Highly parallel computation

Highly parallel computing architectures are the only means to achieve the computation rates demanded by advanced scientific problems. A decade of research has demonstrated the feasibility of such machines and current research focuses on which architectures designated as multiple instruction multiple datastream (MIMD) and single instruction multiple datastream (SIMD) have produced the best results to date; neither shows a decisive advantage for most near-homogeneous scientific problems. For scientific problems with many dissimilar parts, more speculative architectures such as neural networks or data flow may be needed

INTERNET-TESTING OF PHYSICS

The analysis of Internet testing as element of quality management of education on an example of physics is carried out. Remarks and offers on optimization of codificator, contents and results estimation of testing are formulated.Проведен анализ Интернет-тестирования как элемента управления качеством образования на примере дисциплины «физика», сформулированы замечания и предложения по оптимизации кодификатора, содержания и оценки результатов тестирования

Affordable Supercomputer in an Academic Environment. Cloud Computing in Classrooms

Cloud computing technologies are widely used by large corporations, but nowa-days they become more available to research institutions. In this article author suggests a model of creating an affordable cluster for the academic environment needs.Технологии облачной обработки данных широко используются крупными коммерческими организациями, но в настоящее время они становятся доступны даже исследовательским лабораториям вузов или НИИ. В статье автор предлагает модель создания доступного кластера для удовлетворения потребностей академической среды в вычислительных мощностях

Asymmetric Load Balancing on a Heterogeneous Cluster of PCs

In recent years, high performance computing with commodity clusters of personal computers has become an active area of research. Many organizations build them because they need the computational speedup provided by parallel processing but cannot afford to purchase a supercomputer. With commercial supercomputers and homogenous clusters of PCs, applications that can be statically load balanced are done so by assigning equal tasks to each processor. With heterogeneous clusters, the system designers have the option of quickly adding newer hardware that is more powerful than the existing hardware. When this is done, the assignment of equal tasks to each processor results in suboptimal performance. This research addresses techniques by which the size of the tasks assigned to processors is a suitable match to the processors themselves, in which the more powerful processors can do more work, and the less powerful processors perform less work. We find that when the range of processing power is narrow, some benefit can be achieved with asymmetric load balancing. When the range of processing power is broad, dramatic improvements in performance are realized our experiments have shown up to 92% improvement when asymmetrically load balancing a modified version of the NAS Parallel Benchmarks\u27 LU application

Redundant disk arrays: Reliable, parallel secondary storage

During the past decade, advances in processor and memory technology have given rise to increases in computational performance that far outstrip increases in the performance of secondary storage technology. Coupled with emerging small-disk technology, disk arrays provide the cost, volume, and capacity of current disk subsystems, by leveraging parallelism, many times their performance. Unfortunately, arrays of small disks may have much higher failure rates than the single large disks they replace. Redundant arrays of inexpensive disks (RAID) use simple redundancy schemes to provide high data reliability. The data encoding, performance, and reliability of redundant disk arrays are investigated. Organizing redundant data into a disk array is treated as a coding problem. Among alternatives examined, codes as simple as parity are shown to effectively correct single, self-identifying disk failures