Comparing the implementation of two-dimensional numerical quadrature on GPU, FPGA and ClearSpeed systems to study electron scattering by atoms

The use of accelerators, with compute architectures different and distinct from the CPU, has become a new research frontier in high-performance computing over the past ?ve years. This paper is a case study on how the instruction-level parallelism offered by three accelerator technologies, FPGA, GPU and ClearSpeed, can be exploited in atomic physics. The algorithm studied is the evaluation of two electron integrals, using direct numerical quadrature, a task that arises in the study of intermediate energy electron scattering by hydrogen atoms. The results of our ‘productivity’ study show that while each accelerator is viable, there are considerable differences in the implementation strategies that must be followed on each.

Dartmouth Symposium on the Future of Computer Music Software: A Panel Discussion

Transcript of a Panel Discussion at the Dartmouth Symposium, chaired by Eric Lyon.

Nonlinear time series prediction based on a power-law noise model

In this paper we investigate the influence of a power-law noise model, also called noise, on the performance of a feed-forward neural network used to predict time series. We introduce an optimization procedure that optimizes the parameters the neural networks by maximizing the likelihood function based on the power-law model. We show that our optimization procedure minimizes the mean squared leading to an optimal prediction. Further, we present numerical results applying method to time series from the logistic map and the annual number of sunspots demonstrate that a power-law noise model gives better results than a Gaussian model.

A heterosynaptic learning rule for neural networks

In this article we intoduce a novel stochastic Hebb-like learning rule for neural networks that is neurobiologically motivated. This learning rule combines features of unsupervised (Hebbian) and supervised (reinforcement) learning and is stochastic with respect to the selection of the time points when a synapse is modified. Moreover, the learning rule does not only affect the synapse between pre- and postsynaptic neuron, which is called homosynaptic plasticity, but effects also further remote synapses of the pre-and postsynaptic neuron. This more complex form of synaptic plasticity has recently come under investigations in neurobiology and is called heterosynaptic plasticity. We demonstrate that this learning rule is useful in training neural networks by learning parity functions including the exclusive-or (XOR) mapping in a multilayer feed-forward network. We find, that our stochastic learning rule works well, even in the presence of noise. Importantly, the mean leaxning time increases with the number of patterns to be learned polynomially, indicating efficient learning.

Stochastic Sznajd Model in open community

We extend the Sznajd Model for opinion formation by introducing persuasion probabilities for opinions. Moreover, we couple the system to an environment which mimics the application of the opinion. This results in a feedback, representing single-state opinion transitions in opposite to the two-state opinion transitions for persuading other people. We call this model opinion formation in an open community (OFOC). It can be seen as "stochastic extension of the Sznajd model for an open community, because it allows for "special choice of parameters to recover the original Sznajd model. We demonstrate the effect of feedback in the OFOC model by applying it to a scenario in which, e.g., opinion B is worse then opinion A but easier explained to other people. Casually formulated we analyzed the question, how much better one has to be, in order to persuade other people, provided the opinion is worse. Our results reveal a linear relation between the transition probability for opinion B and the influence of the environment on B.

Modelling of the sodium complex of a calixarene tetraester in the 1,3-alternate conformation

This paper describes the use of molecular mechanics to model the geometry of the sodium complex of a calix[4] arene tetraester, in the 1,3-alternate conformation 1. Partial charges were assigned to the calixarene on the basis of semi-empirical (AM1, PM3, MNDO, INDO, CNDO and ZINDO) calculations and the binding of the sodium ion to the calixarene was modelled using molecular mechanics. Agreement between the optimised and X-ray structures of the complex was very good. The effect of placing the cation in different starting positions on the energy-minimised geometry of the complex is described.

QCA Systolic array design

Quantum-dot Cellular Automata (QCA) technology is a promising potential alternative to CMOS technology. To explore the characteristics of QCA and suitable design methodologies, digital circuit design approaches have been investigated. Due to the inherent wire delay in QCA, pipelined architectures appear to be a particularly suitable design technique. Also, because of the pipeline nature of QCA technology, it is not suitable for complicated control system design. Systolic arrays take advantage of pipelining, parallelism and simple local control. Therefore, an investigation into these architectures in QCA technology is provided in this paper. Two case studies, (a matrix multiplier and a Galois Field multiplier) are designed and analyzed based on both multilayer and coplanar crossings. The performance of these two types of interconnections are compared and it is found that even though coplanar crossings are currently more practical, they tend to occupy a larger design area and incur slightly more delay. A general semi-conductor QCA systolic array design methodology is also proposed. It is found that by applying a systolic array structure in QCA design, significant benefits can be achieved particularly with large systolic arrays, even more so than when applied in CMOS-based technology.

