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oai:figshare.com:article/32862833 2026-10-01T16:05:29Z portal_1172 item_type_8 month_year_10_2026

Software-defined Significance-Driven Computing Charalambos Chalios (24161448) PUREID: 148227324 approximate computing significance-based computing parallel computing reliability task-based programming DRAM scheduling linux Approximate computing has been an emerging programming and system design paradigm that has been proposed as a way to overcome the <br>power-wall problem that hinders the scaling of the next generation of both high-end and mobile computing systems. Towards this<br>end, a lot of researchers have been studying the effects of approximation to applications and those hardware modifications that<br>allow increased power benefits for reduced reliability. In this work, we focus on runtime system modifications and task-based<br>programming models that enable software-controlled, user-driven approximate computing.<br><br>We employ a systematic methodology that allows us to evaluate the potential energy and performance benefits of approximate<br>computing using as building blocks unreliable hardware components. We present a set of extensions to OpenMP 4.0 that enable the <br>programmer to define computations suitable for approximation. We introduce task-significance, a novel concept that describes the <br>contribution of a task to the quality of the result. We use significance as a channel of communication from domain specific<br>knowledge about applications towards the runtime-system, where we can optimise approximate execution depending on user<br>constraints.<br><br>Finally, we show extensions to the Linux kernel that enable it to operate seamlessly on top of unreliable memory and provide a<br>user-space interface for memory allocation from the unreliable portion of the physical memory. Having this framework in place<br>allowed us to identify what we call the refresh-by-access property of applications that use dynamic random-access memory (DRAM).<br>We use this property to implement techniques for task-based applications that minimise the probability of errors when using<br>unreliable memory enabling increased quality and power efficiency when using unreliable DRAM. 2026-10-01T16:05:29Z Text Thesis 10.17034/32862833.v1 https://figshare.com/articles/thesis/Software-defined_Significance-Driven_Computing/32862833 All Rights Reserved