Read e-book online Advances in Software Tools for Scientific Computing PDF

By Jeremy Siek, Andrew Lumsdaine (auth.), Hans Petter Langtangen, Are Magnus Bruaset, Ewald Quak (eds.)

ISBN-10: 3540665579

ISBN-13: 9783540665571

ISBN-10: 3642571727

ISBN-13: 9783642571725

To make complete use of the ever expanding functions of recent com­ puters, it's important to swiftly improve the functionality and reliability of the software program in addition, and infrequently with no need an appropriate mathematical thought on hand. within the dealing with of an increasing number of advanced real-life numerical difficulties in all types of functions, a contemporary object-oriented de­ signal and implementation of software program instruments has turn into a very important part. The substantial demanding situations posed by way of the call for for effective object-oriented software program in all components of clinical computing make it essential to trade principles and studies from as many various resources as attainable. stimulated by means of the good fortune of the 1st assembly of this sort in Norway in 1996, we made up our minds to arrange one other overseas Workshop on smooth software program instruments for clinical Computing, also known as SciTools'98. This workshop happened in Oslo, Norway, September 14-16, 1998. The ob­ jective used to be back to supply an open discussion board for trade and dialogue of recent, state of the art software program concepts utilized to not easy numerical difficulties. The association used to be undertaken together by way of the examine institute SINTEF utilized arithmetic, the Departments of arithmetic and Infor­ matics on the college of Oslo, and the corporate Numerical gadgets AS.

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The code below is a short example of creating a sparselD with a set, inserting a few elements, and then accessing the values and indices of the resulting vector. The normal iterators of the sparselD, returned by beginO, give J. Siek, A. Lumsdaine 24 access to the values ofthe elements (through dereference) and also the indices (through the indexO method on the iterator). If one wishes to view the indices only (the non-zero structure of the vector), one can use the nz_struct 0 method to obtain a container consisting of the indices of the elements in the sparse vector.

Many compilers do not recognize that this optimization can also be applied to objects on the stack. They specify a load or store for each access to a data item within the object, which kills performance for codes like STL and MTL which use iterators objects that have to be accessed over and over again within the inner loops of the code. A typical example of the problem with small objects comes up in the use of complex numbers in C++. We illustrate the problem with the code below which calculates the sum of a complex vector.

To obtain high performance on a modern microprocessor, an algorithm must properly exploit the associated memory hierarchy and pipeline architecture. Todays compilers are not able to apply all of these transformations, so the programmer must apply some optimizations by hand. To make matters worse, the transformations are somewhat machine dependent. The number of registers, size of cache, and other machine characteristics affect the blocking sizes. This makes it difficult to express high performance 1 We use the C++ compiler from Kuck and Associates, Inc.

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Advances in Software Tools for Scientific Computing by Jeremy Siek, Andrew Lumsdaine (auth.), Hans Petter Langtangen, Are Magnus Bruaset, Ewald Quak (eds.)


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