Stochastic Speculative Computation Method and its Application to Monte Carlo Molecular Simulation
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Summary
It is shown that speculative computation can accelerate MC molecular simulations and is performed theoretical verification and experiments to determine if speculative computation is more effective than conventional parallel computation methods.
- Type
- article
- Published
- 2018-01-03
- Cited by
- 2
- References
- 17
- Access
- Open access
- OpenAlex
- https://openalex.org/W2785785366
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:46949178
Keywords
Computer science, Computation, Parallel computing, Monte Carlo method, Multi-core processor
References
- Random Restarts in Global Optimization
- Improving search by incorporating evolution principles in parallel Tabu Search
- Toward Structure Prediction for Short Peptides Using the Improved SAAP Force Field Parameters
- Speculative parallel simulated annealing with acceptance prediction
- A cost-driven compilation framework for speculative parallelization of sequential programs
- The SAAP force field: Development of the single amino acid potentials for 20 proteinogenic amino acids and Monte Carlo molecular simulation for short peptides
- Hardware and software support for speculative execution of sequential binaries on a chip-multiprocessor
- Speculative computation in multilisp
- Parallel Speed-Up of Monte Carlo Methods for Global Optimization
- Solving combinatorial optimization problems with line-up competition algorithm
- Paragon: collaborative speculative loop execution on GPU and CPU
- Parallel simulated annealing by generalized speculative computation
- A Statistical Distribution Function of Wide Applicability
- Algorithm portfolios
- Multi-start Methods
- Simulated annealing and Boltzmann machines
- Exceeding the Efficiency of Distributed Approximate Algorithms Enabling by the Multiplexing Method
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