Loop transformations: convexity, pruning, and optimization

High-level loop transformations are a key instrument in mapping computational kernels to effectively exploit the resources in modern processor architectures. Nevertheless, selecting required compositions of loop transformations to achieve this remains a significantly challenging task; current compilers may be off by orders of magnitude in performance compared to hand-optimized programs. To address this fundamental challenge, we first present a convex characterization of all distinct, semantics-preserving, multidimensional affine transformations. We then bring together algebraic, algorithmic, and performance analysis results to design a tractable optimization algorithm over this highly expressive space. Our framework has been implemented and validated experimentally on a representative set of benchmarks running on state-of-the-art multi-core platforms.

A game theoretic approach for feature clustering and its application to feature selection

In this paper, we develop a game theoretic approach for clustering features in a learning problem. Feature clustering can serve as an important preprocessing step in many problems such as feature selection, dimensionality reduction, etc. In this approach, we view features as rational players of a coalitional game where they form coalitions (or clusters) among themselves in order to maximize their individual payoffs. We show how Nash Stable Partition (NSP), a well known concept in the coalitional game theory, provides a natural way of clustering features. Through this approach, one can obtain some desirable properties of the clusters by choosing appropriate payoff functions. For a small number of features, the NSP based clustering can be found by solving an integer linear program (ILP). However, for large number of features, the ILP based approach does not scale well and hence we propose a hierarchical approach. Interestingly, a key result that we prove on the equivalence between a k-size NSP of a coalitional game and minimum k-cut of an appropriately constructed graph comes in handy for large scale problems. In this paper, we use feature selection problem (in a classification setting) as a running example to illustrate our approach. We conduct experiments to illustrate the efficacy of our approach.

Decoherence suppression by parallelization

Decoherence as an obstacle in quantum computation is viewed as a struggle between two forces [1]: the computation which uses the exponential dimension of Hilbert space, and decoherence which destroys this entanglement by collapse. In this model of decohered quantum computation, a sequential quantum computer loses the battle, because at each time step, only a local operation is carried out but g*(t) number of gates collapse. With quantum circuits computing in parallel way the situation is different- g(t) number of gates can be applied at each time step and number gates collapse because of decoherence. As g(t) ≈ g*(t) competition here is even [1]. Our paper improves on this model by slowing down g*(t) by encoding the circuit in parallel computing architectures and running it in Single Instruction Multiple Data (SIMD) paradigm. We have proposed a parallel ion trap architecture for single-bit rotation of a qubit.

SuSeFLAV 1.2: Program for supersymmetric mass spectra with seesaw mechanism and rare lepton flavor violating decays

Accurate supersymmetric spectra are required to confront data from direct and indirect searches of supersymmetry. SuSeFLAV is a numerical tool capable of computing supersymmetric spectra precisely for various supersymmetric breaking scenarios applicable even in the presence of flavor violation. The program solves MSSM RGEs with complete 3 x 3 flavor mixing at 2-loop level and one loop finite threshold corrections to all MSSM parameters by incorporating radiative electroweak symmetry breaking conditions. The program also incorporates the Type-I seesaw mechanism with three massive right handed neutrinos at user defined mass scales and mixing. It also computes branching ratios of flavor violating processes such as l(j) -> l(i)gamma, l(j) -> 3 l(i), b -> s gamma and supersymmetric contributions to flavor conserving quantities such as (g(mu) - 2). A large choice of executables suitable for various operations of the program are provided. Program summary Program title: SuSeFLAV Catalogue identifier: AEOD_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEOD_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GNU General Public License No. of lines in distributed program, including test data, etc.: 76552 No. of bytes in distributed program, including test data, etc.: 582787 Distribution format: tar.gz Programming language: Fortran 95. Computer: Personal Computer, Work-Station. Operating system: Linux, Unix. Classification: 11.6. Nature of problem: Determination of masses and mixing of supersymmetric particles within the context of MSSM with conserved R-parity with and without the presence of Type-I seesaw. Inter-generational mixing is considered while calculating the mass spectrum. Supersymmetry breaking parameters are taken as inputs at a high scale specified by the mechanism of supersymmetry breaking. RG equations including full inter-generational mixing are then used to evolve these parameters up to the electroweak breaking scale. The low energy supersymmetric spectrum is calculated at the scale where successful radiative electroweak symmetry breaking occurs. At weak scale standard model fermion masses, gauge couplings are determined including the supersymmetric radiative corrections. Once the spectrum is computed, the program proceeds to various lepton flavor violating observables (e.g., BR(mu -> e gamma), BR(tau -> mu gamma) etc.) at the weak scale. Solution method: Two loop RGEs with full 3 x 3 flavor mixing for all supersymmetry breaking parameters are used to compute the low energy supersymmetric mass spectrum. An adaptive step size Runge-Kutta method is used to solve the RGEs numerically between the high scale and the electroweak breaking scale. Iterative procedure is employed to get the consistent radiative electroweak symmetry breaking condition. The masses of the supersymmetric particles are computed at 1-loop order. The third generation SM particles and the gauge couplings are evaluated at the 1-loop order including supersymmetric corrections. A further iteration of the full program is employed such that the SM masses and couplings are consistent with the supersymmetric particle spectrum. Additional comments: Several executables are presented for the user. Running time: 0.2 s on a Intel(R) Core(TM) i5 CPU 650 with 3.20 GHz. (c) 2012 Elsevier B.V. All rights reserved.

