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Physical Simulation for Animation and Visual Effects: Parallelization and Characterization for Chip Multiprocessors

机译:动画和视觉效果的物理模拟:芯片多处理器的并行化和表征

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We explore the emerging application area of physics-based simulation for computer animation and visual special effects. In particular, we examine its parallelization potential and characterize its behavior on a chip multiprocessor (CMP). Applications in this domain model and simulate natural phenomena, and often direct visual components of motion pictures. We study a set of three workloads that exemplify the span and complexity of physical simulation applications used in a production environment: fluid dynamics, facial animation, and cloth simulation. They are computationally demanding, requiring from a few seconds to several minutes to simulate a single frame; therefore, they can benefit greatly from the acceleration possible with large scale CMPs.rnStarting with serial versions of these applications, we parallelize code accounting for at least 96% of the serial execution time, targeting a large number of threads. We then study the most expensive modules using a simulated 64-core CMP.rnFor the code in key modules, we achieve parallel scaling of 45x, 50x, and 30x for fluid, face, and cloth simulations, respectively. The modules have a spectrum of task granularity and locking behavior, and all but one are dominated by loop-level parallelism. Many modules operate on streams of data. In some cases, modules iterate over their data, leading to significant temporal locality. This streaming behavior leads to very high on-die and main memory bandwidth requirements. Finally, most modules have little inter-thread communication since they are data-parallel, but a few require heavy communication between data-parallel operations.
机译:我们探索了基于物理的模拟技术在计算机动画和视觉特效方面的新兴应用领域。特别是,我们检查了其并行化潜力,并在芯片多处理器(CMP)上表征了其行为。该领域的应用程序可以模拟自然现象,并通常指导电影的视觉成分。我们研究了一组三个工作负载,这些工作负载示例了生产环境中使用的物理模拟应用程序的范围和复杂性:流体动力学,面部动画和布料模拟。它们对计算的要求很高,要模拟单个帧需要几秒钟到几分钟的时间。因此,它们可以从大规模CMP的加速中受益匪浅。从这些应用程序的串行版本开始,我们并行处理至少占串行执行时间96%的代码,并以大量线程为目标。然后,我们使用模拟的64核CMP研究最昂贵的模块。对于关键模块中的代码,对于流体,表面和布料模拟,我们分别实现了45x,50x和30x的并行缩放。这些模块具有一系列的任务粒度和锁定行为,除一个模块外,其他所有模块均由循环级并行性控制。许多模块对数据流进行操作。在某些情况下,模块会遍历其数据,从而导致明显的时间局部性。这种流传输行为导致非常高的片上和主存储器带宽需求。最后,大多数模块几乎没有线程间通信,因为它们是数据并行的,但是有些模块需要在数据并行操作之间进行大量的通信。

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