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Register a new userA Note on Disk Drag Dynamics
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Added by
Neil J. Gunther
Publication date
2012
fields
Informatics Engineering
and research's language is
English
Authors
Neil J. Gunther
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The electrical power consumed by typical magnetic hard disk drives (HDD) not only increases linearly with the number of spindles but, more significantly, it increases as very fast power-laws of speed (RPM) and diameter. Since the theoretical basis for this relationship is neither well-known nor readily accessible in the literature, we show how these exponents arise from aerodynamic disk drag and discuss their import for green storage capacity planning.
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Given the growing importance of large-scale graph analytics, there is a need to improve the performance of graph analysis frameworks without compromising on productivity. GraphMat is our solution to bridge this gap between a user-friendly graph analytics framework and native, hand-optimized code. GraphMat functions by taking vertex programs and mapping them to high performance sparse matrix operations in the backend. We get the productivity benefits of a vertex programming framework without sacrificing performance. GraphMat is in C++, and we have been able to write a diverse set of graph algorithms in this framework with the same effort compared to other vertex programming frameworks. GraphMat performs 1.2-7X faster than high performance frameworks such as GraphLab, CombBLAS and Galois. It achieves better multicore scalability (13-15X on 24 cores) than other frameworks and is 1.2X off native, hand-optimized code on a variety of different graph algorithms. Since GraphMat performance depends mainly on a few scalable and well-understood sparse matrix operations, GraphMatcan naturally benefit from the trend of increasing parallelism on future hardware.
An ever increasing number of configuration parameters are provided to system users. But many users have used one configuration setting across different workloads, leaving untapped the performance potential of systems. A good configuration setting can greatly improve the performance of a deployed system under certain workloads. But with tens or hundreds of parameters, it becomes a highly costly task to decide which configuration setting leads to the best performance. While such task requires the strong expertise in both the system and the application, users commonly lack such expertise. To help users tap the performance potential of systems, we present BestConfig, a system for automatically finding a best configuration setting within a resource limit for a deployed system under a given application workload. BestConfig is designed with an extensible architecture to automate the configuration tuning for general systems. To tune system configurations within a resource limit, we propose the divide-and-diverge sampling method and the recursive bound-and-search algorithm. BestConfig can improve the throughput of Tomcat by 75%, that of Cassandra by 63%, that of MySQL by 430%, and reduce the running time of Hive join job by about 50% and that of Spark join job by about 80%, solely by configuration adjustment.
Simple floating point operations like addition or multiplication on normalized floating point values can be computed by current AMD and Intel processors in three to five cycles. This is different for denormalized numbers, which appear when an underflow occurs and the value can no longer be represented as a normalized floating-point value. Here the costs are about two magnitudes higher.
We investigate the effects of ram pressure stripping on gas-rich disk galaxies in the cluster environment. Ram pressure stripping principally effects the atomic gas in disk galaxies, stripping away outer disk gas to a truncation radius. We demonstrate that the drag force exerted on truncated gas disks is passed to the stellar disk, and surrounding dark matter through their mutual gravity. Using a toy model of ram pressure stripping, we show that this can drag a stellar disk and dark matter cusp off centre within its dark matter halo by several kiloparsecs. We present a simple analytical description of this process that predicts the drag force strength and its dependency on ram pressures and disk galaxy properties to first order. The motion of the disk can result in temporary deformation of the stellar disk. However we demonstrate that the key source of stellar disk heating is the removal of the gas potential from within the disk. This can result in disk thickening by approximately a factor of two in gas-rich disks.
We develop an analytical model for the accretion and gravitational drag on a point mass that moves hypersonically in the midplane of a gaseous disk with a Gaussian vertical density stratification. Such a model is of interest for studying the interaction between a planet and a protoplanetary disk, as well as the dynamical decay of massive black holes in galactic nuclei. The model considers that the flow is ballistic, and gives fully analytical expressions for both the accretion rate onto the point mass, and the gravitational drag it suffers. The expressions are further simplified by taking the limits of a thick, and of a thin disk. The results for the thick disk reduce correctly to those for a uniform density environment (Canto et al. 2011). We find that for a thin disk (small vertical scaleheight compared to the gravitational radius) the accretion rate is proportional to the mass of the moving object and to the surface density of the disk, while the drag force is independent of the velocity of the object. The gravitational deceleration of the hypersonic perturber in a thin disk was found to be independent of its parameters (i.e. mass or velocity) and depends only on the surface mass density of the disk. The predictions of the model are compared to the results of three-dimensional hydrodynamical simulations, with a reasonable agreement.
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