Approximate computing has been an emerging programming and system design paradigm that has been proposed as a way to overcome the power-wall problem that hinders the scaling of the next generation of both high-end and mobile computing systems. Towards this end, a lot of researchers have been studying the effects of approximation to applications and those hardware modifications that allow increased power benefits for reduced reliability. In this work, we focus on runtime system modifications and task-based programming models that enable software-controlled, user-driven approximate computing. We employ a systematic methodology that allows us to evaluate the potential energy and performance benefits of approximate computing using as building blocks unreliable hardware components. We present a set of extensions to OpenMP 4.0 that enable the programmer to define computations suitable for approximation. We introduce task-significance, a novel concept that describes the contribution of a task to the quality of the result. We use significance as a channel of communication from domain specific knowledge about applications towards the runtime-system, where we can optimise approximate execution depending on user constraints. Finally, we show extensions to the Linux kernel that enable it to operate seamlessly on top of unreliable memory and provide a user-space interface for memory allocation from the unreliable portion of the physical memory. Having this framework in place allowed us to identify what we call the refresh-by-access property of applications that use dynamic random-access memory (DRAM). We use this property to implement techniques for task-based applications that minimise the probability of errors when using unreliable memory enabling increased quality and power efficiency when using unreliable DRAM.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:737762 |
| Date | January 2017 |
| Creators | Chalios, Charalambos |
| Contributors | Vandierendonck, Hans ; de Supinski, Bronis ; Nikolopoulos, Dimitrios |
| Publisher | Queen's University Belfast |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://pure.qub.ac.uk/portal/en/theses/softwaredefined-significancedriven-computing(22a3cdcb-3773-4117-a06d-23f031539a36).html |
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