Many commercial and scientific applications are becoming more data intensive than ever before. The volume of the data and the pressure on the runtime system capability of supporting data intensive operations substantially increases over the time. This project introduces and optimizes data processing capabilities within memory. The project provides a fundamental change to data management and program optimization, and brings promising performance and energy benefits.
The goal of the project is to enable high-performance, energy-efficient, and flexible processing-in-memory design, which is adaptive to the irregular, diverse, and changing behaviors among data intensive scientific applications. To achieve the goal, a heterogeneous processing-in-memory design is introduced. The project explores a series of critical questions for building emerging processing-in-memory, including heterogeneous processing-in-memory architecture, processing-in-memory programming models, runtime design, and the implications of processing-in-memory on high performance scientific applications.
- Dong Li (Faculty)
- Hanlin He (PhD student)
- . In International Symposium on Memory Systems.
- . In 5th Workshop on Heterogeneous and Unconventional Cluster Architectures and Applications.
This research is based on work supported by NSF. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the sponsor.