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Parallel Processing01:20

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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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The parallel-axis theorem provides a convenient and quick method of finding the moment of inertia of an object about an axis parallel to the axis passing through its center of mass. Consider a thin rod as an example. There is a striking similarity between the process of finding the moment of inertia of a thin rod about an axis through its middle, where the center of mass lies, and about an axis through its end using the conventional method. In the conventional method, the concept of linear mass...
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The moment of inertia is a fundamental concept in mechanical engineering that plays a significant role in designing rotationally symmetric objects such as flywheels, gears, and other mechanical systems. In this context, we will discuss the moment of inertia of a flywheel rotating about its centroidal axis and how it relates to the moment of inertia about an axis parallel to it.
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随机MScan:通过矩阵计算和随机跳跃策略加速并行扫描.

Shujun Peng1, Xinhan Lin2, Yu Zhang3,1

  • 1Tsinghua University, Beijing, 100084, China.

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此摘要是机器生成的。

RandMScan为人工智能加速器提供了一种新的两阶段并行扫描框架. 这种方法通过减少通信开销,显著加快了长序列处理的速度,优于现有的基于矩阵的技术.

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能

背景情况:

  • 平行扫描对于各种计算任务至关重要,如排序和大型语言模型推断.
  • 当前的GPU优化扫描在缺乏矢量单位但具有处理元件 (PE) 阵列的AI加速器上是低效的.
  • 现有的基于矩阵的方法面临带宽和通信挑战,限制了可扩展性.

研究的目的:

  • 提出RandMScan,一个针对PE阵列的AI加速器优化的并行扫描框架.
  • 在现代硬件上解决现有扫描算法的低效率和可扩展性限制.
  • 在以人工智能为中心的工作负载中提高扫描操作的性能.

主要方法:

  • 开发了一个两阶段并行扫描框架:RandMScan.
  • 第一阶段使用基于矩阵的本地块扫描,利用PE阵列并行性.
  • 第二阶段采用随机跳跃策略,以实现高效的全球聚合,同时减少同步.

主要成果:

  • 与现有的基于矩阵的方法相比,RandMScan实现了超过80%的加快速度,用于长序列.
  • 在下游应用中将端到端延迟降低15% - 26%.
  • 在较长的序列中展示可扩展的执行,并减轻通信开销.

结论:

  • 兰德MScan有效地利用AI加速器PE阵列进行高效的并行扫描.
  • 拟议的框架克服了以前解决方案的带宽和通信瓶.
  • RandMScan为AI硬件上的扫描原始体提供了一个可扩展和高性能替代方案.