Regina Célia P. Leal-Toledo

Automatic Dynamic Task Distribution between CPU and GPU for Real-Time Systems

The increase of computational power of programmable GPU (graphics processing unit) brings new concepts for using these devices for generic processing. Hence, with the use of the CPU and the GPU for data processing come new ideas that deals with distribution of tasks among CPU and GPU, such as automatic distribution. The importance of the automatic distribution of tasks between CPU and GPU lies in three facts. First, automatic task distribution enables the applications to use the best of both processors. Second, the developer does not have to decide which processor will do the work, allowing the automatic task distribution system to choose the best option for the moment. And third, sometimes, the application can be slowed down by other processes if the CPU or GPU is already overloaded. Based on these facts, this paper presents new schemes for efficient automatic task distribution between CPU and GPU. This paper also includes tests and results of implementing those schemes with a test case and with a real-time system.

An adaptative game loop architecture with automatic distribution of tasks between CPU and GPU

This article presents a new architecture to implement all game loop models for games and real-time applications that use the GPU as a mathematics and physics coprocessor, working in parallel processing mode with the CPU. The presented model applies automatic task distribution concepts. The architecture can apply a set of heuristics defined in Lua scripts in order to get acquainted with the best processor for handling a given task. The model applies the GPGPU (general-purpose computation on GPUs) paradigm. In this article we propose an architecture that acquires knowledge about the hardware by running tasks in each processor and, by studying their performance over time, finding the best processor for a group of tasks.

A game loop architecture for the GPU used as a math coprocessor in real-time applications

This article concerns the use of a graphics processor unit (GPU) as a math co-processor in real-time applications in special games and physics simulations. To validate this approach, we present a new game loop architecture that employs GPUs for general-purpose computations (GPGPUs). A critical issue here is the process distribution between the CPU and the GPU. The architecture consists of a model for distribution, and our implementation offers many advantages in comparison to other approaches without the GPGPU stage. This architecture can be used either by a general-purpose language such as the Compute Unified Device Architecture (CUDA), or shader languages such as the High-Level Shader Language (HLSL) and the OpenGL Shading Language (GLSL). Although the architecture proposed here aims at supporting mathematics and physics on the GPU, it is possible to adapt any kind of generic computation. This article discusses the model implementation in an open-source game engine and presents the results of using this platform.

A new stochastic cellular automata model for traffic flow simulation with drivers’ behavior prediction

Abstract In this work, we introduce a novel, flexible and robust traffic flow cellular automata model. Our proposal includes two important stages that make possible the consideration of different profiles of drivers’ behavior in a simple way. We first consider the motion expectation of vehicles that are in front of each driver. Secondly, we define how a specific vehicle decides to get around, considering the foreground traffic configuration. Our model uses stochastic rules for both situations, using the Probability Density Function of the Beta Distribution to model three drivers’ behavior, adjusting different parameters of the Beta Distribution for each one.

Performance Evaluation of Optimized Implementations of Finite Difference Method for Wave Propagation Problems on GPU Architecture

The scattering of acoustic waves in non-homogeneous media has been of practical interest for the petroleum industry, mainly in the determination of new oil deposits. A family of computational models that represent this phenomenon is based on finite difference methods. The simulation of these phenomena demands a high computational cost. In this work we employ GPU for the development of solvers for a 2D wave propagation problem with finite difference methods. Although there are many related works that use the same implementation presented in this paper, we propose a detailed and novel performance and memory bottleneck analysis for this hardware architecture.

An Architecture with Automatic Load Balancing and Distribution for Digital Games

Distributed computing is being used in several fields to solve many computation intensive problems. In digital games, it is used mainly in multi-player games, where the majority of the game logic is processed in a mainframe or cluster. Single player games could also use distributed computing to process the game logic, devoting host processing to renderization, which is usually the task that digital games spend most of its processing time. By using distributed computing, games could need softer system requirements, since the game loop would be distributed. This paper presents a game loop that can be applied in both multi-player and single-player games, using automatic load balancing and distributing game logic computation among several computers.

A Distributed Architecture for Mobile Digital Games Based on Cloud Computing

Several fields in Computer Science use distributed computing to solve many intensive computational problems. Digital games use this approach mainly in multiplayer games, where a mainframe or cluster processes the majority of game logic. Single player games can also use distribute computing to process game logic and visualization algorithms, usually the tasks where digital games spend most of the processing time. By applying an approach based on distributed computing, games would have softer requirements regarding hardware, since the network cluster would be responsible for processing parts of game loop tasks. With the concept of cloud computing, games could rely on other computers to aid in processing their tasks. This work presents game-loop architecture for single-player or multiplayer games, using automatic load balancing and distributing game logic computation among several computers.

A probabilistic cellular automata model for highway traffic simulation

Abstract This work presents a probabilistic model for the microscopic simulation of traffic roads based on Nagel-Schreckenberg’s model. Each driver’s behavior is described through the combination of a continuous probability function with an anticipatory feature that leads to a counter flow velocity tunning. The simulations developed and described herein give rise to a phase diagram which resembles and enriches the fundamental diagram, in its theoretical as well as for real data.

Finite Difference Method for Solving Acoustic Wave Equation Using Locally Adjustable Time-steps☆

Abstract Explicit finite difference method has been widely used for seismic modeling in heterogeneous media with strong discontinuities in physical properties. In such cases, due to stability considerations, the time step size is primarily determined by the medium with higher wave speed propagation, resulting that the higher the speed, the lower the time step needs to be to ensure stability throughout the whole domain. Therefore, the use of different temporal discretizations can greatly reduce the computational cost involved when solving this kind of problem. In this paper we propose an algorithm for the local temporal discretization setting named Region Triangular Transition (RTT), which allows the local temporal discretizations to be related by any integer value that enables these discretizations to operate at the stability limit of the finite difference approximations used.

A Novel Cellular Automaton Model for Traffic Freeway Simulation

This work introduces a novel Cellular Automata (CA) model applied for freeway traffic. Besides its capacity for representing basic traffic proprieties, it is capable of representing different drivers behaviors as well as its fluctuation and variation, based on the combination of acceleration and anticipation policy. Both policies are based on normal probabilistic function which represents the nature of unpredictable human behavior. The simulations developed and described herein give rise to a phase diagram which resembles and enriches the fundamental diagram, in its theoretical as well as for real data. Therefore, novel feature proposed herein is normal probabilistic function invoked in both policy (acceleration and anticipation), which allows for a simple group of rules with a few parameters.