DFM 2011 - First Workshop on Data-Flow Execution Models for Extreme Scale Computing

The purpose of DFM is to bring together researchers that are interested into novel computational model based on the Data-Flow principles of execution.

The switch to multi-core systems has elevated concurrency as a major issue in utilizing the ever increasing number of cores in a chip. Sequential computing has dominated the computer architecture landscape for five decades. Designers were able to design and build faster and faster computers by relying on improvements on fabrication technologies and architectural/organization optimizations. The most severe limitation of the sequential model, namely its inability to tolerate long latencies has slowed down the performance gains, forcing the industry to hit the Memory wall and to switch to multiple cores per chip and thus move into the concurrency era. New concurrent models/paradigms are needed in order to fully utilize the potential of Multi-core chips. The Data-flow model is a formal model that can handle concurrency and it can tolerate memory and synchronization latencies. Data-Flow inspired systems could also be simpler and more power efficient than conventional systems.

Recent work on Data-flow inspired systems has shown that the Data-Flow principles can be used to develop data-driven systems that can perform as good and in some cases outperform systems that are based on conventional techniques, running on commercial Multi-core systems. Thus, it is time to revisit Data-driven computation and bring it to the Multi-core and extreme scale computing.

DFM solicits novel papers that include but are not limited to:

Novel Data-Flow inspired Execution models and architectures
Functional and Single assignment based Languages.
Strict and non-strict execution models.
Compilers and tools for Data-Flow/Data-Driven systems.
Hybrid Data-driven/Control-Driven systems.
Survey papers on Data-Flow/Data-Driven systems.
Position Papers on the Future of Data-Flow in the Multi-core era and beyond.
Extended Versions of the best papers will be published in a special issue of the IJPP.