scholarly journals SIMD-Optimized Search Over Sorted Data

2021 ◽  
pp. 1-1
Author(s):  
Benjamin P Mastripolito ◽  
Nicholas A. Koskelo ◽  
Dylan A. Weatherred ◽  
David A Pimentel ◽  
Daniel G. Sheppard ◽  
...  
Keyword(s):  
Equation Of State ◽  
Search Algorithm ◽  
Search Algorithms ◽  
Floating Point ◽  
Table Lookup ◽  
State Data ◽  
Point Representation ◽  
Small Array ◽  
A Performance

Abstract Applications often require a fast, single-threaded search algorithm over sorted data, typical in table-lookup operations. We explore various search algorithms for a large number of search candidates over a relatively small array of logarithmically-distributed sorted data. These include an innovative hash-based search that takes advantage of floating point representation to bin data by the exponent. Algorithms that can be optimized to take advantage of SIMD vector instructions are of particular interest. We then conduct a case study applying our results and analyzing algorithmic performance with the EOSPAC package. EOSPAC is a table look-up library for manipulation and interpolation of SESAME equation-of-state data. Our investigation results in a couple of algorithms with better performance with a best case eight times speedup over the original EOSPAC Hunt-and-Locate implementation. Our techniques should generalize to other instances of search algorithms seeking to get a performance boost from vectorization.

scholarly journals Characterizing the Nature of Probability-Based Proof Number Search: A Case Study in the Othello and Connect Four Games

Information ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 264
Author(s):  
Anggina Primanita ◽  
Mohd Nor Akmal Khalid ◽  
Hiroyuki Iida
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Search Algorithm ◽  
Search Algorithms ◽  
Game Tree ◽  
Tree Structures ◽  
Best First Search

Variants of best-first search algorithms and their expansions have continuously been introduced to solve challenging problems. The probability-based proof number search (PPNS) is a best-first search algorithm that can be used to solve positions in AND/OR game tree structures. It combines information from explored (based on winning status) and unexplored (through Monte Carlo simulation) nodes from a game tree using an indicator called the probability-based proof number (PPN). In this study, PPNS is employed to solve randomly generated positions in Connect Four and Othello, in which the results are compared with the two well-known best-first search algorithms (proof number search (PNS) and Monte Carlo proof number search). Adopting a simple improvement parameter in PPNS reduces the number of nodes that need to be explored by up to 57%. Moreover, further observation showed the varying importance of information from explored and unexplored nodes in which PPNS relies critically on the combination of such information in earlier stages of the Othello game. Discussion and insights from these findings are provided where the potential future works are briefly described.

The Category Is . . . Opulence!

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Joseph Plaster
Keyword(s):  
Performance Studies ◽  
Knowledge Creation ◽  
People Of Color ◽  
Gender Nonconforming ◽  
Johns Hopkins University ◽  
The Cross ◽  
And Gender ◽  
A Performance

In recent years there has been a strong “public turn” within universities that is renewing interest in collaborative approaches to knowledge creation. This article draws on performance studies literature to explore the cross-disciplinary collaborations made possible when the academy broadens our scope of inquiry to include knowledge produced through performance. It takes as a case study the “Peabody Ballroom Experience,” an ongoing collaboration between the Johns Hopkins University Sheridan Libraries, the Peabody Institute BFA Dance program, and Baltimore’s ballroom community—a performance-based arts culture comprising gay, lesbian, queer, transgender, and gender-nonconforming people of color.

scholarly journals Circumspect descent prevails in solving random constraint satisfaction problems

2008 ◽  
Vol 105 (40) ◽  
pp. 15253-15257 ◽  
Author(s):  
Mikko Alava ◽  
John Ardelius ◽  
Erik Aurell ◽  
Petteri Kaski ◽  
Supriya Krishnamurthy ◽  
...  
Keyword(s):  
Local Search ◽  
Linear Time ◽  
Search Algorithm ◽  
Solution Space ◽  
Search Algorithms ◽  
Constraint Satisfaction Problems ◽  
Problem Instance ◽  
Stochastic Local Search ◽  
Local Search Algorithms ◽  
Local Energy Minimum

We study the performance of stochastic local search algorithms for random instances of the K-satisfiability (K-SAT) problem. We present a stochastic local search algorithm, ChainSAT, which moves in the energy landscape of a problem instance by never going upwards in energy. ChainSAT is a focused algorithm in the sense that it focuses on variables occurring in unsatisfied clauses. We show by extensive numerical investigations that ChainSAT and other focused algorithms solve large K-SAT instances almost surely in linear time, up to high clause-to-variable ratios α; for example, for K = 4 we observe linear-time performance well beyond the recently postulated clustering and condensation transitions in the solution space. The performance of ChainSAT is a surprise given that by design the algorithm gets trapped into the first local energy minimum it encounters, yet no such minima are encountered. We also study the geometry of the solution space as accessed by stochastic local search algorithms.

Integrating a performance-based approach into practice: a case study

2005 ◽  
Vol 33 (2) ◽  
pp. 128-141 ◽  
Author(s):  
David Hammond ◽  
James J. Dempsey ◽  
Françoise Szigeti ◽  
Gerald Davis
Keyword(s):  
A Performance

scholarly journals Finding A Small Vertex Cover in Massive Sparse Graphs: Construct, Local Search, and Preprocess

2017 ◽  
Vol 59 ◽  
pp. 463-494 ◽  
Author(s):  
Shaowei Cai ◽  
Jinkun Lin ◽  
Chuan Luo
Keyword(s):  
Local Search ◽  
Real World ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Vertex Cover ◽  
Search Algorithms ◽  
Theory And Practice ◽  
Sparse Graphs ◽  
Massive Graphs

The problem of finding a minimum vertex cover (MinVC) in a graph is a well known NP-hard combinatorial optimization problem of great importance in theory and practice. Due to its NP-hardness, there has been much interest in developing heuristic algorithms for finding a small vertex cover in reasonable time. Previously, heuristic algorithms for MinVC have focused on solving graphs of relatively small size, and they are not suitable for solving massive graphs as they usually have high-complexity heuristics. This paper explores techniques for solving MinVC in very large scale real-world graphs, including a construction algorithm, a local search algorithm and a preprocessing algorithm. Both the construction and search algorithms are based on low-complexity heuristics, and we combine them to develop a heuristic algorithm for MinVC called FastVC. Experimental results on a broad range of real-world massive graphs show that, our algorithms are very fast and have better performance than previous heuristic algorithms for MinVC. We also develop a preprocessing algorithm to simplify graphs for MinVC algorithms. By applying the preprocessing algorithm to local search algorithms, we obtain two efficient MinVC solvers called NuMVC2+p and FastVC2+p, which show further improvement on the massive graphs.

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