Accelerating scientific codes by performance and accuracy modeling

Scientific software is often driven by multiple parameters that affect both accuracy and performance. Since finding the optimal configuration of these parameters is a highly complex task, it extremely common that the software is used suboptimally. In a typical scenario, accuracy requirements are imposed, and attained through suboptimal performance. In this paper, we present a methodology for the automatic selection of parameters for simulation codes, and a corresponding prototype tool. To be amenable to our methodology, the target code must expose the parameters affecting accuracy and performance, and there must be formulas available for error bounds and computational complexity of the underlying methods. As a case study, we consider the particle-particle particle-mesh method (PPPM) from the LAMMPS suite for molecular dynamics, and use our tool to identify configurations of the input parameters that achieve a given accuracy in the shortest execution time. When compared with the configurations suggested by expert users, the parameters selected by our tool yield reductions in the time-to-solution ranging between 10% and 60%. In other words, for the typical scenario where a fixed number of core-hours are granted and simulations of a fixed number of timesteps are to be run, usage of our tool may allow up to twice as many simulations. While we develop our ideas using LAMMPS as computational framework and use the PPPM method for dispersion as case study, the methodology is general and valid for a range of software tools and methods

Accuracy of Tilt Rotor Hover Performance Predictions

The accuracy of various methods used to predict tilt rotor hover performance was established by comparing predictions with large-scale experimental data. A wide range of analytical approaches were examined. Blade lift was predicted with a lifting line analysis, two lifting surface analyses, and by a finite-difference solution of the full potential equation. Blade profile drag was predicted with two different types of airfoil tables and an integral boundary layer analysis. The inflow at the rotor was predicted using momentum theory, two types of prescribed wakes, and two free wake analyses. All of the analyses were accurate at moderate thrust coefficients. The accuracy of the analyses at high thrust coefficients was dependent upon their treatment of high sectional angles of attack on the inboard sections of the rotor blade. The analyses which allowed sectional lift coefficients on the inboard stations of the blade to exceed the maximum observed in two-dimensional wind tunnel tests provided better accuracy at high thrust coefficients than those which limited lift to the maximum two-dimensional value. These results provide tilt rotor aircraft designers guidance on which analytical approaches provide the best results, and the level of accuracy which can be expected from the best analyses

Improve the performance of transfer learning without fine-tuning using dissimilarity-based multi-view learning for breast cancer histology images

Breast cancer is one of the most common types of cancer and leading cancer-related death causes for women. In the context of ICIAR 2018 Grand Challenge on Breast Cancer Histology Images, we compare one handcrafted feature extractor and five transfer learning feature extractors based on deep learning. We find out that the deep learning networks pretrained on ImageNet have better performance than the popular handcrafted features used for breast cancer histology images. The best feature extractor achieves an average accuracy of 79.30%. To improve the classification performance, a random forest dissimilarity based integration method is used to combine different feature groups together. When the five deep learning feature groups are combined, the average accuracy is improved to 82.90% (best accuracy 85.00%). When handcrafted features are combined with the five deep learning feature groups, the average accuracy is improved to 87.10% (best accuracy 93.00%)

Space-Time Reference with an Optical Link

We describe a method for realizing a high-performance Space-Time Reference (STR) using a stable atomic clock in a precisely defined orbit and synchronizing the orbiting clock to high-accuracy atomic clocks on the ground. The synchronization would be accomplished using a two-way lasercom link between ground and space. The basic concept is to take advantage of the highest-performance cold-atom atomic clocks at national standards laboratories on the ground and to transfer that performance to an orbiting clock that has good stability and that serves as a "frequency-flywheel" over time-scales of a few hours. The two-way lasercom link would also provide precise range information and thus precise orbit determination (POD). With a well-defined orbit and a synchronized clock, the satellite cold serve as a high-accuracy Space-Time Reference, providing precise time worldwide, a valuable reference frame for geodesy, and independent high-accuracy measurements of GNSS clocks. With reasonable assumptions, a practical system would be able to deliver picosecond timing worldwide and millimeter orbit determination

Neural markers of performance states in an Olympic athlete: An EEG case study in air-pistol shooting

This study focused on identifying the neural markers underlying optimal and suboptimal performance experiences of an elite air-pistol shooter, based on the tenets of the multi-action plan (MAP) model. According to the MAP model’s assumptions, skilled athletes’ cortical patterns are expected to differ among optimal/automatic (Type 1), optimal/controlled (Type 2), suboptimal/controlled (Type 3), and suboptimal/automatic (Type 4) performance experiences. We collected performance (target pistol shots), cognitive-affective (perceived control, accuracy, and hedonic tone), and cortical activity data (32-channel EEG) of an elite shooter. Idiosyncratic descriptive analyses revealed differences in perceived accuracy in regard to optimal and suboptimal performance states. Event-Related Desynchronization/Synchronization analysis supported the notion that optimal-automatic performance experiences (Type 1) were characterized by a global synchronization of cortical arousal associated with the shooting task, whereas suboptimal controlled states (Type 3) were underpinned by high cortical activity levels in the attentional brain network. Results are addressed in the light of the neural efficiency hypothesis and reinvestment theory. Perceptual training recommendations aimed at restoring optimal performance levels are discussed

The MVA Priority Approximation

A Mean Value Analysis (MVA) approximation is presented for computing the average performance measures of closed-, open-, and mixed-type multiclass queuing networks containing Preemptive Resume (PR) and nonpreemptive Head-Of-Line (HOL) priority service centers. The approximation has essentially the same storage and computational requirements as MVA, thus allowing computationally efficient solutions of large priority queuing networks. The accuracy of the MVA approximation is systematically investigated and presented. It is shown that the approximation can compute the average performance measures of priority networks to within an accuracy of 5 percent for a large range of network parameter values. Accuracy of the method is shown to be superior to that of Sevcik's shadow approximation