A dynamic bandwidth allocation scheme for interactive multimedia applications over cellular networks

Cellular networks played key role in enabling high level of bandwidth for users by employing traditional methods such as guaranteed QoS based on application category at radio access stratum level for various classes of QoSs. Also, the newer multimode phones (e.g., phones that support LTE (Long Term Evolution standard), UMTS, GSM, WIFI all at once) are capable to use multiple access methods simulta- neously and can perform seamless handover among various supported technologies to remain connected. With various types of applications (including interactive ones) running on these devices, which in turn have different QoS requirements, this work discusses as how QoS (measured in terms of user level response time, delay, jitter and transmission rate) can be achieved for interactive applications using dynamic bandwidth allocation schemes over cellular networks. In this work, we propose a dynamic bandwidth allocation scheme for interactive multimedia applications with/without background load in the cellular networks. The system has been simulated for many application types running in parallel and it has been observed that if interactive applications are to be provided with decent response time, a periodic overhauling of policy at admission control has to be done by taking into account history, criticality of applications. The results demonstrate that interactive appli- cations can be provided with good service if policy database at admission control is reviewed dynamically.

Minimization of grid current distortion in parallel-connected converters through carrier interleaving

Identical parallel-connected converters with unequal load sharing have unequal terminal voltages. The difference in terminal voltages is more pronounced in case of back-to-back connected converters, operated in power-circulation mode for the purpose of endurance tests. In this paper, a synchronous reference frame based analysis is presented to estimate the grid current distortion in interleaved, grid-connected converters with unequal terminal voltages. Influence of carrier interleaving angle on rms grid current ripple is studied theoretically as well as experimentally. Optimum interleaving angle to minimize the rms grid current ripple is investigated for different applications of parallel converters. The applications include unity power factor rectifiers, inverters for renewable energy sources, reactive power compensators, and circulating-power test set-up used for thermal testing of high-power converters. Optimum interleaving angle is shown to be a strong function of the average of the modulation indices of the two converters, irrespective of the application. The findings are verified experimentally on two parallel-connected converters, circulating reactive power of up to 150 kVA between them.

A practical approach for cross-functional vehicle body weight optimization

The goal of optimization in vehicle design is often blurred by the myriads of requirements belonging to attributes that may not be quite related. If solutions are sought by optimizing attribute performance-related objectives separately starting with a common baseline design configuration as in a traditional design environment, it becomes an arduous task to integrate the potentially conflicting solutions into one satisfactory design. It may be thus more desirable to carry out a combined multi-disciplinary design optimization (MDO) with vehicle weight as an objective function and cross-functional attribute performance targets as constraints. For the particular case of vehicle body structure design, the initial design is likely to be arrived at taking into account styling, packaging and market-driven requirements. The problem with performing a combined cross-functional optimization is the time associated with running such CAE algorithms that can provide a single optimal solution for heterogeneous areas such as NVH and crash safety. In the present paper, a practical MDO methodology is suggested that can be applied to weight optimization of automotive body structures by specifying constraints on frequency and crash performance. Because of the reduced number of cases to be analyzed for crash safety in comparison with other MDO approaches, the present methodology can generate a single size-optimized solution without having to take recourse to empirical techniques such as response surface-based prediction of crash performance and associated successive response surface updating for convergence. An example of weight optimization of spaceframe-based BIW of an aluminum-intensive vehicle is given to illustrate the steps involved in the current optimization process.

Discriminative feature analysis and selection for document classification

Classification of a large document collection involves dealing with a huge feature space where each distinct word is a feature. In such an environment, classification is a costly task both in terms of running time and computing resources. Further it will not guarantee optimal results because it is likely to overfit by considering every feature for classification. In such a context, feature selection is inevitable. This work analyses the feature selection methods, explores the relations among them and attempts to find a minimal subset of features which are discriminative for document classification.

A GIS (geographical information system)-based spatial data mining approach for optimal location and capacity planning of distributed biomass power generation facilities: A case study of Tumkur district, India

This paper primarily intends to develop a GIS (geographical information system)-based data mining approach for optimally selecting the locations and determining installed capacities for setting up distributed biomass power generation systems in the context of decentralized energy planning for rural regions. The optimal locations within a cluster of villages are obtained by matching the installed capacity needed with the demand for power, minimizing the cost of transportation of biomass from dispersed sources to power generation system, and cost of distribution of electricity from the power generation system to demand centers or villages. The methodology was validated by using it for developing an optimal plan for implementing distributed biomass-based power systems for meeting the rural electricity needs of Tumkur district in India consisting of 2700 villages. The approach uses a k-medoid clustering algorithm to divide the total region into clusters of villages and locate biomass power generation systems at the medoids. The optimal value of k is determined iteratively by running the algorithm for the entire search space for different values of k along with demand-supply matching constraints. The optimal value of the k is chosen such that it minimizes the total cost of system installation, costs of transportation of biomass, and transmission and distribution. A smaller region, consisting of 293 villages was selected to study the sensitivity of the results to varying demand and supply parameters. The results of clustering are represented on a GIS map for the region.