• Smaller data sets are problematic because — Overheads involved with launching a kernel — GPU underutilization: lower thread count has lower tolerance for long memory access latencies as there are no "other threads" ready to run. • Not all algorithms benefit from parallelization — Trade off between "parallelization effort" and "performance gain". — If it takes a lot of effort to parallelize an algorithm, it might mean that the algorithm is has no inherent parallelism, (e.g. the aircraft climbing metric).
Massively parallel processing for dynamic airspace configuration
01.10.2011
594773 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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