Accelerating Data Analytics Through Emerging Software-Hardware Mechanisms

本研究的目标是研究支持高效实时事件数据分析平台的算法、设计和体系结构，以支持对大容量和高频事件流的多查询处理。为了实现这些目标，我们计划在我们的设计中利用现代硬件机制，例如现场可编程门阵列(FGA)和应用处理单元(APU)。我们努力通过开发前所未有的并行度和流水线的潜力来实现线速多查询处理，只有通过定制的、特定于应用程序的和低级别的逻辑设计才能做到这一点。此外，我们打算在我们的设计中与新兴图形处理单元(GPU)的使用进行比较，首先评估所有性能，然后评估开发工作。

The objectives of this research are to investigate algorithms, design, and architectures for enabling an efficient real-time event data analytics platform to support multi-query processing over high-volume and high-frequency event streams. To achieve these objectives, we plan to leverage modern hardware mechanisms, such as Field Programmable Gate Arrays (FPGAs) and Application Processing Units (APUs) in our design. We strive to achieve line-rate multi-query processing by exploiting unprecedented degrees of parallelism and potential for pipelining, only available through custom-built, application-specific and low-level logic design. Furthermore, we intend to compare against the use of emerging Graphical Processing Units (GPUs) in our design, evaluating above all performance, but also development effort.