Computer Architecture Support for Complex Networks

复杂网络在我们今天的生活中扮演着重要的角色。它们可以用来更好地了解我们过去的行为，预测我们未来的行动，并使用这些预测来影响结果。例如，产品推荐系统代表用户与他们的商品数据库和过去的购买历史的交互，以建议用户可能感兴趣的未来商品。使用复杂网络进行推断的潜在好处是巨大的。它们已经被用于以下应用：交通拥堵、传染病和安全威胁分析。 从复杂网络推断属性的一个关键挑战是高效地在复杂网络上执行查询。这是困难的，因为未来复杂网络的规模预计将增长到数百到数千PB。这带来了一个有趣的观察，即未来用于查询复杂网络的计算平台将访问大量数据，而不是执行大量计算。这意味着复杂网络的工作负载将不是由计算主导的，而是对数据的访问。不幸的是，现代计算平台并不是基于这种观察而设计的，这将导致复杂网络上的查询时间很长。因此，该研究计划的长期愿景是研究大型复杂网络快速高效查询的计算平台的设计。 帕特尔博士在这项提案中提出了三个主题。第一个主题研究了数据局部性的概念，这些概念最能代表复杂网络中的邻近性，称为图局部性。Patel博士认为，复杂网络上的查询不表现出传统的空间和时间局部性概念。因此，发现图形局部性指标将为重新设计内存层次结构以利用这些指标提供见解。第二个主题探索大型复杂网络的数据压缩，以减少在计算平台的存储器之间移动的数据量。由于整个网络的压缩是不切实际的，帕特尔博士将发现使用图形局部性进行选择性压缩的技术。第三个主题建议将计算移动到更接近数据的位置，而不是需要将数据移动到计算以进行处理的传统方法。这显然是在观察到未来复杂的网络工作负载将是数据访问繁重而不是计算繁重的情况下。研究方法采取了平衡的方法，包括经验、理论和实施三个部分。这项研究将产生高质量的出版物，八个HQP将是它的直接结果。他们将在计算机体系结构、算法、数据库、系统和硬件设计方面拥有专业技能。加拿大在计算平台设计和大数据软件设计方面的行业将大大受益。

Complex networks play an important role in our lives today. They can be used to gain a better understanding of our actions in the past, predict our actions in the future, and use these predictions to influence outcomes.  For example, product recommendation systems represent a user's interactions with their database of items and past purchase histories to suggest future items that may be of interest to the user.  The potential benefits of using complex networks for inferences are enormous.  They are already in use for the following applications: traffic congestion, infectious disease and security threat analyses. A key challenge in inferring properties from complex networks involves performing queries on complex networks efficiently.  This is difficult because of the expected growth in size of future complex networks ranging from hundreds to thousands of petabytes.  This brings about an interesting observation that future computing platforms used to query complex networks will be accessing large amounts of data instead of performing large amounts of computation.  This means that complex network workloads will not be dominated by computation, but accesses to data.  Unfortunately, modern computing platforms are not designed based on this observation, which would result in large query times on complex networks. Consequently, the long-term vision of the research program is to investigate the design of computing platforms for fast and efficient querying of large complex networks. Dr. Patel proposes to address three topics in this proposal. The first topic investigates notions of data locality that best represent proximity in complex networks, termed graph-locality.  Dr. Patel believes that queries on complex networks do not exhibit traditional notions of spatial and temporal locality. Hence, discovering graph-locality metrics will provide insights into redesigning the memory hierarchy to exploit these metrics.  The second topic explores data compression for large complex networks to mitigate the amount of data to move between memories in computing platforms.  Since compression of entire networks is impractical, Dr. Patel will discover techniques for selective compression using graph-locality.  The third topic proposes moving computation closer to the data instead of traditional approaches that require moving data to computation for processing.  This clearly follows the observation that future complex network workloads will be data-access-heavy and not compute-heavy. The research methodology takes a balanced approach with empirical, theoretical and implementation components. The research will result in high-quality publications, and eight HQP will be a direct result of it. They will have expert skills in computer architecture, algorithms, databases, systems, and hardware design.  Canadian industries in computing platform design and big-data software design will greatly benefit.