Computer Architecture Support for Complex Networks

复杂的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其商品数据库和过去购买历史的交互，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。从复杂网络推断属性的一个关键挑战是在复杂网络上高效地执行查询。这很困难，因为未来复杂网络的规模预计会增长，从数百到数千pb不等。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一观察结果设计的，这将导致复杂网络上的大量查询时间。因此，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设计。帕特尔建议在本提案中讨论三个主题。第一个主题研究了数据局部性的概念，它最能代表复杂网络中的接近性，称为图局部性。帕特尔博士认为，对复杂网络的查询不会表现出空间和时间局部性的传统概念。因此，发现图局部性指标将有助于深入了解如何重新设计内存层次结构以利用这些指标。第二个主题探讨了大型复杂网络的数据压缩，以减少计算平台中在内存之间移动的数据量。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局部性选择性压缩的技术。第三个主题提出将计算移动到更靠近数据的地方，而不是传统的需要将数据移动到计算中进行处理的方法。这显然是基于这样的观察：未来复杂的网络工作负载将是数据访问密集型的，而不是计算密集型的。研究方法采取平衡的方法与经验，理论和实施的组成部分。这项研究将产生高质量的出版物，8 HQP将是它的直接结果。他们将具备计算机体系结构、算法、数据库、系统和硬件设计方面的专业技能。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.**