Hardware Accelerated Bio-Inspired Parallel Algorithms for Real World Applications

适用于现实世界应用的硬件加速仿生并行算法

• 批准号: RGPIN-2016-06052
• 负责人: Thulasiraman, Parimala
• 金额: $ 1.6万
• 依托单位: University of Manitoba
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=616074
• 关键词: Hardware; Accelerated; Bio; Inspired; Parallel

Consider an example such as Facebook where you may be connected to many friends. Let's represent a friend as a “node” (circle) and the connection between friends as a link (straight line). Imagine having billions of nodes and links. This representation is called a graph or network. There are many real world problems in sociology, neuroscience, medicine, etc., that be can be represented as graphs. There are many issues related to these graphs. For example, on Facebook we may want to cluster groups of people who share similar interests. Then the graph can be decomposed into clusters such that objects within a cluster have high similarity while objects between clusters have low similarity. This is called the clustering problem. Can we cluster a graph with billions of nodes and links? It is impossible to do this visually. My research focuses on solving such problems by borrowing ideas from nature. For example, ants clean up their nest (brood) by systematically collecting dead ants and piling them depending on their size and shape (called ant brooding). My research proposes solution to the clustering problem using ant brooding technique. This is quite challenging because we need to mathematically model the problem and create algorithms (step-by-step procedures) using ant brooding to solve the problem. Due to the large graph size, providing a solution fast on a single computer is difficult. Parallel computing involves using many computers to solve a given problem fast cooperatively. Today’s general-purpose computers (PC) come with not one CPU (central processing unit or processor) but with 2, 4 or 8 identical processors called cores, allowing simultaneous execution of many tasks. These days, graphical processing units (GPUs) or accelerators have become mainstream (e.g., used for games, video) with hundreds of processors providing lots of potential parallelism. The GPUs come as a single chip and can be installed on any PC. We can fuse CPU and accelerator together on a single chip, like in AMD Accelerated Processing Unit (APU), providing massive amount of parallelism. These are called many-core machines. There is a lot of parallelism within an ant colony. Each ant works independently (very parallel) and can self-organize quite fast. They communicate with each other indirectly (stigmergic communication), at the same time working cooperatively to solve a problem. Indirect communication allows for minimal global synchronization, an asset on parallel computers. Less synchronization means processors are busy doing computations increasing performance. I propose to use many-core machines to find an answer to the clustering problem fast. Therefore, the focus of this proposed research is on the design, development and performance evaluation of nature-inspired techniques to solve large real world problems on parallel computers. In this cycle of my Discovery Grant, I expect to train 3 Undergrad., 4 MSc and 7 PhD students.

考虑一个例子，如Facebook，你可能会连接到许多朋友。让我们将朋友表示为“节点”（圆），将朋友之间的连接表示为链接（直线）。想象一下有数十亿个节点和链接。这种表示被称为图或网络。在社会学、神经科学、医学等领域有许多真实的世界问题，可以用图表来表示。有许多问题与这些图表有关。例如，在Facebook上，我们可能希望将具有相似兴趣的人群聚集在一起。然后，图可以被分解成聚类，使得聚类内的对象具有高相似性，而聚类之间的对象具有低相似性。这称为集群问题。我们能把一个有数十亿个节点和链接的图聚类起来吗？在视觉上是不可能做到这一点的。我的研究重点是通过借鉴自然界的想法来解决这些问题。例如，蚂蚁通过系统地收集死蚂蚁并根据它们的大小和形状将它们堆积起来（称为蚂蚁育雏）来清理它们的巢穴（育雏）。我的研究提出了使用蚂蚁育雏技术解决聚类问题。这是相当具有挑战性的，因为我们需要对问题进行数学建模，并使用蚂蚁沉思来创建算法（逐步过程）来解决问题。 由于图的大小很大，在一台计算机上快速提供解决方案是困难的。并行计算是指使用多台计算机协同快速解决给定问题。今天的通用计算机（PC）不是一个CPU（中央处理器或处理器），而是2个，4个或8个相同的处理器，称为核心，允许同时执行许多任务。如今，图形处理单元（GPU）或加速器已经成为主流（例如，用于游戏、视频），具有数百个处理器，提供了大量潜在的并行性。GPU作为一个单一的芯片，可以安装在任何PC上。我们可以将CPU和加速器融合在一个芯片上，就像AMD加速处理单元（APU）一样，提供大量的并行性。这些被称为多核机器。 在蚁群中有很多的平行性。每只蚂蚁都独立工作（非常平行），并且可以快速自我组织。他们彼此之间进行间接的沟通（stigmergic communication），同时合作解决问题。间接通信允许最小的全局同步，这是并行计算机上的资产。更少的同步意味着处理器忙于忙碌，从而提高了性能。我建议使用众核机器来快速找到集群问题的答案。 因此，本研究的重点是设计，开发和性能评估的自然启发的技术，以解决大型真实的世界问题的并行计算机。在我的发现补助金的这个周期中，我希望培养3名本科生，4名硕士生和7名博士生。