GoDEL: A Multidirectional Dataflow Execution Model for Large-Scale Computing

Abhishek Kulkarni, Michael Lang, Andrew Lumsdaine
2011 2011 First Workshop on Data-Flow Execution Models for Extreme Scale Computing  
As the emerging trends in hardware architecture guided by performance, power efficiency and complexity drive us towards massive processor parallelism, there has been a renewed interest in dataflow models for large-scale computing. Dataflow programming models, being declarative in nature, lead to improved programmability at scale by implicitly managing the computation and communication for the application. In this paper, we present GoDEL, a multidirectional dataflow execution model based on
more » ... gation networks. Propagator networks allow general-purpose parallel computation on partial data. Implemented with efficiency and programmer productivity as its goals, we describe the syntax and semantics of the GoDEL language and discuss its implementation and runtime. We further discuss representative examples from various programming paradigms that are encompassed by and benefit from the flexibility in the multidirectional execution model.
doi:10.1109/dfm.2011.12 fatcat:oohxvm4xlndcrp4qkam2wxhi3e