TurtleKV uses a balanced TurtleTree on-disk structure and flexible memory tuning knobs to deliver strong performance across inserts, mixed workloads, point queries, and scans in YCSB tests, matching or beating SplinterDB, RocksDB, and WiredTiger.
Meiklejohn, and Xiangfeng Zhu
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2025 3representative citing papers
Hamiltonian Graph Networks achieve 150-600x faster training via random feature parameter construction while retaining comparable accuracy and physical invariances on N-body systems up to 10,000 particles.
vMODB co-designs a Virtual Micro Service programming model with a system that unifies event and data management to enforce ACID properties in distributed asynchronous applications, outperforming eventual-consistency frameworks by up to 3x on two benchmarks.
citing papers explorer
-
Dynamic read & write optimization with TurtleKV
TurtleKV uses a balanced TurtleTree on-disk structure and flexible memory tuning knobs to deliver strong performance across inserts, mixed workloads, point queries, and scans in YCSB tests, matching or beating SplinterDB, RocksDB, and WiredTiger.
-
Rapid training of Hamiltonian graph networks using random features
Hamiltonian Graph Networks achieve 150-600x faster training via random feature parameter construction while retaining comparable accuracy and physical invariances on N-body systems up to 10,000 particles.
-
vMODB: Unifying Event and Data Management for Distributed Asynchronous Applications
vMODB co-designs a Virtual Micro Service programming model with a system that unifies event and data management to enforce ACID properties in distributed asynchronous applications, outperforming eventual-consistency frameworks by up to 3x on two benchmarks.