In this paper, we present an approach to lift on-demand analysis to higher-order languages. Specifically, our approach bootstraps an on-demand call graph construction by leveraging a pair of on-demand dataflow analys...
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dataflow models are efficient programming paradigms for expressing the parallelism of an application. dataflow-based resource allocation methods on multicore architectures usually rely on complex graph transformations...
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In this paper, we propose Xenos, a high-performance edge platform for model inference. Unlike the prior works which mainly focus one operator-centric optimization, Xenos can automatically conduct dataflow-centric opti...
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As double data rate synchronous dynamic random-access memory (DDR SDRAM) iterations continue, the hardware response rate is unable to keep up with the increase in frequency. In order to fully utilize the bus, the Bank...
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Serverless applications spanning the cloud and edge require flexible programming frameworks for expressing compositions across the different levels of deployment. Another critical aspect for applications with state is...
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Execution graphs of parallel loop programs exhibit a nested, repeating structure. We show how such graphs that are the result of nested repetition can be represented by succinct parametric structures. This parametric ...
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dataflow testing creates test requirements as definition-use (DU) associations, where a definition is a program location that assigns a value to a variable and a use is a location where that value is accessed. data f...
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Trivial operations cause software inefficiencies that waste functional units and memory bandwidth for executing useless instructions. Although previous works have identified a significant amount of trivial operations ...
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The task-based dataflow programming model has emerged as an alternative to the process-centric programming model for extreme-scale applications. However, load balancing is still a challenge in task-based dataflow runt...
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Program slicing is a technique for simplifying programs by focusing on selected aspects of their *** mainstream static slicing methods operate on dependence graph PDG(program dependence graph)or SDG(system dependence ...
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Program slicing is a technique for simplifying programs by focusing on selected aspects of their *** mainstream static slicing methods operate on dependence graph PDG(program dependence graph)or SDG(system dependence graph),but these friendly graph representations may be a bit expensive for some *** this paper we attempt to study a light-weight approach of static program slicing,called Symbolic Program Slicing(SymPas),which works as a dataflowanalysis on LLVM(low-level virtual machine).In our SymPas approach,slices are stored in symbolic forms,not in procedures being re-analyzed(*** summaries).Instead of re-analyzing a procedure multiple times to find its slices for each callling context,we calculate a single symbolic slice which can be instantiated at call sites avoiding re-analysis;SymPas is implemented with LLVM to perform slicing on LLVM intermediate representation(IR).For comparison,we systematically adapt IFDS(interprocedural finite distributive subset)analysis and the SDG-based slicing method(SDGIFDS)to statically slice IR *** on open-source and benchmark programs,our backward SymPas shows a factor-of-6 reduction in time cost and a factor-of-4 reduction in space cost,compared with backward SDG-IFDS,thus being more *** addition,the result shows that after studying slices from 66 programs,ranging up to 336800 IR instructions in size,SymPas is highly size-scalable.
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