Runtime Improvements Research Articles

300-FPS Salient Object Detection via Minimum Directional Contrast.

Global contrast considers the color difference between a target region or pixel and the rest of the image. It is frequently used to measure the saliency of the region or pixel. In previous global contrast-based methods, saliency is usually measured by the sum of contrast from the entire image. We find that the spatial distribution of contrast is one important cue of saliency that is neglected by previous works. Foreground pixel usually has high contrast from all directions, since it is surrounded by the background. Background pixel often shows low contrast in at least one direction, as it has to connect to the background. Motivated by this intuition, we first compute directional contrast from different directions for each pixel, and propose minimum directional contrast (MDC) as raw saliency metric. Then an O(1) computation of MDC using integral image is proposed. It takes only 1.5 ms for an input image of the QVGA resolution. In saliency post-processing, we use marker-based watershed algorithm to estimate each pixel as foreground or background, followed by one linear function to highlight or suppress its saliency. Performance evaluation is carried on four public data sets. The proposed method significantly outperforms other global contrast-based methods, and achieves comparable or better performance than the state-of-the-art methods. The proposed method runs at 300 FPS and shows six times improvement in runtime over the state-of-the-art methods.

COMPARATIVE ANALYSIS AND IMPLEMENTATION OF DIJKSTRA'S SHORTEST PATH ALGORITHM FOR EMERGENCY RESPONSE AND LOGISTIC PLANNING

TransRoute: a web-based vehicle route planning application is proposed in this paper. This application leverages existing input-output (I/O) efficient implementations of shortest path algorithms (SPAs) to implement the proposed system that will fundamentally address the problems experienced in moving people, goods and services from one location to another. A number of SPAs are evaluated using landau notations. Main functionalities of the system will be implemented as a web-enabled geographic information system (GIS) application based on open-source technologies and object-oriented software development methodology using unified modeling language. Pilot implementation is done based on spatial data of three selected states in Nigeria, pulled from web-based mapping tools like Google Maps and Microsoft Bings respectively. In conclusion, the Dijkstra's algorithm implemented with double bucket dynamic data structure is selected for implementing the proposed route planning system, as past research efforts has proven that it is the fastest with run-time improvements from O(m + n/log C) to O(m) respectively. Â http://dx.doi.org/10.4314/njt.v36i3.30

COMPARATIVE ANALYSIS AND IMPLEMENTATION OF DIJKSTRA'S SHORTEST PATH ALGORITHM FOR EMERGENCY RESPONSE AND LOGISTIC PLANNING

TransRoute: a web-based vehicle route planning application is proposed in this paper. This application leverages existing input-output (I/O) efficient implementations of shortest path algorithms (SPAs) to implement the proposed system that will fundamentally address the problems experienced in moving people, goods and services from one location to another. A number of SPAs are evaluated using landau notations. Main functionalities of the system will be implemented as a web-enabled geographic information system (GIS) application based on open-source technologies and object-oriented software development methodology using unified modeling language. Pilot implementation is done based on spatial data of three selected states in Nigeria, pulled from web-based mapping tools like Google Maps and Microsoft Bings respectively. In conclusion, the Dijkstra's algorithm implemented with double bucket dynamic data structure is selected for implementing the proposed route planning system, as past research efforts has proven that it is the fastest with run-time improvements from O(m + n/log C) to O(m) respectively. http://dx.doi.org/10.4314/njt.v36i3.30

Weapon–Target-Allocation Strategies in Multiagent Target–Missile–Defender Engagement

A cooperative interception scenario between multiple maneuvering targets, their defenders, and missiles attacking the targets is considered. To assign defenders to missiles, several computationally efficient weapon–target-allocation algorithms are developed. The algorithms use the adjoint system representation and are based on evaluating a figure of merit for the possible allocations of defenders to missiles, given that the allocation of missiles to targets is known. Furthermore, an optimal cooperative guidance law for the target is developed and, together with the weapon–target-allocation algorithms, evaluated in a nonlinear simulation setting. A comparison to the optimal allocation demonstrates the fact that the proposed weapon–target-allocation algorithms can provide close-to-optimal performance with a significant computational runtime improvement.

A Coherence-Based Algorithm for Optimizing Rank-1 Grassmannian Codebooks

An iterative algorithm for the numerical optimization of rank-1 Grassmannian codebooks is presented. This algorithm achieves the same results as the current state-of-the-art algorithm, but requires a median of 9.52 times less computation time. This improvement is achieved by reformulating the optimization problem to directly minimize coherence instead of maximizing the Euclidean distance between codewords, thereby removing the need to consider large numbers of complex antipodals. The runtime improvement achieved allows the optimization of the larger codebooks required in many applications.

