Darren M. Chitty
ABSTRACT
Modern CPUs are complex with hierarchical cache memory levels, vector instruction sets, instruction level parallelism and multiple processor cores. Hence, extracting the maximum performance for a given algorithm is a complex task and can require the optimisation of a number of parameters. This paper will demonstrate the use of an evolutionary approach to tune a matrix multiplication algorithm in terms of both execution speed and also cache memory usage. Moreover, it will be shown that these objectives conflict to some degree. Hence, a multi-objective evolutionary tuning approach is demonstrated that optimises for both of these objectives establishing a Pareto front of solutions.
- K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. A fast and elitist multiobjective genetic algorithm: NSGA-II. Trans. Evol. Comp, 6(2):182--197, Apr. 2002. Google Scholar
Digital Library
- J. Dongarra and A. R. Hinds. Unrolling loops in FORTRAN. Softw., Pract. Exper., 9(3):219--226, 1979.Google ScholarCross Ref
- G. Golub and C. Loan. Matrix computations. Johns Hopkins series in the mathematical sciences. Johns Hopkins University Press, 1989.Google Scholar
Index Terms
- Multi-objective evolutionary auto-tuning for optimising algorithm speed and cache memory usage
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