Abstract We propose a high-performance texture streaming system for real-time rendering of large 3D cities with millions of textures. Our main contribution is a texture streaming system that automatically adjusts the streaming workload at runtime based on measured frame latencies, specifically addressing the high memory binding costs of hardware virtual texturing which causes frame rate stuttering. Our system streams textures in parallel with prioritization based on GPU computed mesh perceptibility, and these textures are cached in a sparse partially resident image at runtime without the need for a texture preprocessing step. In addition, we improve rendering quality by minimizing texture pop-in artifacts using a color blending scheme based on mipmap levels. We evaluate our texture streaming system using three structurally distinct datasets with many textures and compared it to a baseline, a game engine, and our prior method. Results show an 8X improvement in rendering performance and 7X improvement in rendering quality compared to the baseline.

Click-through rate (CTR) prediction plays a critical role in recommender systems and online advertising. The data used in these applications are multi-field categorical data, where each feature belongs to one field. Field information is proved to be important and there are several works considering fields in their models. In this paper, we proposed a novel approach to model the field information effectively and efficiently. The proposed approach is a direct improvement of FwFM, and is named Field-matrixed Factorization Machines (FmFM, or FM2). We also proposed a new explanation of FM and FwFM within the FmFM framework, and compared it with the FFM. Besides pruning the cross terms, our model supports field-specific variable dimensions of embedding vectors, which acts as a soft pruning. We also proposed an efficient way to minimize the dimension while keeping the model performance. The FmFM model can also be optimized further by caching the intermediate vectors, and it only takes thousands of floating-point operations (FLOPs) to make a prediction. Our experiment results show that it can out-perform the FFM, which is more complex. The FmFM model’s performance is also comparable to DNN models which require much more FLOPs in runtime.

We introduce a novel, high performing, bandwidth-aware texture streaming system for progressive texturing of buildings in large 3D cities, with optional texture pre-processing. We seek to maintain high and consistent texture streaming performance across different city datasets, and to address the high memory binding latency in hardware virtual textures. We adopt the sparse partially-resident image to cache mesh textures at runtime and propose to allocate memory persistently, based on mesh visibility weightings and estimated GPU bandwidth. We also retain high quality rendering by minimizing texture pop-ins when transitioning between texture mipmaps. We evaluate our texture streaming system on large city datasets, including a tile-based dataset with 56K large atlases and a dataset containing 5.7M individual textures. Results indicate fast and robust streaming and rendering performance with minimal pop-in artifacts suitable for real-time rendering of large 3D cities.

Many industries experience an explosion in digital content. This explosion of electronic documents, along with new regulations and document retention rules, sets new requirements for performance efficiency of traditional data protection and archival tools. During a backup session a predefined set of objects (client filesystems) should be backed up. Traditionally, no information on the expected duration and throughput requirements of different backup jobs is provided. This may lead to a suboptimal job schedule that results in the increased backup session time. In this work, we characterize each backup job via two metrics, called job duration and job throughput. These metrics are derived from collected historic information about backup jobs during previous backup sessions. Our goal is to automate the design of a backup schedule that minimizes the overall completion time for a given set of backup jobs. This problem can be formulated as a resource constrained scheduling problem where a set of n jobs should be scheduled on m machines with given capacities. We provide an integer programming (IP) formulation of this problem and use available IP-solvers for finding an optimized schedule, called binpacking schedule. Performance benefits of the new bin-packing schedule are evaluated via a broad variety of realistic experiments using backup processing data from six backup servers in HP Labs. The new bin-packing job schedule significantly optimizes the backup session time (20%-60% of backup time reduction). HP Data Protector (DP) is HP's enterprise backup offering and it can directly benefit from the designed technique. Moreover, significantly reduced backup session times guarantee an improved resource/power usage of the overall backup solution.

This brief announcement presents a pair of performance laws that bound the change in aggregate job queueing time that results when the processor speed changes in a parallel computing system. Our laws require only lightweight passive external observations of a black-box system and they apply to many commonly employed scheduling policies. By predicting the application-level performance impact of processing speed adjustments in parallel processors, including traditional SMPs and now increasingly ubiquitous multicore processors, our laws address problems ranging from capacity planning to dynamic resource allocation. Finally, our results show that operational analysis---an approach to performance analysis traditionally associated with commercial transaction processing systems---usefully complements existing parallel performance analysis techniques.

Real production applications ranging from enterprise applications to large e-commerce sites share a crucial but seldom-noted characteristic: The relative frequencies of transaction types in their workloads are nonstationary, i.e., the transaction mix changes over time. Accurately predicting application-level performance in business-critical production applications is an increasingly important problem. However, transaction mix nonstationarity casts doubt on the practical usefulness of prediction methods that ignore this phenomenon.