Numerical integration methods and layout improvements in the context of dynamic RNA visualization

BackgroundRNA visualization software tools have traditionally presented a static visualization of RNA molecules with limited ability for users to interact with the resulting image once it is complete. Only a few tools allowed for dynamic structures. One such tool is jViz.RNA. Currently, jViz.RNA employs a unique method for the creation of the RNA molecule layout by mapping the RNA nucleotides into vertexes in a graph, which we call the detailed graph, and then utilizes a Newtonian mechanics inspired system of forces to calculate a layout for the RNA molecule. The work presented here focuses on improvements to jViz.RNA that allow the drawing of RNA secondary structures according to common drawing conventions, as well as dramatic run-time performance improvements. This is done first by presenting an alternative method for mapping the RNA molecule into a graph, which we call the compressed graph, and then employing advanced numerical integration methods for the compressed graph representation.ResultsComparing the compressed graph and detailed graph implementations, we find that the compressed graph produces results more consistent with RNA drawing conventions. However, we also find that employing the compressed graph method requires a more sophisticated initial layout to produce visualizations that would require minimal user interference. Comparing the two numerical integration methods demonstrates the higher stability of the Backward Euler method, and its resulting ability to handle much larger time steps, a high priority feature for any software which entails user interaction.ConclusionThe work in this manuscript presents the preferred use of compressed graphs to detailed ones, as well as the advantages of employing the Backward Euler method over the Forward Euler method. These improvements produce more stable as well as visually aesthetic representations of the RNA secondary structures. The results presented demonstrate that both the compressed graph representation, as well as the Backward Euler integrator, greatly enhance the run-time performance and usability. The newest iteration of jViz.RNA is available at https://jviz.cs.sfu.ca/download/download.html.

Flow-resistive Inspiratory Muscle Training Improves Running Time To Exhaustion With Thoracic Load Carriage

Exercise while carrying an external load upon the thoracic cavity imposes extra stress on the cardiopulmonary and limb locomotor systems. This in turn, negatively impacts exercise tolerance and performance, as well as pulmonary and respiratory muscle function. Thoracic load carriage exercise (LC) has been shown to induce global respiratory muscle fatigue as assessed by volitional mouth pressures, with a concomitant impairment in running time-trial performance. Inspiratory muscle training (IMT) has been shown to improve performance in a running time-trial with thoracic LC. However, in many occupational and recreational activities that require thoracic LC, the capacity to sustain prolonged exercise may be of equal or greater importance than performance in a time-trial. PURPOSE: To determine the efficacy of 6 weeks of flow-resistive IMT on running time to exhaustion with thoracic LC; and to determine whether 6 weeks of flow-resistive IMT moderates diaphragmatic fatigue that may occur following a thoracic LC running time to exhaustion test. METHODS: Twelve recreationally active males completed two runs to exhaustion (Tlim) at a fixed speed eliciting 70 % of V˙O2max while carrying a 10 kg backpack. Visits were completed at baseline and after 6 weeks of either IMT or placebo-IMT. Exercise metabolic and ventilatory measures were recorded and diaphragm strength was measured using bilateral phrenic nerve stimulation in conjunction with esophageal balloon-tipped catheters to measure intrathoracic pressures. Twitch transdiaphragmatic pressure amplitude was recorded pre-exercise and immediately post-exercise in both trials. Maximal volitional mouth pressures (PImax) were recorded at baseline and post-IMT. RESULTS: 6 weeks of IMT significantly improved Tlim (p = 0.029, %Δ +29.3 ± 6.4 % IMT, -8.8 ± 11.1 %), but did not alter the change in diaphragm strength following a run to exhaustion (p > 0.05). The %Δ in PImax from pre- to post-training was significantly correlated with the %Δ in Tlim from pre- to post-training (p = 0.031, r = 0.622). No changes were observed in minute ventilation or breathing mechanics (all p > 0.05). CONCLUSIONS: IMT improves exercise tolerance with thoracic LC, but does not attenuate the severity of diaphragmatic fatigue following a running time to exhaustion test.

