Performance of a Very Large-Scale Neighborhood for Minimizing Makespan on Parallel Machines

2006 ◽  
Vol 25 ◽  
pp. 29-33 ◽  
Author(s):  
Tobias Brueggemann ◽  
Johann L. Hurink ◽  
Tjark Vredeveld ◽  
Gerhard J. Woeginger
Keyword(s):  
Large Scale ◽  
Parallel Machines

Periodic hierarchical load balancing for large supercomputers

2011 ◽  
Vol 25 (4) ◽  
pp. 371-385 ◽  
Author(s):  
Gengbin Zheng ◽  
Abhinav Bhatelé ◽  
Esteban Meneses ◽  
Laxmikant V. Kalé
Keyword(s):  
Load Balancing ◽  
Large Scale ◽  
Parallel Machines ◽  
National Laboratory ◽  
Argonne National Laboratory ◽  
Parallel Applications ◽  
Scientific Application ◽  
Computing Center ◽  
Blue Gene ◽  
Advanced Computing

Large parallel machines with hundreds of thousands of processors are becoming more prevalent. Ensuring good load balance is critical for scaling certain classes of parallel applications on even thousands of processors. Centralized load balancing algorithms suffer from scalability problems, especially on machines with a relatively small amount of memory. Fully distributed load balancing algorithms, on the other hand, tend to take longer to arrive at good solutions. In this paper, we present an automatic dynamic hierarchical load balancing method that overcomes the scalability challenges of centralized schemes and longer running times of traditional distributed schemes. Our solution overcomes these issues by creating multiple levels of load balancing domains which form a tree. This hierarchical method is demonstrated within a measurement-based load balancing framework in Charm++. We discuss techniques to deal with scalability challenges of load balancing at very large scale. We present performance data of the hierarchical load balancing method on up to 16,384 cores of Ranger (at the Texas Advanced Computing Center) and 65,536 cores of Intrepid (the Blue Gene/P at Argonne National Laboratory) for a synthetic benchmark. We also demonstrate the successful deployment of the method in a scientific application, NAMD, with results on Intrepid.

Parallelization of the Lattice Boltzmann Method in Simulating Buoyancy-Driven Convection Heat Transfer

2004 ◽  
Author(s):  
Anoosheh Niavarani-Kheirier ◽  
Masoud Darbandi ◽  
Gerry E. Schneider
Keyword(s):  
Lattice Boltzmann Method ◽  
Lattice Boltzmann ◽  
Message Passing ◽  
Large Scale ◽  
Message Passing Interface ◽  
Parallel Machines ◽  
Convection Heat Transfer ◽  
Wide Range ◽  
Buoyancy Driven Convection ◽  
Boltzmann Method

The main objective of the current work is to utilize Lattice Boltzmann Method (LBM) for simulating buoyancy-driven flow considering the hybrid thermal lattice Boltzmann equation (HTLBE). After deriving the required formulations, they are validated against a wide range of Rayleigh numbers in buoyancy-driven square cavity problem. The performance of the method is investigated on parallel machines using Message Passing Interface (MPI) library and implementing domain decomposition technique to solve problems with large order of computations. The achieved results show that the code is highly efficient to solve large scale problems with excellent speedup.

scholarly journals Evaluation of architectural support for global address-based communication in large-scale parallel machines

1996 ◽  
Vol 30 (5) ◽  
pp. 37-48
Author(s):  
Arvind Krishnamurthy ◽  
Klaus E. Schauser ◽  
Chris J. Scheiman ◽  
Randolph Y. Wang ◽  
David E. Culler ◽  
...  
Keyword(s):  
Large Scale ◽  
Parallel Machines ◽  
Architectural Support

Large Scale Molecular Dynamics Study of Amorphous Carbon and Graphite on Parallel Machines

1995 ◽  
Vol 408 ◽  
Author(s):  
J. Yu ◽  
Andrey Omeltchenko ◽  
Rajiv K. Kalia ◽  
Priya Vashishta ◽  
Donald W. Brenner
Keyword(s):  
Molecular Dynamics ◽  
Crack Propagation ◽  
Molecular Dynamics Simulations ◽  
Amorphous Carbon ◽  
Large Scale ◽  
Parallel Machines ◽  
External Stress ◽  
Graphite Sheet ◽  
Bond Order Potential ◽  
Dynamics Simulations

AbstractUsing a reactive empirical bond-order potential (REBOP) model for hydrocarbons1, large scale molecular dynamics simulations of carbon systems are carried out on parallel machines. Structural and dynamical correlations of amorphous carbon at various densities are studied. The calculated structure factor agrees well with neutron scattering experiments and the results of tightbinding molecular dynamics simulations. The dynamic behavior of crack propagation through graphite sheet is also investigated with the molecular-dynamics method. Effects of external stress and initial notch shape on crack propagation in graphite are studied. It is found that graphite sheet fractures in a cleavage-like or branching manners depending on the orientations of the graphite sheet with respect to the external stress. The roughness of crack surfaces is analyzed. Two roughness exponents are observed in two different regions.

scholarly journals Study on Massive-Scale Slow-Hash Recovery Using Unified Probabilistic Context-Free Grammar and Symmetrical Collaborative Prioritization with Parallel Machines

Symmetry ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 450
Author(s):  
Tianjun Wu ◽  
Yuexiang Yang ◽  
Chi Wang ◽  
Rui Wang
Keyword(s):  
Data Structures ◽  
Hash Function ◽  
Large Scale ◽  
Parallel Machines ◽  
Limited Time ◽  
Context Free Grammar ◽  
Average Speed ◽  
Massive Scale ◽  
Context Free ◽  
Probabilistic Context

Slow-hash algorithms are proposed to defend against traditional offline password recovery by making the hash function very slow to compute. In this paper, we study the problem of slow-hash recovery on a large scale. We attack the problem by proposing a novel concurrent model that guesses the target password hash by leveraging known passwords from a largest-ever password corpus. Previously proposed password-reused learning models are specifically designed for targeted online guessing for a single hash and thus cannot be efficiently parallelized for massive-scale offline recovery, which is demanded by modern hash-cracking tasks. In particular, because the size of a probabilistic context-free grammar (PCFG for short) model is non-trivial and keeping track of the next most probable password to guess across all global accounts is difficult, we choose clever data structures and only expand transformations as needed to make the attack computationally tractable. Our adoption of max-min heap, which globally ranks weak accounts for both expanding and guessing according to unified PCFGs and allows for concurrent global ranking, significantly increases the hashes can be recovered within limited time. For example, 59.1% accounts in one of our target password list can be found in our source corpus, allowing our solution to recover 20.1% accounts within one week at an average speed of 7200 non-identical passwords cracked per hour, compared to previous solutions such as oclHashcat (using default configuration), which cracks at an average speed of 28 and needs months to recover the same number of accounts with equal computing resources (thus are infeasible for a real-world attacker who would maximize the gain against the cracking cost). This implies an underestimated threat to slow-hash protected password dumps. Our method provides organizations with a better model of offline attackers and helps them better decide the hashing costs of slow-hash algorithms and detect potential vulnerable credentials before hackers do.

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