Prediction-Based Power-Performance Adaptation of Multithreaded Scientific Codes

Computing has recently reached an inflection point with the introduction of multicore processors. On-chip thread-level parallelism is doubling approximately every other year. Concurrency lends itself naturally to allowing a program to trade performance for power savings by regulating the number of active cores; however, in several domains, users are unwilling to sacrifice performance to save power. We present a prediction model for identifying energy-efficient operating points of concurrency in well-tuned multithreaded scientific applications and a runtime system that uses live program analysis to optimize applications dynamically. We describe a dynamic phase-aware performance prediction model that combines multivariate regression techniques with runtime analysis of data collected from hardware event counters to locate optimal operating points of concurrency. Using our model, we develop a prediction-driven phase-aware runtime optimization scheme that throttles concurrency so that power consumption can be reduced and performance can be set at the knee of the scalability curve of each program phase. The use of prediction reduces the overhead of searching the optimization space while achieving near-optimal performance and power savings. A thorough evaluation of our approach shows a reduction in power consumption of 10.8 percent, simultaneous with an improvement in performance of 17.9 percent, resulting in energy savings of 26.7 percent.

Approximating the Set of Local Minima in Partial RNA Folding Landscapes

Motivation: We study a stochastic method for approximating the set of local minima in partial RNA folding landscapes associated with a bounded-distance neighbourhood of folding conformations. The conformations are limited to RNA secondary structures without pseudoknots. The method aims at exploring partial energy landscapes pL induced by folding simulations and their underlying neighbourhood relations. It combines an approximation of the number of local optima devised by Garnier and Kallel (2002) with a run-time estimation for identifying sets of local optima established by Reeves and Eremeev (2004).

Results: The method is tested on nine sequences of length between 50 nt and 400 nt, which allows us to compare the results with data generated by RNAsubopt and subsequent barrier tree calculations. On the nine sequences, the method captures on average 92% of local minima with settings designed for a target of 95%. The run-time of the heuristic can be estimated by O(n2D?ln?), where n is the sequence length, ? is the number of local minima in the partial landscape pL under consideration and D is the maximum number of steepest descent steps in attraction basins associated with pL.

Mixed radix Reed-Muller expansions

The choice of radix is crucial for multi-valued logic synthesis. Practical examples, however, reveal that it is not always possible to find the optimal radix when taking into consideration actual physical parameters of multi-valued operations. In other words, each radix has its advantages and disadvantages. Our proposal is to synthesise logic in different radices, so it may benefit from their combination. The theory presented in this paper is based on Reed-Muller expansions over Galois field arithmetic. The work aims to firstly estimate the potential of the new approach and to secondly analyse its impact on circuit parameters down to the level of physical gates. The presented theory has been applied to real-life examples focusing on cryptographic circuits where Galois Fields find frequent application. The benchmark results show the approach creates a new dimension for the trade-off between circuit parameters and provides information on how the implemented functions are related to different radices.

NPY and cardiac diseases

Hypertension-induced left ventricular hypertrophy (LVH), along with ischemic heart disease, result in LV remodeling as part of a continuum that often leads to congestive heart failure. The neurohormonal model has been used to underpin many treatment strategies, but optimal outcomes have not been achieved. Neuropeptide Y (NPY) has emerged as an additional therapeutic target, ever since it was recognised as an important mediator released from sympathetic nerves in the heart, affecting coronary artery constriction and myocardial contraction. More recent interest has focused on the mitogenic and hypertrophic effects that are observed in endothelial and vascular smooth muscle cells, and cardiac myocytes. Of the six identified NPY receptor subtypes, Y-1, Y-2, and Y-5 appear to mediate the main functional responses in the heart. Plasma levels of NPY become elevated due to the increased sympathetic activation present in stress-related cardiac conditions. Also, NPY and Y receptor polymorphisms have been identified that may predispose individuals to increased risk of hypertension and cardiac complications. This review examines what understanding exists regarding the likely contribution of NPY to cardiac pathology. It appears that NPY may play a part in compensatory or detrimental remodeling of myocardial tissue subsequent to hemodynamic overload or myocardial infarction, and in angiogenic processes to regenerate myocardium after ischemic injury. However, greater mechanistic information is required in order to truly assess the potential for treatment of cardiac diseases using NPY-based drugs.