Restoring data operations after a disaster is a daunting task: how should recovery be performed to minimize data loss and application downtime? Administrators are under considerable pressure to recover quickly, so they lack time to make good scheduling decisions. They schedule recovery based on rules of thumb, or on pre-determined orders that might not be best for the failure occurrence. With multiple workloads and recovery techniques, the number of possibilities is large, so the decision process is not trivial.This paper makes several contributions to the area of data recovery scheduling. First, we formalize the description of potential recovery processes by defining recovery graphs. Recovery graphs explicitly capture alternative approaches for recovering workloads, including their recovery tasks, operational states, timing information and precedence relationships. Second, we formulate the data recovery scheduling problem as an optimization problem, where the goal is to find the schedule that minimizes the financial penalties due to downtime, data loss and vulnerability to subsequent failures. Third, we present several methods for finding optimal or near-optimal solutions, including priority-based, randomized and genetic algorithm-guided ad hoc heuristics. We quantitatively evaluate these methods using realistic storage system designs and workloads, and compare the quality of the algorithms' solutions to optimal solutions provided by a math programming formulation and to the solutions from a simple heuristic that emulates the choices made by human administrators. We find that our heuristics' solutions improve on the administrator heuristic's solutions, often approaching or achieving optimality.

This work considers the problem of hosting multiple third-party Internet services in a cost-effective manner so as to maximize a provider's business objective. For this purpose, we present a dynamic capacity management framework based on an optimization model, which links a cost model based on SLA contracts with an analytical queuing-based performance model, in an attempt to adapt the platform to changing capacity needs in real time. In addition, we propose a two-level SLA specification for different operation modes, namely, normal and surge, which allows for per-use service accounting with respect to requirements of throughput and tail distribution response time. The cost model proposed is based on penalties, incurred by the provider due to SLA violation, and rewards, received when the service level expectations are exceeded. Finally, we evaluate approximations for predicting the performance of the hosted services under two different scheduling disciplines, namely FCFS and processor sharing. Through simulation, we assess the effectiveness of the proposed approach as well as the level of accuracy resulting from the performance model approximations.

IWRAP, the imaging wind and rain airborne profiler, is the first high-resolution dual-hand airborne Doppler radar designed for studying the inner core of tropical cyclones (TCs). The system is designed to provide high-resolution, dual-polarized C and Ku-Band reflectivity and Doppler profiles of precipitation volume backscatter and ocean surface backscatter within the inner core precipitation bands of tropical cyclones (TCs). SFMR is a C-band nadir viewing radiometer used operationally to provide continuous estimates of the surface wind speed and title column average rain rate. Both IWRAP and SFMR arc operated from a National Oceanic and Atmospheric Administration (NOAA) WP-3D aircraft during missions through TCs and severe ocean storms. We present wind and reflectivity profiles of the atmospheric boundary layer within the inner-core of hurricane Isabel (2003) from IWRAP measurements. These profiles are the highest resolution measurements of the ABL winds of a hurricane ever obtained. The profiles are compared to coincident surface wind speed measurements provided by SFMR, flight level wind measurements and GPS dropsonde winds.

During the 2002 and 2003 ONR CBLAST Hurricane Program Field (HPF) and the NOAA/NESDIS Hurricane Ocean Winds and Rain Experiment, the University of Massachusetts (UMass) installed IWRAP, a conically scanning dual-band (C- and Ku-band), dual-polarized pencil-beam airborne Doppler radar that profiles the volume backscatter and Doppler velocity from rain and the backscatter from the ocean surface simultaneously at four incidence angles covering incidence angles from 25 to 55 degrees. From the measurements acquired during missions flown through hurricanes Gustav, Isadore, and Lili (2002) and Fabian and Isabel (2003), high wind regime Geophysical Model Functions have been derived at both frequencies and polarizations. Concrete saturation effects in the NRCS are presented, and sensitivity in the wind direction at high wind speed is discussed through the analysis of the second harmonic of the NRCS.

We present a strategy for automatic negotiation that takes the same approach as computer programs that play games such as chess; we build the game tree. For every offer we look at every counteroffer, every counteroffer to each of them, and so on. The strategy then selects the counteroffer that has the largest expected payoff. A number of problems arise that are unique to using this strategy for negotiation. These include uncertainty in the opponent’s goals, the fact that a bad move can penalize both players, and moves that are continuous, as opposed to discrete. We show how the standard methods of building the tree and evaluating the results were adapted to this environment.