Robust and fast nonlinear optimization of diffusion MRI microstructure models

Advances in biophysical multi-compartment modeling for diffusion MRI (dMRI) have gained popularity because of greater specificity than DTI in relating the dMRI signal to underlying cellular microstructure. A large range of these diffusion microstructure models have been developed and each of the popular models comes with its own, often different, optimization algorithm, noise model and initialization strategy to estimate its parameter maps. Since data fit, accuracy and precision is hard to verify, this creates additional challenges to comparability and generalization of results from diffusion microstructure models. In addition, non-linear optimization is computationally expensive leading to very long run times, which can be prohibitive in large group or population studies. In this technical note we investigate the performance of several optimization algorithms and initialization strategies over a few of the most popular diffusion microstructure models, including NODDI and CHARMED. We evaluate whether a single well performing optimization approach exists that could be applied to many models and would equate both run time and fit aspects. All models, algorithms and strategies were implemented on the Graphics Processing Unit (GPU) to remove run time constraints, with which we achieve whole brain dataset fits in seconds to minutes. We then evaluated fit, accuracy, precision and run time for different models of differing complexity against three common optimization algorithms and three parameter initialization strategies. Variability of the achieved quality of fit in actual data was evaluated on ten subjects of each of two population studies with a different acquisition protocol. We find that optimization algorithms and multi-step optimization approaches have a considerable influence on performance and stability over subjects and over acquisition protocols. The gradient-free Powell conjugate-direction algorithm was found to outperform other common algorithms in terms of run time, fit, accuracy and precision. Parameter initialization approaches were found to be relevant especially for more complex models, such as those involving several fiber orientations per voxel. For these, a fitting cascade initializing or fixing parameter values in a later optimization step from simpler models in an earlier optimization step further improved run time, fit, accuracy and precision compared to a single step fit. This establishes and makes available standards by which robust fit and accuracy can be achieved in shorter run times. This is especially relevant for the use of diffusion microstructure modeling in large group or population studies and in combining microstructure parameter maps with tractography results.

Enhanced Ant Colony-Inspired Parallel Algorithm to Improve Cryptographic PRNGs

We present and motivate a parallel algorithm to compute promising candidate states for modifying the state space of a pseudo-random number generator in order to increase its cycle length. This is important for generators in low-power devices where increase of state space to achieve longer cycles is not an alternative. The runtime of the parallel algorithm is improved by an analogy to ant colony behavior: if two paths meet, the resulting path is followed at accelerated speed just as ants tend to reinforce paths that have been used by other ants. We evaluate our algorithm with simulations and demonstrate high parallel efficiency that makes the algorithm well-suited even for massively parallel systems like GPUs. Furthermore, the accelerated path variant of the algorithm achieves a runtime improvement of up to 4% over the straightforward implementation.1

Automatic algorithm transformation for efficient multi-snapshot analytics on temporal graphs

Analytical graph algorithms commonly compute metrics for a graph at one point in time. In practice it is often also of interest how metrics change over time, e.g., to find trends. For this purpose, algorithms must be executed for multiple graph snapshots. We present Single Algorithm Multiple Snapshots (SAMS) , a novel approach to execute algorithms concurrently for multiple graph snapshots. SAMS automatically transforms graph algorithms to leverage similarities between the analyzed graph snapshots. The automatic transformation interleaves algorithm executions on multiple snapshots, synergistically shares their graph accesses and traversals, and optimizes the algorithm's data layout. Thus, SAMS can amortize the cost of random data accesses and improve memory bandwidth utilization---two main cost factors in graph analytics. We extensively evaluate SAMS using six well-known algorithms and multiple synthetic as well as real-world graph datasets. Our measurements show that in multi-snapshot analyses, SAMS offers runtime improvements of up to two orders of magnitude over traditional snapshot-at-a-time execution.

Multi-Period Portfolio Optimization with Cone Constraints and Discrete Decisions

In this study, we consider multi-period portfolio optimization model that is formulated as a mixed-integer second-order cone programming problems (MISOCPs). The Markowitz (1952) mean/variance framework has been extended by including transaction costs, conditional value-at-risk (CVaR), diversification-by-sector and buy-in thresholds constraints. The model is obtained using a binary scenario tree that is constructed with monthly returns of the stocks from the S&P 500. We solve these models with a MATLAB based Mixed Integer Linear and Nonlinear Optimizer (MILANO). Numerical results show that we can solve small to medium-sized instances successfully, and we provide a substantial improvement in runtimes using warmstarts in outer approximation algorithm.

