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-VMcVMc). 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-VMcVMc 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-VMcVMc 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-VMcVMc, using a suite of standard computational benchmarks. The evaluation shows that DDM-VMcVMc scales well and tolerates scheduling overheads, memory and communication latencies effectively. Furthermore, DDM-VMcVMc compares favorably with other platforms targeting the Cell processor, such as, the CellSs and Sequoia.

Title

International Journal of Parallel Programming

Publisher

Springer New York LLC

Publisher Country

United States of America

Indexing

Thomson Reuters

Impact Factor

1.156

Publication Type

Online only

Volume

45

Year

2017

Pages

198–224