Performance and Energy Trade-Offs for Parallel Applications on Heterogeneous Multi-Processing Systems

This work proposes a methodology to find performance and energy trade-offs for parallel applications running on Heterogeneous Multi-Processing systems with a single instruction-set architecture. These offer flexibility in the form of different core types and voltage and frequency pairings, defining a vast design space to explore. Therefore, for a given application, choosing a configuration that optimizes the performance and energy consumption is not straightforward. Our method proposes novel analytical models for performance and power consumption whose parameters can be fitted using only a few strategically sampled offline measurements. These models are then used to estimate an application’s performance and energy consumption for the whole configuration space. In turn, these offline predictions define the choice of estimated Pareto-optimal configurations of the model, which are used to inform the selection of the configuration that the application should be executed on. The methodology was validated on an ODROID-XU3 board for eight programs from the PARSEC Benchmark, Phoronix Test Suite and Rodinia applications. The generated Pareto-optimal configuration space represented a 99% reduction of the universe of all available configurations. Energy savings of up to 59.77%, 61.38% and 17.7% were observed when compared to the performance, ondemand and powersave Linux governors, respectively, with higher or similar performance

Energy-Efficient Transaction Scheduling in Data Systems

Natural short term fluctuations in the load of transactional data systems present an opportunity for power savings. For example, a system handling 1000 requests per second on average can expect more than 1000 requests in some seconds, fewer in others. By quickly adjusting processing capacity to match such fluctuations, power consumption can be reduced. Many systems do this already, using dynamic voltage and frequency scaling (DVFS) to reduce processor performance and power consumption when the load is low. DVFS is typically controlled by frequency governors in the operating system or by the processor itself. The work presented in this dissertation shows that transactional data systems can manage DVFS more effectively than the underlying operating system. This is because data systems have more information about the workload, and more control over that workload, than is available to the operating system. Our goal is to minimize power consumption while ensuring that transaction requests meet specified latency targets. We present energy-efficient scheduling algorithms and systems that manage CPU power consumption and performance within data systems. These algorithms are workload-aware and can accommodate concurrent workloads with different characteristics and latency budgets. The first technique we present is called POLARIS. It directly manages processor DVFS and controls database transaction scheduling. We show that POLARIS can simultaneously reduce power consumption and reduce missed latency targets, relative to operating-system-based DVFS governors. Second, we present PLASM, an energy-efficient scheduler that generalizes POLARIS to support multi-core, multi-processor systems. PLASM controls the distribution of requests to the processors, and it employs POLARIS to manage power consumption locally at each core. We show that PLASM can save power and reduce missed latency targets compared to generic routing techniques such as round-robin