A flow based pruning scheme for enumerative equitable coloring algorithms

An equitable graph coloring is a proper vertex coloring of a graph G where the sizes of the color classes differ by at most one. The equitable chromatic number, denoted by $$\chi _{eq}(G),$$ is the smallest number k such that G admits such equitable k-coloring. We focus on enumerative algorithms for the computation of $$\chi _{eq}(G)$$ and propose a general scheme to derive pruning rules for them: We show how the extendability of a partial coloring into an equitable coloring can be modeled via network flows. Thus, we obtain pruning rules which can be checked via flow algorithms. Computational experiments show that the search tree of enumerative algorithms can be significantly reduced in size by these rules and, in most instances, such naive approach even yields a faster algorithm. Moreover, the stability, i.e., the number of solved instances within a given time limit, is greatly improved. Since the execution of flow algorithms at each node of a search tree is time consuming, we derive arithmetic pruning rules (generalized Hall-conditions) from the network model. Adding these rules to an enumerative algorithm yields an even larger runtime improvement.

Systematic Design Space Exploration of Floating-Point Expressions on FPGA

In this brief, we propose novel methods to systematically explore the design space of floating-point expressions on a field-programmable gate array in order to extract a nondominated set that covers the whole design space in terms of the accuracy, area, and run-time numbers. We first introduce a regular selection method that consists of two main phases: 1) generating equivalent expressions and 2) using a Pareto algorithm to extract a nondominated set. Then, we introduce a fast selection method in which two phases are composed in order to speed up the optimization process, while the quality of the final results does not change significantly. The results show that regular selection enjoys up to a 35.3% run-time improvement in comparison with the state-of-the-art techniques. Furthermore, the fast selection method is approximately 45× faster than the regular selection.

The effects of 20-m repeated sprint training on aerobic capacity in college volleyball players

Summary Study aim: The purpose of this study was to examine the effects of a repeated sprint training program in addition to volleyball training on the aerobic capacity of college volleyball players. Materials and methods: Eighteen male volleyball players were randomly assigned to either an experimental group (n = 9, age: 21.2 ± 1.3 years) or a control (n = 9, age: 21.2 ± 1.6 years) group. Both groups followed a traditional volleyball training program three times per week for 6 weeks. The experimental group additionally performed a repeated sprint training protocol immediately before each volleyball training session. The repeated sprint training consisted of 1-3 sets of 5 × 20 m maximal sprints with 20 seconds of active recovery between sprints and 4 min of passive recovery between sets. Before and after the 6-week training period, all participants performed an incremental treadmill test to determine maximal oxygen uptake (VO2max) and time to exhaustion, and the repeated sprint test (10 × 20 m with a 20‑second recovery between each sprint). Results: The experimental group showed significant improvements in VO2max (+7.1 ± 4.8%; p = 0.001) and running time to exhaustion (+15.8 ± 6.8%; p = 0.004) after training. The best 20-m sprint time (−2.3 ± 2.5%; p = 0.029), mean sprint time (−5.3 ± 3.1%; p = 0.001) and fatigue index (−34.1 ± 28.2%; p = 0.012) also improved significantly in the experimental group. None of these variables changed significantly in the control group (p > 0.05). Conclusions: The current findings indicate that the addition of a repeated sprint training program can improve both the aerobic capacity and anaerobic performance of college volleyball players.

On dynamic algorithms for factorization invariants in numerical monoids

Studying the factorization theory of numerical monoids relies on understanding several important factorization invariants, including length sets, delta sets, and $\omega$-primality. While progress in this field has been accelerated by the use of computer algebra systems, many existing algorithms are computationally infeasible for numerical monoids with several irreducible elements. In this paper, we present dynamic algorithms for the factorization set, length set, delta set, and $\omega$-primality in numerical monoids and demonstrate that these algorithms give significant improvements in runtime and memory usage. In describing our dynamic approach to computing $\omega$-primality, we extend the usual definition of this invariant to the quotient group of the monoid and show that several useful results naturally extend to this broader setting.

Faster Algorithms for Isomer Network Generation.

Isomer networks provide a mechanism to understand and interpret relationships between organic molecules with applications in medicinal chemistry and drug design. The extraction of isomer networks is a time- and data-intensive computation (e.g., we have experimentally determined the space required for the computation of a set of 25 isomers of nicotine to be 205 MB; extrapolating this, we have projected the computation to require 8 TB of storage for a set of 1 050 219 isomers of nicotine). In this paper we describe our efforts to improve the network extraction process by using the symmetry present in most molecules to reduce runtime and memory and streamlining the algorithm used for the detection of duplicate dnNames. Together, these techniques result in reductions in memory of up to 60% and improvements in runtime of up to a factor of 100.

