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理解我们过去的行为，预测我们未来的行为，并利用这些预测来影响结果。 例如，产品推荐系统表示用户与他们的项目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项目。 使用复杂网络进行推理的潜在好处是巨大的。 它们已经用于以下应用：交通拥堵、传染病和安全威胁分析。 从复杂网络中推断属性的一个关键挑战涉及在复杂网络上有效地执行查询。 这是困难的，因为未来复杂网络的规模预计将从数百到数千PB增长。 这带来了一个有趣的观察，即用于查询复杂网络的未来计算平台将访问大量数据，而不是执行大量计算。 这意味着复杂的网络工作负载将不再由计算主导，而是由数据访问主导。 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大量查询时间。因此，该研究计划的长期愿景是研究计算平台的设计，以快速有效地查询大型复杂网络。 博士帕特尔在这份提案中提出了三个主题。第一个主题研究数据局部性的概念，最能代表复杂网络中的邻近性，称为图局部性。 Patel博士认为，在复杂网络上的查询并没有表现出传统的空间和时间局部性概念。因此，发现图局部性度量将提供重新设计内存层次结构以利用这些度量的见解。 第二个主题探讨了大型复杂网络的数据压缩，以减少计算平台中存储器之间移动的数据量。 由于压缩整个网络是不切实际的，帕特尔博士将发现使用图局部性选择性压缩的技术。 第三个主题建议将计算移动到更接近数据的地方，而不是传统的方法，需要将数据移动到计算中进行处理。 这显然遵循了这样的观察，即未来的复杂网络工作负载将是数据访问繁重而不是计算繁重的。研究方法采取了经验，理论和实施组成部分的平衡的方法。这项研究将产生高质量的出版物，八名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。 加拿大的计算平台设计和大数据软件设计行业将受益匪浅。

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.