Comparing Scalability Prediction Strategies on an SMP of CMPs [chapter]

Karan Singh, Matthew Curtis-Maury, Sally A. McKee, Filip Blagojević, Dimitrios S. Nikolopoulos, Bronis R. de Supinski, Martin Schulz
2010 Lecture Notes in Computer Science  
Diminishing performance returns and increasing power consumption of single-threaded processors have made chip multiprocessors (CMPs) an industry imperative. Unfortunately, poor software/hardware interaction and bottlenecks in shared hardware structures can prevent scaling to many cores. In fact, adding a core may harm performance and increase power consumption. Given these observations, we compare two approaches to predicting parallel application scalability: multiple linear regression and
more » ... icial neural networks (ANNs). We throttle concurrency to levels with higher predicted power/performance efficiency. We perform experiments on a state-of-the-art, dual-processor, quad-core platform, showing that both methodologies achieve high accuracy and identify energy-efficient concurrency levels in multithreaded scientific applications. The ANN approach has advantages, but the simpler regression-based model achieves slightly higher accuracy and performance. The approaches exhibit median error of 7.5% and 5.6%, and improve performance by an average of 7.4% and 9.5%, respectively.
doi:10.1007/978-3-642-15277-1_14 fatcat:dcsbavyywbdvhi4izdkkwtoipy