Improving financial computation speed with full and subproblem memoization-Reference-Cited by-同舟云学术

Improving financial computation speed with full and subproblem memoization

Published:2015-10-13 Issue:3 Volume:28 Page:905-915
ISSN:1532-0626
Container-title:Concurrency and Computation: Practice and Experience
language:en
Short-container-title:Concurrency Computat.: Pract. Exper.

Author:

Moreno Alexander¹,Balch Tucker²

Affiliation:

1. School of Computer Science Georgia Institute of Technology 801 Atlantic Drive Northwest Atlanta GA 30332 USA

2. School of Interactive Computing Georgia Institute of Technology 801 Atlantic Drive Northwest Atlanta GA 30332 USA

Funder

Gordon and Betty Moore Foundation

National Science Foundation

University of Chicago

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Theoretical Computer Science,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpe.3693

Reference15 articles.

1. Selective memoization

2. AcarUA.Self‐adjusting computation.Ph.D. Thesis Carnegie Mellon University 2005.

3. VaroquauxG GriselO.Joblib: running Python function as pipeline jobs 2009. packages. python.org/joblib.

4. CoelhoLP.Jug: a task‐based parallelization framework 2009.

5. GuoPJ EnglerD.Towards practical incremental recomputation for scientists: an implementation for the Python language.Proceedings of the 2nd Conference on Theory and Practice of Provenance (TAPP'10) USENIX Association Berkeley CA USA 2010;6–6.

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1. Jug: Software for Parallel Reproducible Computation in Python;Journal of Open Research Software;2017-10-27

2. Workshop on high‐performance computational finance;Concurrency and Computation: Practice and Experience;2015-12-21