Post-compiler software optimization for reducing energy

SchulteEric,; DornJonathan,; HardingStephen,; ForrestStephanie,; WeimerWestley,

doi:10.1145/2654822.2541980

Cited by 19 publications

References 43 publications

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Area-Energy Aware Dataflow Optimisation of Visual Tracking Systems

Garcia¹,

Bhowmik

Wallace³

et al. 2018

Applied Reconfigurable Computing. Architectures, Tools, and Applications

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This paper presents an orderly dataflow-optimisation approach suitable for area-energy aware computer vision applications on FPGAs. Vision systems are increasingly being deployed in power constrained scenarios, where the dataflow model of computation has become popular for describing complex algorithms. Dataflow model allows processing datapaths comprised of several independent and well defined computations. However, compilers are often unsuccessful in identifying domain-specific optimisation opportunities resulting in wasted resources and power consumption. We present a methodology for the optimisation of dataflow networks, according to patterns often found in computer vision systems, focusing on identifying optimisations which are not discovered automatically by an optimising compiler. Code transformation using profiling and refactoring provides opportunities to optimise the design, targeting FPGA implementations and focusing on area and power abatement. Our refactoring methodology, applying transformations to a complex algorithm for visual tracking resulted in significant reduction in power consumption and resource usage.

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Area-Energy Aware Dataflow Optimisation of Visual Tracking Systems

Garcia¹,

Bhowmik

Wallace³

et al. 2018

Applied Reconfigurable Computing. Architectures, Tools, and Applications

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Comparing apples and oranges? Investigating the consistency of CPU and memory profiler results across multiple java versions

Watkinson,

Brownlee

2024

Autom Softw Eng

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Profiling is an important tool in the software developer’s box, used to identify hot methods where most computational resources are used, to focus efforts at improving efficiency. Profilers are also important in the context of Genetic improvement (GI) of software. GI applies search-based optimisation to existing software with many examples of success in a variety of contexts. GI generates variants of the original program, testing each for functionality and properties such as run time or memory footprint, and profiling can be used to target the code variations to increase the search efficiency. We report on an experimental study comparing two profilers included with different versions of the Java Development Kit (JDK), HPROF (JDK 8) and Java Flight Recorder (JFR) (JDK 8, 9, and 17), within the GI toolbox Gin on six open-source applications, for both run time and memory use. We find that a core set of methods are labelled hot in most runs, with a long tail appearing rarely. We suggest five repeats enough to overcome this noise. Perhaps unsurprisingly, changing the profiler and JDK dramatically change the hot methods identified, so profiling must be rerun for new JDKs. We also show that using profiling for test case subset selection is unwise, often missing relevant members of the test suite. Similar general patterns are seen for memory profiling as for run time but the identified hot methods are often quite different.

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