Evaluation of Baseline Methods for IDD-based SSD External Memory Search
About
Many difficult search problems cannot be solved by algorithms such as A* using only RAM. Search algorithms which use external memory such as SSDs and HDDs with much higher capacity than RAM have been proposed in previous work, but previous work has focused on delayed duplicate detection approaches, as well as complex immediate duplicate detection (IDD) methods, and relatively simple methods for IDD have not been systematically studied. In addition, the effect of OS-level mechanisms for managing and speeding up accesses to external memory, such as page caches, has not been studied. This paper addresses these gaps in the literature by evaluating and analyzing the performance of simple baseline approaches for IDD-based A*.
Yuki Suzuki, Alex Fukunaga• 2026
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Automated Planning | Planning Problems 1.0GiB RAM limit | Expansion Rate (states/sec)1.58e+5 | 96 | |
| Heuristic Search | Merge-and-shrink heuristic benchmark set | Expansion Rate (states/sec)1.82e+5 | 88 | |
| Heuristic Search | Blind heuristic benchmark set | Expansions/sec7.70e+4 | 64 | |
| Automated Planning | Planning Problems 600MiB RAM limit | Expansion Rate (states/sec)7.22e+4 | 32 | |
| Heuristic Planning | blocksworld p23 | Expansion Rate (states/sec)6.19e+5 | 3 | |
| Heuristic Planning | data-network p08 | Expansion Rate (states/sec)1.28e+5 | 3 | |
| Heuristic Planning | depots p23 | Expansion Rate (states/sec)2.26e+5 | 3 | |
| Heuristic Planning | floortile p03 | Expansion Rate (states/sec)3.86e+5 | 3 | |
| Heuristic Planning | mprime p05 | Expansion Rate (states/sec)2.36e+5 | 3 | |
| Heuristic Planning | rovers-p03 | Expansion Rate (states/sec)2.29e+5 | 3 |
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