In-Memory Data Analytics on Coupled CPU-GPU Architectures

In the big data era, in-memory data analytics is an effective means of achieving high performance data processing and realizing the value of data in a timely manner. Efforts in this direction have been spent on various aspects, including in-memory algorithmic designs and system optimizations. In this paper, we propose to develop the next-generation in-memory relational database processing techniques on coupled CPU-GPU architectures. Particularly, we demonstrate novel design and implementations of query processing paradigms to utilize the strengths of coupled CPU-GPU architectures such as shared main memory and cache hierarchy. We propose a fine-grained method to distribute workload onto available processors, since the CPU and the GPU share the same main memory space. Besides, we propose an in-cache paradigm for query processing to take advantage of shared cache hierarchy to overcome memory stalls of query processing. Our experimental results demonstrate that 1) the proposed finegrained and in-cache query processing significantly improve the performance of in-memory databases, and 2) such coupled architectures are more energy efficient in query processing compared with other discrete systems.