A REUSED DISTANCE BASED ANALYSIS AND OPTIMIZATION FOR GPU CACHE

As a throughput-oriented device, Graphics Processing Unit(GPU) has already integrated with cache, which is similar to CPU cores. However, the applications in GPGPU computing exhibit distinct memory access patterns. Normally, the cache, in GPU cores, suffers from threads contention and resources over-utilization, whereas few detailed works excavate the root of this phenomenon. In this work, we adequately analyze the memory accesses from twenty benchmarks based on reuse distance theory and quantify their patterns. Additionally, we discuss the optimization suggestions, and implement a Bypassing Aware(BA) Cache which could intellectually bypass the thrashing-prone candidates. BA cache is a cost efficient cache design with two extra bits in each line, they are flags to make the bypassing decision and find the victim cache line. Experimental results show that BA cache can improve the system performance around 20\% and reduce the cache miss rate around 11\% compared with traditional design

PHYFU: Fuzzing Modern Physics Simulation Engines

A physical simulation engine (PSE) is a software system that simulates physical environments and objects. Modern PSEs feature both forward and backward simulations, where the forward phase predicts the behavior of a simulated system, and the backward phase provides gradients (guidance) for learning-based control tasks, such as a robot arm learning to fetch items. This way, modern PSEs show promising support for learning-based control methods. To date, PSEs have been largely used in various high-profitable, commercial applications, such as games, movies, virtual reality (VR), and robotics. Despite the prosperous development and usage of PSEs by academia and industrial manufacturers such as Google and NVIDIA, PSEs may produce incorrect simulations, which may lead to negative results, from poor user experience in entertainment to accidents in robotics-involved manufacturing and surgical operations. This paper introduces PHYFU, a fuzzing framework designed specifically for PSEs to uncover errors in both forward and backward simulation phases. PHYFU mutates initial states and asserts if the PSE under test behaves consistently with respect to basic Physics Laws (PLs). We further use feedback-driven test input scheduling to guide and accelerate the search for errors. Our study of four PSEs covers mainstream industrial vendors (Google and NVIDIA) as well as academic products. We successfully uncover over 5K error-triggering inputs that generate incorrect simulation results spanning across the whole software stack of PSEs.Comment: This paper is accepted at The 38th IEEE/ACM International Conference on Automated Software Engineering, a.k.a. ASE 2023. Please cite the published version as soon as this paper appears in the conference publication

Influence of catwalk design parameters on the galloping of constructing main cables in long-span suspension bridges

A main cable of a long-span suspension bridge is semi-surrounded by a catwalk during construction. Thus, design parameters of a catwalk may have influences on the galloping stability of a main cable during construction. To study the influence of catwalk design parameters on the galloping of steepled main cables, two main foci have been conducted. Firstly, the aerodynamic coefficients of the catwalk with actual design parameters are obtained by numerical simulation based on computational fluid dynamics (CFD), and the numerical results are compared with those of the previous wind tunnel test. Several typical main cables with different cross sections of a long-span suspension bridge during construction are selected, and their Den Hartog coefficients are obtained based on the numerical simulation considering the aerodynamic influences of the catwalks. Then four typical working conditions of a main cable which have great potential to occur galloping are selected based on the galloping analyze, and their aerodynamic coefficients considering the influence of the catwalk with different design parameters are obtained. The influence of the catwalk design parameters on galloping of the main cables is analyzed based on the Den Hartog criterion. Results indicate that catwalk design parameters have evident influences on aerodynamic coefficients and galloping of the main cables. The parameters of the catwalk which are favorable for suppressing the galloping of the main cables are determined, which establish a good guideline for the galloping-resistant design of the catwalk-main cable system on suspension bridges

Optimal route design of electric transit networks considering travel reliability

Travel reliability is the most essential determinant for operating the transit system and improving its service level. In this study, an optimization model for the electric transit route network design problem is proposed, under the precondition that the locations of charging depots are predetermined. Objectives are to pursue maximum travel reliability and meanwhile control the total cost within a certain range. Constraints about the bus route and operation are also considered. A Reinforcement Learning Genetic Algorithm is developed to solve the proposed model. Two case studies including the classic Mandl\u27s road network and a large road network in the context of Zhengzhou city are conducted to demonstrate the effectiveness of the proposed model and the solution algorithm. Results suggest that the proposed methodology is helpful for improving the travel reliability of the transit network with minimal cost increase

Stochastic Simulation on System Reliability and Component Probabilistic Importance of Road Network

Because of the combination explosion problem, it is difficult to use probability analytical method to calculate the system reliability of large networks. The paper develops a stochastic simulation (Monte Carlo-based) method to study the system reliability and component probabilistic importance of the road network. The proposed method considers the characteristics of the practical road network as follows: both link (roadway segment) and node (intersection) components are emphasized in the road network; the reliability for a link or node component may be at the in-between state; namely, its reliability value is between 0 and 1. The method is then implemented using the object-oriented programming language C++ and integrated into a RARN-MGG (reliability analysis of road network using Monte Carlo, GIS, and grid) system. Finally, two numerical examples based on a simple road network and a large real road network, respectively, are carried out to characterize the feasibility and to demonstrate the strength of the stochastic simulation method

Contract-based Time-of-use Pricing for Energy Storage Investment

Time-of-use (ToU) pricing is widely used by the electricity utility. A carefully designed ToU pricing can incentivize end-users' energy storage deployment, which helps shave the system peak load and reduce the system social cost. However, the optimization of ToU pricing is highly non-trivial, and an improperly designed ToU pricing may lead to storage investments that are far from the social optimum. In this paper, we aim at designing the optimal ToU pricing, jointly considering the social cost of the utility and the storage investment decisions of users. Since the storage investment costs are users' private information, we design low-complexity contracts to elicit the necessary information and induce the proper behavior of users' storage investment. The proposed contracts only specify three contract items, which guides users of arbitrarily many different storage-cost types to invest in full, partial, or no storage capacity with respect to their peak demands. Our contracts can achieve the social optimum when the utility knows the aggregate demand of each storage-cost type (but not the individual user's type). When the utility only knows the distribution of each storage-cost type's demand, our contracts can lead to a near-optimal solution. The gap with the social optimum is as small as 1.5% based on the simulations using realistic data. We also show that the proposed contracts can reduce the system social cost by over 30%, compared with no storage investment benchmark