By Richard P. Brent (auth.), Jack Dongarra, Kaj Madsen, Jerzy Waśniewski (eds.)
Introduction The PARA workshops some time past have been dedicated to parallel computing tools in technological know-how and expertise. there were seven PARA conferences up to now: PARA’94, PARA’95 and PARA’96 in Lyngby, Denmark, PARA’98 in Umea, ? Sweden, PARA 2000 in Bergen, N- means, PARA 2002 in Espoo, Finland, and PARA 2004 back in Lyngby, Denmark. The ?rst six conferences featured lectures in sleek numerical algorithms, desktop technology, en- neering, and commercial functions, all within the context of scienti?c parallel computing. This assembly within the sequence, the PARA 2004 Workshop with the identify “State of the paintings in Scienti?c Computing”, used to be held in Lyngby, Denmark, June 20–23, 2004. The PARA 2004 Workshop used to be prepared through Jack Dongarra from the college of Tennessee and Oak Ridge nationwide Laboratory, and Kaj Madsen and Jerzy was once ´niewski from the Technical collage of Denmark. The emphasis right here used to be shifted to high-performance computing (HPC). the continuing improvement of ever extra complex desktops offers the opportunity of fixing more and more dif?cult computational difficulties. even if, given the complexity of recent machine architectures, the duty of understanding this power wishes cautious recognition. for instance, the failure to take advantage of a computer’s reminiscence hello- archy can degrade functionality badly. a primary difficulty of HPC is the advance of software program that optimizes the functionality of a given desktop. The excessive rate of cutting-edge desktops may be prohibitive for plenty of places of work, particularly if there's simply an occasional want for HPC.
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Additional resources for Applied Parallel Computing. State of the Art in Scientific Computing: 7th International Workshop, PARA 2004, Lyngby, Denmark, June 20-23, 2004. Revised Selected Papers
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Using the new data formats reduces this cost to zero. By only doing point 8 we see that we can get near peak performance as every subcomputation of point 8 is a point 6b computation. Now we discuss the use of kernel routines in concert with NDS. Take any standard linear algebra factorization code, say Gaussian elimination with partial pivoting or the QR factorization of an M by N matrix, A. It is quite easy to derive the block equivalent code from the standard code. In the standard code a floating point operation is usually a Fused Multiply Add (FMA), (c = c − ab), whose block equivalent is a call to a DGEMM kernel.
3–45. 7. R. Granat, I. Jonsson, and B. K˚agstr¨om, Combining Explicit and Recursive Blocking for Solving Triangular Sylvester-Type Matrix Equations on Distributed Memory Platforms, in Euro-Par 2004 Parallel Processing, M. Danelutto, D. Laforenza, and M. , Lecture Notes in Comput. Sci. 3149, Springer-Verlag, Berlin Heidelberg, 2004, pp. 742–750. 32 Bo K˚agstr¨om 8. F. G. Gustavson, Recursion leads to automatic variable blocking for dense linear-algebra algorithms, IBM J. Res. , 41 (1997), pp. 737–755.