Detailed Low-cost Energy and Power Monitoring of Computing Systems

Power and energy are increasingly important metrics in modern computing systems. Large supercomputers utilize millions of cores and can consume as much power as a small town; monitoring and reducing power consumption is an important task. At the other extreme, power usage of embedded and mobile devices is also critically important. Battery life is a key concern in such devices; having detailed power measurement allows optimizing these devices for power as well. Current systems are not set up to allow easy power measurement. There has been much work in this area, but obtaining power readings is often expensive, intrusive, and not well validated. In this work we propose a low-cost, easy-to-use, power measurement methodology that can be used in both high-end servers and low-end embedded systems. We then validate the results gathered against existing power measurement systems. We extend the existing Linux perf utility so that it can provide real-world fine-grained power measurements, allowing users easy access to these values, enabling new advanced power optimization opportunities

Enabling Raspberry Pi Performance Counter Support on Linux perf_event

ABSTRACT The Raspberry Pi is a low-cost, low-power, embedded ARM platform designed for use as an educational tool. The ARMv6 processor core included on the Raspberry Pi includes support for hardware performance counters (low-overhead registers that can provide detailed architectural performance measurements). Support for these counters is available for ARM Linux via the perf event interface, but not enabled by default for the Raspberry Pi. In this paper we investigate why the counters were not enabled, describe what steps are needed to enable them, and then validate the results to ensure they are working. We contributed the patches needed to enable the counters to upstream Linux maintainers so that support will be available by default for all users

Arthropod Phylogenetics in Light of Three Novel Millipede (Myriapoda: Diplopoda) Mitochondrial Genomes with Comments on the Appropriateness of Mitochondrial Genome Sequence Data for Inferring Deep Level Relationships

Background Arthropods are the most diverse group of eukaryotic organisms, but their phylogenetic relationships are poorly understood. Herein, we describe three mitochondrial genomes representing orders of millipedes for which complete genomes had not been characterized. Newly sequenced genomes are combined with existing data to characterize the protein coding regions of myriapods and to attempt to reconstruct the evolutionary relationships within the Myriapoda and Arthropoda. Results The newly sequenced genomes are similar to previously characterized millipede sequences in terms of synteny and length. Unique translocations occurred within the newly sequenced taxa, including one half of the Appalachioria falcifera genome, which is inverted with respect to other millipede genomes. Across myriapods, amino acid conservation levels are highly dependent on the gene region. Additionally, individual loci varied in the level of amino acid conservation. Overall, most gene regions showed low levels of conservation at many sites. Attempts to reconstruct the evolutionary relationships suffered from questionable relationships and low support values. Analyses of phylogenetic informativeness show the lack of signal deep in the trees (i.e., genes evolve too quickly). As a result, the myriapod tree resembles previously published results but lacks convincing support, and, within the arthropod tree, well established groups were recovered as polyphyletic. Conclusions The novel genome sequences described herein provide useful genomic information concerning millipede groups that had not been investigated. Taken together with existing sequences, the variety of compositions and evolution of myriapod mitochondrial genomes are shown to be more complex than previously thought. Unfortunately, the use of mitochondrial protein-coding regions in deep arthropod phylogenetics appears problematic, a result consistent with previously published studies. Lack of phylogenetic signal renders the resulting tree topologies as suspect. As such, these data are likely inappropriate for investigating such ancient relationships

Design and Analysis of a 32-bit Embedded High-Performance Cluster Optimized for Energy and Performance -Extended Edition

Abstract A growing number of supercomputers are being built using processors with low-power embedded ancestry, rather than traditional high-performance cores. In order to evaluate this approach we investigate the energy and performance tradeoffs found with ten different 32-bit ARM development boards while running the HPL Linpack and STREAM benchmarks. Based on these results (and other practical concerns) we chose the Raspberry Pi as a basis for a power-aware embedded cluster computing testbed. Each node of the cluster is instrumented with power measurement circuitry so that detailed cluster-wide power measurements can be obtained, enabling power / performance co-design experiments. While our cluster lags recent x86 machines in performance, the power, visualization, and thermal features make it an excellent low-cost platform for education and experimentation

A Raspberry Pi Cluster Instrumented for Fine-Grained Power Measurement

Power consumption has become an increasingly important metric when building large supercomputing clusters. One way to reduce power usage in large clusters is to use low-power embedded processors rather than the more typical high-end server CPUs (central processing units). We investigate various power-related metrics for seventeen different embedded ARM development boards in order to judge the appropriateness of using them in a computing cluster. We then build a custom cluster out of Raspberry Pi boards, which is specially designed for per-node detailed power measurement. In addition to serving as an embedded cluster testbed, our cluster’s power measurement, visualization and thermal features make it an excellent low-cost platform for education and experimentation