Copyright by William Lloyd Bircher 2010 - The Laboratory for ...

Table 7.3: SYSmark 2007 DVFS Hit Rate
Predictor E-Learning Productivity Video Creation 3D
Core-Level
PPPP
82.6% 73.8% 76.4% 72.7%
Aggregate
PPPP
26.8% 26.3% 40.2% 30.7%
Core-Level Reactive (Vista) 66.4% 65.2% 63.5% 59.7%
The limitations of reactive DVFS selection are evident. Due to frequent transitions
between high and low activity levels, the reactive scheme is only able to achieve the
correct frequency about 2/3 of the time. PPPP applied at the aggregate level is much
worse with an average of 31% accuracy. The best case is achieved with the core-level
PPPP which averages 76%. The differences in the success of these predictors are a result
of prediction coverage and accuracy of the predicted phases. See Table 7.4. Coverage is
defined as percentage of the workload in which a prediction is available. A prediction
could be unavailable if the last observed activity pattern has not been seen before or has
caused too many mispredictions. The reactive scheme does not have coverage since it
does not predict. In contrast PPPP has much lower prediction coverage, especially for the
aggregate predictor. The aliasing of multiple core phases obscures predictable behavior
to less than 3% for E-Learning and Productivity. Video Creation and 3D are slightly
better at 16% and 8% respectively. One possible reason is that these workloads have
larger portions of multi-threaded execution. The aggregate activity level is likely more
representative of core-level activity compared to the single-threaded E-Learning and
Productivity. Core-level PPPP achieves the highest accuracy by having a large workload
136