1 research outputs found
Robust Ranking of Equivalent Algorithms via Relative Performance
In scientific computing, it is common that one target computation can be
translated into many different sequences of library calls, each identifying an
algorithm. Although mathematically equivalent, those algorithms might exhibit
significant differences in terms of performance. In practice, we observe that
subsets of algorithms show comparable performance characteristics. For this
reason, we aim to identify and separate not one algorithm, but the subset of
algorithms, that are reliably faster than the rest. One of the motivations for
this setup is that it makes it then possible to select an algorithm based on
additional performance metrics such as those based on energy or scalability. To
this end, instead of using the usual methods of quantifying the performance of
an algorithm in absolute terms, we present a measurement-based approach that
assigns a relative score to the algorithms in comparison to one another. The
relative performance is encoded by sorting the algorithms based on pair-wise
comparisons and by ranking them into equivalence (or performance) classes, so
that multiple algorithms can obtain the same rank. We show that this approach,
based on relative performance, leads to robust identification of the fastest
algorithms, that is, reliable identification even under noisy system
conditions