Exploració per tema "Data flow analysis"
Ara es mostren els items 1-6 de 6
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CRC-based memory reliability for task-parallel HPC applications
(Institute of Electrical and Electronics Engineers (IEEE), 2016)
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Accés restringit per política de l'editorialMemory reliability will be one of the major concerns for future HPC and Exascale systems. This concern is mostly attributed to the expected massive increase in memory capacity and the number of memory devices in Exascale ... -
OmpSs-2@Cluster: Distributed memory execution of nested OpenMP-style tasks
(Springer Nature, 2022)
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Accés obertState-of-the-art programming approaches generally have a strict division between intra-node shared memory parallelism and inter-node MPI communication. Tasking with dependencies offers a clean, dependable abstraction for ... -
Proving non-termination using max-SMT
(Springer, 2014)
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Accés obertWe show how Max-SMT-based invariant generation can be exploited for proving non-termination of programs. The construction of the proof of non-termination is guided by the generation of quasi-invariants - properties such ... -
Runtime-guided management of stacked DRAM memories in task parallel programs
(Association for Computing Machinery (ACM), 2018)
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Accés obertStacked DRAM memories have become a reality in High-Performance Computing (HPC) architectures. These memories provide much higher bandwidth while consuming less power than traditional off-chip memories, but their limited ... -
SACRE: A tool for dealing with uncertainty in contextual requirements at runtime
(Institute of Electrical and Electronics Engineers (IEEE), 2015)
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Accés obertSelf-adaptive systems are capable of dealing with uncertainty at runtime handling complex issues as resource variability, changing user needs, and system intrusions or faults. If the requirements depend on context, runtime ... -
Understanding the design-space of sparse/dense multiphase GNN dataflows on spatial accelerators
(Institute of Electrical and Electronics Engineers (IEEE), 2022)
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Accés obertGraph Neural Networks (GNNs) have garnered a lot of recent interest because of their success in learning representations from graph-structured data across several critical applications in cloud and HPC. Owing to their ...