FliX flips the indexing paradigm by mapping GPU compute to data buckets and using batch binary search, delivering up to 6.5x lower query latency and 4x better throughput per memory than leading GPU B-trees and LSM-trees.
A survey of CPU-GPU heterogeneous computing techniques,
2 Pith papers cite this work. Polarity classification is still indexing.
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A new series-parallel decomposition algorithm for general DAGs enables task mapping in heterogeneous systems that improves makespan over HEFT variants while running orders of magnitude faster than genetic algorithms or ILPs.
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FliX: Flipped-Indexing for Scalable GPU Queries and Updates
FliX flips the indexing paradigm by mapping GPU compute to data buckets and using batch binary search, delivering up to 6.5x lower query latency and 4x better throughput per memory than leading GPU B-trees and LSM-trees.
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Static task mapping for heterogeneous systems based on series-parallel decompositions
A new series-parallel decomposition algorithm for general DAGs enables task mapping in heterogeneous systems that improves makespan over HEFT variants while running orders of magnitude faster than genetic algorithms or ILPs.