A. Grama and V. Kumar. A survey of parallel search algorithms for discrete optimization problems. ORSA Journal of Computing, 7(4):365--385, 1995. Home/Search Document Details and Download
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Parallelizing Satz Using Dynamic Workload Balancing - Jurkowiak, Li, Utard (2001) (2 citations) (Correct)
....work belongs to a large eld of arborescent search parallelization. The particularities of our approach are the treatment of the irregularity of SAT and the use of the sequential SAT solver Satz. In a parallelization of arborescent search, one generally has to establish a certain cuto depth [11]. Search below this depth is done sequentially to reduce communication overhead. The major problem with this mechanism of granularity control is that subtrees below the cuto depth can be of di ering sizes, especially for strongly irregular problems such as SAT. If the cuto depth is too deep, ....
Y. Grama and V. Kumar. A Survey of Parallel Search Algorithms for Discrete Optimization Problems. University of Minnesota (1992).
Parallel and Distributed Branch-and-Bound/A* Algorithms - Cung, Dowaji, Le Cun.. (1994) (Correct)
....[25] via the OPEN list. This scheme is also named centralized scheme. The main difficulty in this scheme is the management of the OPEN list in a concurrent environment. Several parallelizations of the A algorithm have been proposed for tree search, just like the best first B B algorithm (see [18] for a large survey) But at our best knowledge, there are only a few specific parallel implementations of the A algorithm [25, 8] The reason is that the A algorithm seems to be difficult to parallelize [39] The only work to do in parallel in A is the management of OPEN and CLOSED global ....
A. Y. Grama and V. Kumar. A survey of parallel search algorithms for discrete optimization problems. Personnal communication, 1993.
An Efficient Implementation of Parallel A* - Cung, Le Cun (1994) (1 citation) (Correct)
....logically an idea for speeding up the traversal of this space. It could reduce the searching time and therefore increase the size of problems solved. Clearly, if many processors are available, then they can search different parts of the space concurrently. Research in this field is very active [11, 9, 14, 21, 7]. However, a straightforward parallelization of state space search may not be efficient. For many problems, heuristic domain knowledge is available and is gathered during the traversal of a state space. This knowledge can then be used to avoid searching some useless parts of the state space. If ....
....while the A algorithm generates only N . A surelyexpanded state v i is defined by its f value f(v i ) which is less than the cost of the optimal path. 3 Parallelization of A Several parallelizations have been proposed for the Branch and Bound procedure with best first traversal strategy (see [7] for a large survey) But at our best knowlegde, there are only a few specific parallel implementations of the A algorithm [11, 5] Moreover, recent works [25, 22, 9, 23] in this area seem to prefer parallelizing the IDA algorithm to the A one. The reason of this preference is that the A ....
Grama (A. Y.) et Kumar (V.). -- A survey of parallel search algorithms for discrete optimization problems. -- Personnal communication, 1993.
BOB : a Unified Platform for Implementing Branch-and-Bound.. - Le Cun, Team (1995) (8 citations) (Correct)
....the application would be penalized. In sequential implementation, a library should then facilitate the development of an application of the type Branch and Bound and should offer efficient components. Moreover, parallelism seems to be an interesting option to resolve larger problems more quickly [3, 5, 12, 16, 24, 36]. However, when someone writes an efficient sequential Branch and Bound application, he rarely ports it on a parallel machine. Although the use of such machine would retard the combinatorial explosion. We will demonstrate that in the case of the parallelization of a search, it is possible to make ....
Grama (A. Y.) et Kumar (V.). -- A survey of parallel search algorithms for discrete optimization problems. -- Personnal communication, 1993.
Scalable Distributed Depth-First Search with Greedy Work.. - Jaffar, Santosa, Yap, Zhu (Correct)
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A. Grama and V. Kumar. A survey of parallel search algorithms for discrete optimization problems. ORSA Journal of Computing, 7(4):365--385, 1995.
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