Data-Driven Thread Execution on Heterogeneous Processors

Abstract

In this paper we report our experience in implementing and evaluating the Data-Driven Multithreading (DDM) model on a heterogeneous multi-core processor. DDM is a non-blocking multithreading model that decouples the synchronization from the computation portions of a program, allowing them to execute asynchronously in a dataflow manner. Thread dependencies are determined by the compiler/programmer while thread scheduling is done dynamically at runtime based on data availability. The target processor for this implementation is the Cell processor. We call this implementation the Data-Driven Multithreading Virtual Machine for the Cell processor (DDM- $$\hbox {VM}_c$$ ). Thread scheduling is handled in software by the Power Processing Element core of the Cell while the Synergistic Processing Element cores execute the program threads. DDM- $$\hbox {VM}_c$$ virtualizes the parallel resources of the Cell, handles the heterogeneity of the cores, manages the Cell memory hierarchy efficiently and supports distributed execution across a cluster of Cell nodes. DDM- $$\hbox {VM}_c$$ has been implemented on a single Cell processor with six computation cores, as well as, on a four Cell processor cluster with 24 computation cores. We present an in-depth performance analysis of DDM- $$\hbox {VM}_c$$ , using a suite of standard computational benchmarks. The evaluation shows that DDM- $$\hbox {VM}_c$$ scales well and tolerates scheduling overheads, memory and communication latencies effectively. Furthermore, DDM- $$\hbox {VM}_c$$ compares favorably with other platforms targeting the Cell processor, such as, the CellSs and Sequoia.