea is to ”halve” the input list into smaller and smaller lists until only a single element remains. A. Acar and R. Ley-Wild fun reduce mkRandSplit f base = afn l => let afun halfList l = let val putM = mkPut $ () val randSplit = mkRandSplit () afun redRun (v,l) = case get $ l of NIL => (v, l) | CONS (h, t) => if randSplit h then (f (v,h), t) else redRun $ (f(v,h), t) mfun half l = case get $ l of NIL => NIL | CONS (h, t) => let val (v, tt) = redRun $ (h,t) val ttt = half $ tt in CONS(v, putM $ (SOME h, ttt)) end in putM $ (NONE, half $ l) end mfun mreduce l = let val putM = mkPut $ () if get $ ((lengthLessThan 2) $ l) then case get $ l of NIL => base | CONS(h, ) => h else mreduce $ (halfList $ l) in putM $ (NONE, mreduce $ l) end Fig.

Acar and R. Ley-Wild 7 Self-Adjusting Conventional 6 Time(s) 5 4 3 2 1 0 0 20000 40000 60000 80000 100000 Input Size Fig. 13. Time for a from-scratch runs conventional and self-adjusting Figure 13 shows the run-time for executing the conventional and from-scratch versions of CIRCLES with up to 100,000 circles. 0. The measurement shows that the self-adjusting version is about 5 times slower than the conventional version. 5 times slower than the conventional. Although the overhead may be improved significantly by more careful compiler optimization techniques directed to self-adjusting programs (which the current implementation of our compiler does not employ), we consider it to be acceptable, because, in self-adjusting computation, from-scratch executions are infrequent.

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