コンピュータ・サイエンス : ACM computing surveys

http://personal.cimat.mx:8181/~jortega/MaterialDidactico/modestoI10/Proyectos/Blum%20%282005%29AntCol.pdf

Ant colony optimization: Introduction and recent trends

The firefly algorithm has become an increasingly important tool of Swarm Intelligence that has been applied in almost all areas of optimization, as well as engineering practice. Many problems from various areas have been successfully solved using the firefly algorithm and its variants. In order to use the algorithm to solve diverse problems, the original firefly algorithm needs to be modified or hybridized. This paper carries out a comprehensive review of this living and evolving discipline of Swarm Intelligence, in order to show that the firefly algorithm could be applied to every problem arising in practice. On the other hand, it encourages new researchers and algorithm developers to use this simple and yet very efficient algorithm for problem solving. It often guarantees that the obtained results will meet the expectations.

A comprehensive review of firefly algorithms

Molecular mechanics simulations offer a computational approach to study the behavior of biomolecules at atomic detail, but such simulations are limited in size and timescale by the available computing resources. State- of-the-art graphics processing units (GPUs) can perform over 500 billion arithmetic operations per second, a tremen- dous computational resource that can now be utilized for general purpose computing as a result of recent advances in GPU hardware and software architecture. In this article, an overview of recent advances in programmable GPUs is presented, with an emphasis on their application to molecular mechanics simulations and the programming techni- ques required to obtain optimal performance in these cases. We demonstrate the use of GPUs for the calculation of long-range electrostatics and nonbonded forces for molecular dynamics simulations, where GPU-based calculations are typically 10-100 times faster than heavily optimized CPU-based implementations. The application of GPU accel- eration to biomolecular simulation is also demonstrated through the use of GPU-accelerated Coulomb-based ion placement and calculation of time-averaged potentials from molecular dynamics trajectories. A novel approximation to Coulomb potential calculation, the multilevel summation method, is introduced and compared with direct Cou- lomb summation. In light of the performance obtained for this set of calculations, future applications of graphics processors to molecular dynamics simulations are discussed.

/pdf/accelerating-molecular-modeling-applications-with-graphics-2bdy8a42vp.pdf

Accelerating molecular modeling applications with graphics processors

Metaheuristics are the most exciting development in approximate optimization techniques of the last two decades. They have had widespread successes in attacking a variety of difficult combinatorial optimization problems that arise in many practical areas. This bibliography provides a classification of a comprehensive list of 1380 references on the theory and application of metaheuristics. Metaheuristics include but are not limited to constraint logic programming; greedy random adaptive search procedures; natural evolutionary computation; neural networks; non-monotonic search strategies; space-search methods; simulated annealing; tabu search; threshold algorithms and their hybrids. References are presented in alphabetical order under a number of subheadings.

Metaheuristics: A bibliography

Hardware multithreading is becoming a generally applied technique in the next generation of microprocessors. Several multithreaded processors are announced by industry or already into production in the areas of high-performance microprocessors, media, and network processors.A multithreaded processor is able to pursue two or more threads of control in parallel within the processor pipeline. The contexts of two or more threads of control are often stored in separate on-chip register sets. Unused instruction slots, which arise from latencies during the pipelined execution of single-threaded programs by a contemporary microprocessor, are filled by instructions of other threads within a multithreaded processor. The execution units are multiplexed between the thread contexts that are loaded in the register sets.Underutilization of a superscalar processor due to missing instruction-level parallelism can be overcome by simultaneous multithreading, where a processor can issue multiple instructions from multiple threads each cycle. Simultaneous multithreaded processors combine the multithreading technique with a wide-issue superscalar processor to utilize a larger part of the issue bandwidth by issuing instructions from different threads simultaneously.Explicit multithreaded processors are multithreaded processors that apply processes or operating system threads in their hardware thread slots. These processors optimize the throughput of multiprogramming workloads rather than single-thread performance. We distinguish these processors from implicit multithreaded processors that utilize thread-level speculation by speculatively executing compiler- or machine-generated threads of control that are part of a single sequential program.This survey paper explains and classifies the explicit multithreading techniques in research and in commercial microprocessors.

https://www.di.unipi.it/~vannesch/SPA%202010-11/ungerer%20-%20multithreading.pdf

A survey of processors with explicit multithreading

The paper presents an overview of the parallel computing models, architectures, and research projects that are based on asynchronous instruction scheduling. It starts with pure dataflow computing models and presents an historical development of several ideas (i.e. single-token-per-arc dataflow, tagged-token dataflow, explicit token store, threaded dataflow, large-grain dataflow, RISC dataflow, cycle-by-cycle interleaved multithreading, block interleaved multithreading, simultaneous multithreading) that resulted in modern multithreaded superscalar processors. The paper shows that unification of von Neumann and dataflow models is possible and preferred to treating them as two unrelated, orthogonal computing paradigms. Today's dataflow research incorporates more explicit notions of state into the architecture, and von Neumann models using many dataflow techniques to improve the latency hiding aspects of modern multithreaded systems.

Asynchrony in parallel computing: from dataflow to multithreading

The differential ant-stigmergy algorithm

A comparison of models for forecasting the residential natural gas demand of an urban area

Many real-world optimisation problems are of dynamic nature, requiring an optimisation algorithm which is able to continuously track a changing optimum over time. To achieve this, we propose two population-based algorithms for solving dynamic optimisation problems DOPs with continuous variables: the self-adaptive differential evolution algorithm jDE and the differential ant-stigmergy algorithm DASA. The performances of the jDE and the DASA are evaluated on the set of well-known benchmark problems provided for the special session on Evolutionary Computation in Dynamic and Uncertain Environments. We analyse the results for five algorithms presented by using the non-parametric statistical test procedure. The two proposed algorithms show a consistently superior performance over other recently proposed methods. The results show that both algorithms are appropriate candidates for DOPs.

Jurij Šilc

Papers

A survey of processors with explicit multithreading

Asynchrony in parallel computing: from dataflow to multithreading

The differential ant-stigmergy algorithm

A comparison of models for forecasting the residential natural gas demand of an urban area

Differential evolution and differential ant-stigmergy on dynamic optimisation problems