Solving quantum-mechanical problems on parallel systems

Komasa, Jacek; Rychlewski, Jacek

doi:10.1016/s0167-8191(00)00023-5

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…Fortunately, currently accessible computational resources give chance for a progress in calculations towards the systems with five, six and even seven electrons. The most natural way it can be achieved is a transformation of the existing sequential algorithms to parallel versions [4,11]. In the next step, we need to perform large scale tests to compare different strategies for optimization and to select critical sections of the implemented programs, which depend on high-performance computing architecture employed.…”

Section: Motivationsmentioning

confidence: 99%

Parallel application benchmarks and performance evaluation of the Intel Xeon 7500 family processors

Kopta

Kulczewski

Kurowski

et al. 2011

Procedia Computer Science

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With the recent advent of novel multi-and many-core hardware architectures, application programmers have to deal with many hardware-specific implementation details and have to be familiar with software optimization techniques to benefit from new high-performance computing machines. Highly efficient parallel application design is in fact an interdisciplinary process involving domain specific and IT experts. Therefore, this paper aims to present early experiences with computationally demanding applications, development efforts and evaluation of their performance on the new family of Intel Xeon 7500 processors. We selected two application benchmarks applicable to real quantum chemistry and Computational Fluid Dynamics (CFD) problems as they can potentially take advantage of parallel processing on novel hardware architectures and built-in new features. Additionally, we discuss various parallel software improvements to mentioned applications, including appropriate changes to data structures as well as to communication and synchronization routines to deal with multi-level parallelism and hybrid hardware architectures. The obtained results confirmed that new hardware solutions can improve the overall application performance. However, in order to obtain a high level of parallel scalability various application modifications and tuning procedures are required as hardware configurations, including processors characteristics, interconnects and topologies, and they have a great influence on large-scale simulations.

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Section: Motivationsmentioning

confidence: 99%

Parallel application benchmarks and performance evaluation of the Intel Xeon 7500 family processors

Kopta

Kulczewski

Kurowski

et al. 2011

Procedia Computer Science

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“…The second part -the solution of the GSEP is typical linear algebra task and its parallelization is not trivial [3]. However, if the work needed to compute matrix elements exceeds significantly the diagonalization effort then performing the matrix algebra on a single processor is an acceptable solution.…”

Section: Motivation and Quantum-chemistry Essentialsmentioning

confidence: 99%

Efficiency of Matrix Elements Computations on Parallel Systems*

Mieczysław¹,

Jacek²

2003

CMST

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Experience with adapting sequential programs to a parallel environment is shared with the reader. Our programs are used in quantum-chemical calculations but certain parts of them are of general application and our results can be adapted to other types of problems. Several PC nodes are connected through a fast network and consolidated to a cluster. Our applications make use of the Message Passing Interface environment. Encouraging results concerning speedup and efficiency have been obtained. Experiments leading to a superlinear speedup using the hyperthreading technology are also reported.

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“…In contrast, qubits, which are used in quantum computers, can simultaneously exist in a linear combination of both 0 and 1 states as a result of the superposition phenomenon. [3]; This characteristic enables quantum computers to investigate various solutions to a problem in parallel, providing exponential speedup for some problem classes. Entanglement, which occurs when one qubit's state is inextricably linked to another's state and allows for effective communication and correlation, is another advantage of quantum computers.…”

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confidence: 99%

Quantum Computing Algorithms for Solving Complex Mathematical Problems

Lalit Mohan Trivedi

2023

IJRITCC

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The power of quantum mechanics, that is too complex for conventional computers, can be solved by an innovative model of computing known as quantum computing. Quantum algorithms can provide exponential speedups for some types of problems, such as many difficult mathematical ones. In this paper, we review some of the most important quantum algorithms for hard mathematical problems. When factoring large numbers, Shor's algorithm is orders of magnitude faster than any other known classical algorithm. The Grover's algorithm, which searches unsorted databases much more quickly than conventional algorithms, is then discussed.

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Solving quantum-mechanical problems on parallel systems

Cited by 3 publications

References 15 publications

Parallel application benchmarks and performance evaluation of the Intel Xeon 7500 family processors

Parallel application benchmarks and performance evaluation of the Intel Xeon 7500 family processors

Efficiency of Matrix Elements Computations on Parallel Systems*

Quantum Computing Algorithms for Solving Complex Mathematical Problems

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