Binding Performance and Power of Dense Linear Algebra Operations

In this paper we combine a powerful tracing framework with a power measurement setup to perform a visual analysis of the computational performance and the power consumption of tuned implementations for three key dense linear algebra operations: the LU factorization, the Cholesky factorization, and the reduction to tridiagonal form. Our results using 6 and 12 cores of an AMD Opteron-based platform reveal the serial/concurrent phases of the algorithms, and their connection to periods of low/high power consumption, as well as the linear dependency between execution time and energy for this class of operations.