Cabinet Layout Optimization of Supercomputer Topologies for Shorter Cable Length

Abstract—As the scales of supercomputers increase total cable length becomes enormous, e.g., up to thousands of kilometers. Recent high-radix switches with dozens of ports make switch layout and system packaging more complex. In this study, we study the optimization of the physical layout of topologies of switches on a machine room floor with the goal of reducing cable length. For a given topology, using graph clustering algorithms, we group switches logically into cabinets so that the number of inter-cabinet cables is small. Then, we map the cabinets onto a physical floor space so as to minimize total cable length. This is done by modeling and optimizing the mapping problem as a facility location problem. Our evaluation results show that, when compared to standard clustering/mapping approaches and for popular network topologies, our clustering approach can reduce the number of inter-cabinet cables by up to 40.3 % and our mapping approach can reduce the inter-rack cable length by up to 39.6%. Index Terms—Topology, cabinet layout, interconnection networks, high performance computing, high-radix switches I

Study on Combinatorial Auction Mechanism for Resource Allocation in Cloud Computing Environment

This thesis proposes a combinatorial auction-based marketplace mechanism for cloudcomputing services, which allows users to reserve arbitrary combination of services atrequested timeslots, prices and quality of service. The proposed mechanism helps enterpriseusers build workflow applications in a cloud computing environment, specifically on theplatform-as-a-service, where the users need to compose multiple types of services at differenttimeslots.The proposed marketplace mechanism consists of a forward market for an advancereservation and a spot market for an immediate allocation of services. Each market employsmixed integer programming to enforce a Pareto optimum allocation with maximized socialeconomic welfare, as well as double-sided auction design to encourage both users andproviders to compete for buying and selling the services.A marketplace simulator, named W-Mart, is specially developed for this thesis. Itimplements the proposed mechanism on Java platform being powered by CPLEX, thestate-of-the-art MIP solver. W-Mart is designed after the multi-agent virtual market systemU-Mart, and is also capable to deal with human agents and machine agents at the same time.Three experiments are carried out by means of multi-agent simulations. First, theaccuracy of the combinatorial allocation scheme is validated. The result demonstrates that itworks properly. Second, the overhead of the proposed market mechanism including MIPsolver is assessed. The result shows that the overhead is acceptable to deal with an expectednumber of participants within the proposed trading schedule. Third, the performances of fourtypes of market mechanisms are extensively evaluated. The results clarify that (1) theproposed forward/combinatorial mechanism outperforms other non-combinatorial and/ornon-reservation (spot) mechanisms in both user-centric rationality and global efficiency, (2)running both a forward market and a spot market improves resource utilization withoutdisturbing advance reservations, and (3) the users\u27 preference between the forward marketand the spot market affects the performance of whole marketplace significantly in tightdemand/supply conditions

Combining Molecular Dynamics and Machine Learning to Analyze Shear Thinning for Alkane and Globular Lubricants in the Low Shear Regime

Lubricants with desirable frictional properties are important in achieving an energy-saving society. Lubricants at the interfaces of mechanical components are confined under high shear rates and pressures and behave quite differently from the bulk material. Computational approaches such as nonequilibrium molecular dynamics (NEMD) simulations have been performed to probe the molecular behavior of lubricants. However, the low-shear-velocity regions of the materials have rarely been simulated owing to the expensive calculations necessary to do so, and the molecular dynamics under shear velocities comparable with that in the experiments are not clearly understood. In this study, we performed NEMD simulations of extremely confined lubricants, i.e., two molecular layers for four types of lubricants confined in mica walls, under shear velocities from 0.001 to 1 m/s. While we confirmed shear thinning, the velocity profiles could not show the flow behavior when the shear velocity was much slower than thermal fluctuations. Therefore, we used an unsupervised machine learning approach to detect molecular movements that contribute to shear thinning. First, we extracted the simple features of molecular movements from large amounts of MD data, which were found to correlate with the effective viscosity. Subsequently, the extracted features were interpreted by examining the trajectories contributing to these features. The magnitude of diffusion corresponded to the viscosity, and the location of slips that varied depending on the spherical and chain lubricants was irrelevant. Finally, we attempted to apply a modified Stokes–Einstein relation at equilibrium to the nonequilibrium and confined systems. While systems with low shear rates obeyed the relation sufficiently, large deviations were observed under large shear rates