Performance/Energy Optimization of DSP Transforms on the XScale Processor

Abstract

The XScale processor family provides user-controllable independent configuration of CPU, bus, and memory frequencies. This feature introduces another handle for the code optimization with respect to energy consumption or runtime performance. We quantify the effect of frequency configurations on both performance and energy for three signal processing transforms: the discrete Fourier transform (DFT), finite impulse response (FIR) filters, and the Walsh-Hadamard Transform (WHT).

To do this, we use SPIRAL, a program generation and optimization system for signal processing transforms. For a given transform to be implemented, SPIRAL searches over different algorithms to find the best match to the given platform with respect to the chosen performance metric (usually runtime). In this paper we use SPIRAL to generate implementations for different frequency configurations and optimize for runtime and physically measured energy consumption. In doing so we show that first, each transform achieves best performance/energy consumption for a different system configuration; second, the best code depends on the chosen configuration, problem size and algorithm; third, the fastest implementation is not always the most energy efficient; fourth, we introduce dynamic (i.e., during execution) reconfiguration in order to further improve performance/energy. Finally, we benchmark SPIRAL generated code against Intel’s vendor library routines. We show competitive results as well as 20% performance improvements or energy reduction for selected transforms and problem sizes.

This work was supported by DARPA through the Department of Interior grant NBCH1050009 and by NSF through awards 0234293 and 0325687.