PyDac: a distributed runtime system and programming model for a heterogeneous many-core architecture

1 online resource (88 pages) : PDF

2014

University of North Carolina at Charlotte

Heterogeneous many-core architectures that consist of big, fast cores and small, energy-efficient cores are very promising for future high-performance computing (HPC) systems. These architectures offer a good balance between single-threaded performance and multithreaded throughput. Such systems impose challenges on the design of programming model and runtime system. Specifically, these challenges include (a) how to fully utilize the chip's performance, (b) how to manage heterogeneous, unreliable hardware resources, and (c) how to generate and manage a large amount of parallel tasks.This dissertation proposes and evaluates a Python-based programming framework called PyDac. PyDac supports a two-level programming model. At the high level, a programmer creates a very large number of tasks, using the divide-and-conquer strategy. At the low level, tasks are written in imperative programming style. The runtime system seamlessly manages the parallel tasks, system resilience, and inter-task communication with architecture support. PyDac has been implemented on both an field-programmable gate array (FPGA) emulation of an unconventional heterogeneous architecture and a conventional multicore microprocessor. To evaluate the performance, resilience, and programmability of the proposed system, several micro-benchmarks were developed. We found that (a) the PyDac abstracts away task communication and achieves programmability, (b) the micro-benchmarks are scalable on the hardware prototype, but (predictably) serial operation limits some micro-benchmarks, and (c) the degree of protection versus speed could be varied in redundant threading that is transparent to programmers.

doctoral dissertations

Computer engineering

Ph.D.

Emulation
FPGA
Many-Core Architecture
Programming Model
Python
Runtime System

Electrical Engineering

Sass, Ronald

Thesis (Ph.D.)--University of North Carolina at Charlotte, 2014.

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Huang_uncc_0694D_10641

http://hdl.handle.net/20.500.13093/etd:1474