Harnessing Unreliable Cores in Heterogeneous Architecture: The PyDac Programming Model and Runtime

Abstract

Heterogeneous many-core architectures combined with scratch-pad memories are attractive because they promise better energy efficiency than conventional architectures and a good balance between single-thread performance and multi-thread throughput. However, programmers will need an environment for finding and managing the large degree of parallelism, locality, and system resilience. We propose a Python-based task parallel programming model called PyDac to support these objectives. PyDac provides a two-level programming model based on the divide-and-conquer strategy. The PyDac runtime system allows threads to be run on unreliable hardware by dynamically checking the results without involvement from the programmer. To test this programming model and runtime, an unconventional heterogeneous architecture consisting of PowerPC and ARM cores was developed and emulated on an FPGA device. We inject faults during the execution of micro-benchmarks and show that through the use of double and triple modular redundancy we are able to complete the benchmarks with the correct results while only incurring a proportional performance penalty.