Scalable State Space Search on the GPU with Multi-Level Parallelism

Author(s):  
Egor Shipovalov ◽  
Valentin Pryanichnikov
2011 ◽  
Vol 135-136 ◽  
pp. 573-577 ◽  
Author(s):  
Rui Shi Liang ◽  
Min Huang

Increasing interest has been devoted to Planning as Heuristic Search over the years. Intense research has focused on deriving fast and accurate heuristics for domain-independent planning. This paper reports on an extensive survey and analysis of research work related to heuristic derivation techniques for state space search. Survey results reveal that heuristic techniques have been extensively applied in many efficient planners and result in impressive performances. We extend the survey analysis to suggest promising avenues for future research in heuristic derivation and heuristic search techniques.


2010 ◽  
Vol 7 (1) ◽  
pp. 189-200 ◽  
Author(s):  
Haitao Wei ◽  
Yu Junqing ◽  
Li Jiang

As a video coding standard, H.264 achieves high compress rate while keeping good fidelity. But it requires more intensive computation than before to get such high coding performance. A Hierarchical Multi-level Parallelisms (HMLP) framework for H.264 encoder is proposed which integrates four level parallelisms - frame-level, slice-level, macroblock-level and data-level into one implementation. Each level parallelism is designed in a hierarchical parallel framework and mapped onto the multi-cores and SIMD units on multi-core architecture. According to the analysis of coding performance on each level parallelism, we propose a method to combine different parallel levels to attain a good compromise between high speedup and low bit-rate. The experimental results show that for CIF format video, our method achieves the speedup of 33.57x-42.3x with 1.04x-1.08x bit-rate increasing on 8-core Intel Xeon processor with SIMD Technology.


1999 ◽  
pp. 19-48
Author(s):  
Pallab Dasgupta ◽  
P. P. Chakrabarti ◽  
S. C. DeSarkar

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