Many cloud-based applications employ a data centre as a central server to process data that is generated by edge devices, such as smartphones, tablets and wearables. This model places ever increasing demands on communication and computational infrastructure with inevitable adverse effect on Qualityof-Service and Experience. The concept of Edge Computing is predicated on moving some of this computational load towards the edge of the network to harness computational capabilities that are currently untapped in edge nodes, such as base stations, routers and switches. This position paper considers the challenges and opportunities that arise out of this new direction in the computing landscape.
Current computing techniques using the cloud as a centralised server will become untenable as billions of devices get connected to the Internet. This raises the need for fog computing, which leverages computing at the edge of the network on nodes, such as routers, base stations and switches, along with the cloud. However, to realise fog computing the challenge of managing edge nodes will need to be addressed. This paper is motivated to address the resource management challenge. We develop the first framework to manage edge nodes, namely the Edge NOde Resource Management (ENORM) framework. Mechanisms for provisioning and auto-scaling edge node resources are proposed. The feasibility of the framework is demonstrated on a Pok éMon Go-like online game use-case. The benefits of using ENORM are observed by reduced application latency between 20%-80% and reduced data transfer and communication frequency between the edge node and the cloud by up to 95%. These results highlight the potential of fog computing for improving the quality of service and experience.
Power has become a primary concern for HPC systems. Dynamic voltage and frequency scaling (DVFS) and dynamic concurrency throttling (DCT) are two software tools (or knobs) for reducing the dynamic power consumption of HPC systems. To date, few works have considered the synergistic integration of DVFS and DCT in performance-constrained systems, and, to the best of our knowledge, no prior research has developed application-aware simultaneous DVFS and DCT controllers in real systems and parallel programming frameworks. We present a multi-dimensional, online performance predictor, which we deploy to address the problem of simultaneous runtime optimization of DVFS and DCT on multi-core systems. We present results from an implementation of the predictor in a runtime library linked to the Intel OpenMP environment and running on an actual dual-processor quad-core system. We show that our predictor derives near-optimal settings of the power-aware program adaptation knobs that we consider. Our overall framework achieves significant reductions in energy (19% mean) and ED 2 (40% mean), through simultaneous power savings (6% mean) and performance improvements (14% mean). We also find that our framework outperforms earlier solutions that adapt only DVFS or DCT, as well as one that sequentially applies DCT then DVFS. Further, our results indicate that prediction-based schemes for runtime adaptation compare favorably and typically improve upon heuristic search-based approaches in both performance and energy savings.
Phylogenetic inference is considered to be one of the grand challenges in Bioinformatics due to the immense computational requirements. RAxML is currently among the fastest and most accurate programs for phylogenetic tree inference under the Maximum Likelihood (ML) criterion. First, we introduce new tree search heuristics that accelerate RAxML by a factor of 2.43 while returning equally good trees. The performance of the new search algorithm has been assessed on 18 real-world datasets comprising 148 up to 4,843 DNA sequences. We then present the implementation, optimization, and evaluation of RAxML on the IBM Cell Broadband Engine. We address the problems and provide solutions pertaining to the optimization of floating point code, control flow, communication, and scheduling of multi-level parallelism on the Cell.
With high-end systems featuring multicore/multithreaded processors and high component density, power-aware high-performance multithreading libraries become a critical element of the system software stack. Online power and performance adaptation of multithreaded code from within user-level runtime libraries is a relatively new and unexplored area of research. We present a user-level library framework for nearly optimal online adaptation of multithreaded codes for low-power, high-performance execution. Our framework operates by regulating concurrency and changing the processors/threads configuration as the program executes. It is innovative in that it uses fast, runtime performance prediction derived from hardware event-driven profiling, to select thread granularities that achieve nearly optimal energy-efficiency points. The use of predictors substantially reduces the runtime cost of granularity control and program adaptation. Our framework achieves performance and ED 2 (energy-delay-squared) levels which are: i) comparable to or better than those of oracle-derived offline predictors; ii) significantly better than those of online predictors using exhaustive or localized linear search. The complete prediction and adaptation framework is implemented on a real multi-SMT system with Intel Hyperthreaded processors and embeds adaptation capabilities in OpenMP programs.
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