This dissertation introduces measurement-based performance modeling and
prediction techniques for dense linear algebra algorithms. As a core principle,
these techniques avoid executions of such algorithms entirely, and instead
predict their performance through runtime estimates for the underlying compute
kernels. For a variety of operations, these predictions allow to quickly select
the fastest algorithm configurations from available alternatives. We consider
two scenarios that cover a wide range of computations:
To predict the performance of blocked algorithms, we design
algorithm-independent performance models for kernel operations that are
generated automatically once per platform. For various matrix operations,
instantaneous predictions based on such models both accurately identify the
fastest algorithm, and select a near-optimal block size.
For performance predictions of BLAS-based tensor contractions, we propose
cache-aware micro-benchmarks that take advantage of the highly regular
structure inherent to contraction algorithms. At merely a fraction of a
contraction's runtime, predictions based on such micro-benchmarks identify the
fastest combination of tensor traversal and compute kernel
