A bibliography on parallel and vector numerical algorithms

This is a bibliography of numerical methods. It also includes a number of other references on machine architecture, programming language, and other topics of interest to scientific computing. Certain conference proceedings and anthologies which have been published in book form are listed also

Integration of continuous-time dynamics in a spiking neural network simulator

Contemporary modeling approaches to the dynamics of neural networks consider two main classes of models: biologically grounded spiking neurons and functionally inspired rate-based units. The unified simulation framework presented here supports the combination of the two for multi-scale modeling approaches, the quantitative validation of mean-field approaches by spiking network simulations, and an increase in reliability by usage of the same simulation code and the same network model specifications for both model classes. While most efficient spiking simulations rely on the communication of discrete events, rate models require time-continuous interactions between neurons. Exploiting the conceptual similarity to the inclusion of gap junctions in spiking network simulations, we arrive at a reference implementation of instantaneous and delayed interactions between rate-based models in a spiking network simulator. The separation of rate dynamics from the general connection and communication infrastructure ensures flexibility of the framework. We further demonstrate the broad applicability of the framework by considering various examples from the literature ranging from random networks to neural field models. The study provides the prerequisite for interactions between rate-based and spiking models in a joint simulation

Information technologies for astrophysics circa 2001

It is easy to extrapolate current trends to see where technologies relating to information systems in astrophysics and other disciplines will be by the end of the decade. These technologies include mineaturization, multiprocessing, software technology, networking, databases, graphics, pattern computation, and interdisciplinary studies. It is easy to see what limits our current paradigms place on our thinking about technologies that will allow us to understand the laws governing very large systems about which we have large datasets. Three limiting paradigms are saving all the bits collected by instruments or generated by supercomputers; obtaining technology for information compression, storage and retrieval off the shelf; and the linear mode of innovation. We must extend these paradigms to meet our goals for information technology at the end of the decade

Concurrent use of two programming tools for heterogeneous supercomputers

In this thesis, a demostration of the heterogeneous use of two programming paradigms for heterogeneous computing called Cluster-M and HAsC is presented. Both paradigms can efficiently support heterogeneous networks by preserving a level of abstraction which does not include any architecture mapping details. Furthermore, they are both machine independent and hence are scalable. Unlike, almost all existing heterogeneous orchestration tools which are MIMD based, HAsC is based on the fundamental concepts of SIMD associative computing. HAsC models a heterogeneous network as a coarse grained associative computer and is designed to optimize the execution of problems with large ratios of computations to instructions. Ease of programming and execution speed, not the utilization of idle resources are the primary goals of HAsC On the other hand, Cluster-M is a generic technique that can be applied to both coarse grained as well as fine grained networks. Cluster-M provides an environment for porting various tasks onto the machines in a heterogeneous suite such that resources utilization is maximized and the overall execution time is minimized. An illustration of how these two paradigms can be used together to provide an efficient medium for heterogeneous programming is included. Finally, their scalability is discussed

The future of human cerebral cartography: a novel approach.

Cerebral cartography can be understood in a limited, static, neuroanatomical sense. Temporal information from electrical recordings contributes information on regional interactions adding a functional dimension. Selective tagging and imaging of molecules adds biochemical contributions. Cartographic detail can also be correlated with normal or abnormal psychological or behavioural data. Modern cerebral cartography is assimilating all these elements. Cartographers continue to collect ever more precise data in the hope that general principles of organization will emerge. However, even detailed cartographic data cannot generate knowledge without a multi-scale framework making it possible to relate individual observations and discoveries. We propose that, in the next quarter century, advances in cartography will result in progressively more accurate drafts of a data-led, multi-scale model of human brain structure and function. These blueprints will result from analysis of large volumes of neuroscientific and clinical data, by a process of reconstruction, modelling and simulation. This strategy will capitalize on remarkable recent developments in informatics and computer science and on the existence of much existing, addressable data and prior, though fragmented, knowledge. The models will instantiate principles that govern how the brain is organized at different levels and how different spatio-temporal scales relate to each other in an organ-centred context