Genetic-Algorithm-Based FPGA Architectural Exploration Using Analytical Models

FPGA architectural optimization has emerged as one of the most important digital design challenges. In recent years, experimental methods have been replaced by analytical ones to find the optimized architecture. Time is the main reason for this replacement. Conventional Geometric Programming (GP) is a routine framework to solve analytical models, including area, delay, and power models. In this article, we discuss the application of the Genetic Algorithm (GA) to the design of FPGA architectures. The performance model has been integrated into the Genetic Algorithm framework in order to investigate the impact of various architectural parameters on the performance efficiency of FPGAs. This way, we are able to rapidly analyze FPGA architectures and select the best one. The main advantages of using GA versus GP are concurrency and speed. The results show that concurrent optimization of high-level architecture parameters, including lookup table size ( K ) and cluster size ( N ), and low-level parameters, like scaling of transistors, is possible for GA, whereas GP does not capture K and N under its concurrency and it needs to exhaustively search all possible combinations of K and N . The results also show that more than two orders of magnitude in runtime improvement in comparison with GP-based analysis is achieved.

Precision performance surgery for CostgreSQL

We demonstrate how the compilation of SQL expressions into machine code leads to significant query runtime improvements in PostgreSQL 9. Our primary goal is to connect recent research in query code generation with one of the most widely deployed database engines. The approach calls on LLVM to translate arithmetic and filter expressions into native x86 instructions just before SQL query execution begins. We deliberately follow a non-invasive design that does not turn PostgreSQL on its head: interpreted and compiled expression evaluation coexist and both are used to execute the same query. We will bring an enhanced version of PostgreSQL that exhibits notable runtime savings and provides visual insight into exactly where and how execution plans can benefit from SQL expression compilation.

An efficient algorithm for finding the minimum energy path for cation migration in ionic materials.

The Nudged Elastic Band (NEB) is an established method for finding minimum-energy paths and energy barriers of ion migration in materials, but has been hampered in its general application by its significant computational expense when coupled with density functional theory (DFT) calculations. Typically, an NEB calculation is initialized from a linear interpolation of successive intermediate structures (also known as images) between known initial and final states. However, the linear interpolation introduces two problems: (1) slow convergence of the calculation, particularly in cases where the final path exhibits notable curvature; (2) divergence of the NEB calculations if any intermediate image comes too close to a non-diffusing species, causing instabilities in the ensuing calculation. In this work, we propose a new scheme to accelerate NEB calculations through an improved path initialization and associated energy estimation workflow. We demonstrate that for cation migration in an ionic framework, initializing the diffusion path as the minimum energy path through a static potential built upon the DFT charge density reproduces the true NEB path within a 0.2 Å deviation and yields up to a 25% improvement in typical NEB runtimes. Furthermore, we find that the locally relaxed energy barrier derived from this initialization yields a good approximation of the NEB barrier, with errors within 20 meV of the true NEB value, while reducing computational expense by up to a factor of 5. Finally, and of critical importance for the automation of migration path calculations in high-throughput studies, we find that the new approach significantly enhances the stability of the calculation by avoiding unphysical image initialization. Our algorithm promises to enable efficient calculations of diffusion pathways, resolving a long-standing obstacle to the computational screening of intercalation compounds for Li-ion and multivalent batteries.

Single- and Multi-Period Portfolio Optimization with Cone Constraints and Discrete Decisions

Portfolio optimization literature has come quite far in the decades since the first publication, and many modern models are formulated using second-order cone constraints and take discrete decisions into consideration. In this study we consider both single-period and multi-period portfolio optimization problems based on the Markowitz (1952) mean/variance framework, where there is a trade-off between expected return and the risk that the investor may be willing to take on. Our model is aggregated from current literature. In this model, we have included transaction costs, conditional value-at-risk (CVaR) constraints, diversification-by-sector constraints and buy-in-thresholds. Our numerical experiments are conducted on portfolios drawn from 20 to 400 different stocks available from the S&P 500 for the single period-model. The multi-period portfolio optimization model is obtained using a binary scenario tree that is constructed with monthly returns of the closing price of the stocks from the S&P 500. We solve these models with a MATLAB based Mixed Integer Linear and Nonlinear Optimizer (MILANO). We provide substantial improvement in runtimes using warmstarts in both branch-and-bound and outer approximation algorithms.