0000000000530964

AUTHOR

Thomas Soddemann

showing 2 related works from this author

Pure Functions in C: A Small Keyword for Automatic Parallelization

2017

AbstractThe need for parallel task execution has been steadily growing in recent years since manufacturers mainly improve processor performance by increasing the number of installed cores instead of scaling the processor’s frequency. To make use of this potential, an essential technique to increase the parallelism of a program is to parallelize loops. Several automatic loop nest parallelizers have been developed in the past such as PluTo. The main restriction of these tools is that the loops must be statically analyzable which, among other things, disallows function calls within the loops. In this article, we present a seemingly simple extension to the C programming language which marks fun…

LOOP (programming language)Computer sciencemedia_common.quotation_subject020209 energy02 engineering and technologyParallel computingcomputer.software_genreToolchainTheoretical Computer ScienceTask (computing)Automatic parallelizationSide effect (computer science)Parallel processing (DSP implementation)020204 information systemsTheory of computationParallelism (grammar)0202 electrical engineering electronic engineering information engineeringPolytope model020201 artificial intelligence & image processingCompilerFunction (engineering)computerSoftwareInformation Systemsmedia_common2017 IEEE International Conference on Cluster Computing (CLUSTER)
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VarySched: A Framework for Variable Scheduling in Heterogeneous Environments

2016

Despite many efforts to better utilize the potential of GPUs and CPUs, it is far from being fully exploited. Although many tasks can be easily sped up by using accelerators, most of the existing schedulers are not flexible enough to really optimize the resource usage of the complete system. The main reasons are (i) that each processing unit requires a specific program code and that this code is often not provided for every task, and (ii) that schedulers may follow the run-until-completion model and, hence, disallow resource changes during runtime. In this paper, we present VarySched, a configurable task scheduler framework tailored to efficiently utilize all available computing resources in…

ScheduleComputer science020204 information systemsDistributed computing0202 electrical engineering electronic engineering information engineeringProcessor scheduling020201 artificial intelligence & image processing02 engineering and technologyEfficient energy useScheduling (computing)2016 IEEE International Conference on Cluster Computing (CLUSTER)
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