Balancing productivity and performance on the cell broadband engine

Abstract

The cell broadband engine (BE) is a heterogeneous multicore processor, combining a general-purpose POWER architecture core with eight independent single-instruction-multiple-data (SIMD) cores. Each core is capable of very high performance; however, users must explicitly manage data movement, scheduling, and synchronization. While these attributes provide some of the cell processorpsilas greatest performance strengths, they also form its greatest weaknesses in terms of developer productivity, code portability, and initial performance efficiencies. In this paper, we evaluate productivity and relative performance improvements of a cell BE system for a diverse set of kernels and applications. Our experimental workload includes algorithms from scientific, cognitive, and imaging problem domains. Our results demonstrate that the cell processor could be several times faster than a SSE-enabled, contemporary dual-core processor, and could sustain a high performance-to-productivity ratio. We outline strategies for transforming applications to exploit the cellpsilas architectural features, and measure productivity by comparing programming effort in terms of lines of code and performance. For instance, our measurements revealed that a covariance matrix creation routine - a common routine in hyperspectral imaging - ran over eight times faster than a 2.66 GHz Intel Woodcrest processor while sustaining a productivity metric of over two by parallelizing across the heterogeneous cores, unrolling loops, and improving instruction level parallelism with SIMD instructions in a high-level language.