ACM Home Page
Please provide us with feedback. Feedback
Modeling the performance of interface contraction
Full text PdfPdf (157 KB)
Source ACM Transactions on Mathematical Software (TOMS) archive
Volume 29 ,  Issue 4  (December 2003) table of contents
Pages: 440 - 457  
Year of Publication: 2003
ISSN:0098-3500
Authors
H. Martin Bücker  Aachen University, Aachen, Germany
Arno Rasch  Aachen University, Aachen, Germany
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 3,   Downloads (12 Months): 23,   Citation Count: 1
Additional Information:

abstract   references   cited by   index terms   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/962437.962442
What is a DOI?

ABSTRACT

Automatic differentiation is a technique used to transform a computer code implementing some mathematical function into another program capable of evaluating the function and its derivatives. Compared to numerical differentiation, the derivatives obtained from applying automatic differentiation are free from truncation error, and their computation often requires less time. To increase the efficiency of a black box approach of automatic differentiation, a technique called interface contraction may be used. Interface contraction exploits the local structure of a code to temporarily reduce the global number of derivatives propagated through the code. Two performance models are introduced to predict the potential improvement in the execution time of a program making use of interface contraction compared to a program generated by a black box approach of automatic differentiation. The performance models are validated by numerical experiments carried out on different computing platforms. The computer codes used in the experiments stem from the application areas of neutron scattering and biostatistics.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
Amdahl, G. M. 1967. Validity of a single processor approach to achieving large scale computer capabilities. In Proceedings of AFIPS Spring Joint Computer Conference Vol. 30. Atlantic City, NJ, 438--485.
 
2
Berz, M., Bischof, C., Corliss, G., and Griewank, A. 1996. Computational Differentiation: Techniques, Applications, and Tools. SIAM, Philadelphia.
 
3
 
4
Bischof, C., Khademi, P., Bouaricha, A., and Carle, A. 1996b. Efficient computation of gradients and Jacobians by dynamic exploitation of sparsity in automatic differentiation. Optimization Methods and Software 7, 1 (July), 1--39.
 
5
Bischof, C. H. and Haghighat, M. R. 1996. Hierarchical approaches to automatic differentiation. In M. Berz, C. Bischof, G. Corliss, and A. Griewank, Computational Differentiation: Techniques, Applications, and Tools. SIAM, Philadelphia, (1996).
6
 
7
 
8
 
9
Griewank, A. and Corliss, G. 1991. Automatic Differentiation of Algorithms. SIAM, Philadelphia.
 
10
 
11
Pele, H. W., Hempelmann, R., Prager, M., and Zeidler, M. D. 1991. Dynamics of 18-crown-6 ether in aqueous solution studied by quasielastic neutron scattering. Berichte der Bunsen-Gesellschaft für physikalische Chemie 95, 5, 592--598.
 
12
Rall, L. B. 1981. Automatic Differentiation: Techniques and Applications. Lecture Notes in Computer Science, vol. 120. Springer Verlag, Berlin.


Collaborative Colleagues:
H. Martin Bücker: colleagues
Arno Rasch: colleagues