Програмний комплекс для монiторингу та пiдтримки експлуатацiї транспортної мережi зв’язку iз використанням технологiї GPGPU

На основi дослiджень розроблено програмне забезпечення, яке задовольняє задачу скорочення витрат при обслуговуваннi транспортної мережi. Програма, забезпечує прийняття рiшень у складних i позаштатних ситуацiях, при цьому мiнiмiзуючи помилки, пов’язанi з людським фактором

Building textual OLAP cubes using real-time intelligent heterogeneous approach

This article describes how the ever-growing amount of data entails introducing innovative solutions in or-der to capture, process, and store the information. OLAP has been considered a powerful analytical technology that enables analysts to gain insight into data and project information from diversified points of view. Thereupon, OLAP has been utilized in a broad spectrum of sensitive applications in the industry. The technology has occupied its place at the forefront of the vibrant information technology landscape of research in order to meet the evolving needs. One of these needs that has enticed the researchers’ attention is providing real-time answers which suggests, in particular cases, processing billions of records in few seconds or less. The limited processing capacities have arisen as a major hurdle in the way of achieving such an aim. Although numerous improvements have been suggested, few have considered the heterogeneous computing approach, whereby quantum leap in terms of the response time has been achieved, albeit in most cases, only numerical data have been utilized. In this article, the authors introduce a novel heterogeneous OLAP approach targets textual OLAP cubes aggregation and can be utilized efficiently in OLAP-based pattern recognition problems. In this context, the approach (a) exploits the GPU along with the CPU in order to process textual data. (b) Stores the queries aggregations’ hash table in the global memory such that the higher aggregations levels are being answered in a shorter time (c) Introduces an intelligent self-evaluating mechanism (ISEM), that evaluates the resource efficiency on query-basis by deciding which resource (CPU or GPU+CPU) is more reliable to process each query. The authors’ empirical results have shown the achieved gain is up to thirty-two folds over the parallel CPU-based counterpart solution. Furthermore, their approach has demonstrated that adopting aggregation-memory optimization significantly improves the performance of high-level textual aggregations

Прикладні аспекти застосування інтелектуальних технологій в системах підтримки експлуатації транспортної мережі зв'язку

В проведених дослідженнях проаналізовано способи підвищення продуктивності інтелектуально-комп’ютерних засобів підтримки експлуатації транспортної мережі зв'язку на основі застосування GPGPU технологій паралельних обчислень та технологій інтелектуального аналізу даних. Розроблено програмний засіб, призначений для обліку каналів транспортної мережі зв’язку.В проведенных исследованиях проанализированы способы повышения производительности интеллектуально-компьютерных средств поддержки эксплуатации транспортной сети связи на базе применение GPGPU технологий параллельных вычислений и технологий интеллектуального анализа данных. Разработан программное средство, предназначенное для учета каналов транспортной сети связи.In the study analyzed ways to improve intellectual performance computer tools to support the operation of the transport network communication through the use of GPGPU technologies of parallel computing and data mining technologies. Developed a software tool designed to account for feed transport network

Анализ эффективности использования графических процессоров для ускорения обработки SQL-запросов

В работе представляются результаты исследования эффективности использования вычислительной мощности графического процессора для выполнения задач СУБД. Используя графический процессор для решения подобного рода задач, можно освободить ресурсы на центральном процессоре для других задач и тем самым обеспечить более быстрое выполнение

Аппаратная реализация СУБД в инфологических системах

The article discusses the project hardware and software implementation database management system (DBMS) using programmable logic chips (FPGA) as a system on chip (SoC). A possible implementation, and evaluation.В статье рассматривается проект программно-аппаратной реализации системы управления базами данных (СУБД) с использованием микросхем программируемой логики (ПЛМ) в виде системы на кристалле (СнК). Приводится вариант реализации и оценка эффективности

Efficient Cross-Device Query Processing

The increasing diversity of hardware within a single system promises large performance gains but also poses a challenge for data management systems. Strategies for the efficient use of hardware with large performance differences are still lacking. For example, existing research on GPU supported data management largely handles the GPU in isolation from the system’s CPU — The GPU is considered the central processor and the CPU used only to mitigate the GPU’s weaknesses where necessary. To make efficient use of all available devices, we developed a processing strategy that lets unequal devices like GPU and CPU combine their strengths rather than work in isolation. To this end, we decompose relational data into individual bits and place the resulting partitions on the appropriate devices. Operations are processed in phases, each phase executed on one device. This way, we achieve significant performance gains and good load distribution among the available devices in a limited real-life use case. To grow this idea into a generic system, we identify challenges as well as potential hardware configurations and applications that can benefit from this approach

A Parallelization of Non-Serial Polyadic Dynamic Programming on GPU

Parallelization of Non-Serial Polyadic Dynamic Programming (NPDP) on high-throughput manycore architectures, such as NVIDIA GPUs, suffers from load imbalance, i.e. non-optimal mapping between the sub-problems of NPDP and the processing elements of the GPU. NPDP exhibits non-uniformity in the number of subproblems as well as computational complexity across the phases. In NPDP parallelization, phases are computed sequentially whereas subproblems of each phase are computed concurrently. Therefore, it is essential to effectively map the subproblems of each phase to the processing elements while implementing thread level parallelism. We propose an adaptive Generalized Mapping Method (GMM) for NPDP parallelization that utilizes the GPU for efficient mapping of subproblems onto processing threads in each phase. Input-size and targeted GPU decide the computing power and the best mapping for each phase in NPDP parallelization. The performance of GMM is compared with different conventional parallelization approaches. For sufficiently large inputs, our technique outperforms the state-of-the-art conventional parallelization approach and achieves a significant speedup of a factor 30. We also summarize the general heuristics for achieving better gain in the NPDP parallelization

ACCELERATING SELECT WHERE AND SELECT JOIN QUERIES ON A GPU

This paper presents implementations of a few selected SQL operations using theCUDA programming framework on the GPU platform. Nowadays, the GPU’sparallel architectures give a high speed-up on certain problems. Therefore, thenumber of non-graphical problems that can be run and sped-up on the GPUstill increases. Especially, there has been a lot of research in data mining onGPUs. In many cases it proves the advantage of offloading processing fromthe CPU to the GPU. At the beginning of our project we chose the set ofSELECT WHERE and SELECT JOIN instructions as the most common op-erations used in databases. We parallelized these SQL operations using threemain mechanisms in CUDA: thread group hierarchy, shared memories, andbarrier synchronization. Our results show that the implemented highly parallelSELECT WHERE and SELECT JOIN operations on the GPU platform canbe significantly faster than the sequential one in a database system run on theCPU