High Performance Computing Design by Code Migration for Distributed Desktop Computing Grids

2011 ◽  
Vol 3 (4) ◽  
pp. 53-70
Author(s):  
Makoto Yoshida ◽  
Kazumine Kojima
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Workload Management ◽  
Transfer Policy ◽  
Loosely Coupled ◽  
Design Methodologies ◽  
Processing Power ◽  
Network Delays ◽  
Desktop Computing ◽  
Code Migration

Large scale loosely coupled PCs can organize clusters and form desktop computing grids on sharing each processing power; power of PCs, transaction distributions, network scales, network delays, and code migration algorithms characterize the performance of the computing grids. This article describes the design methodologies of workload management in distributed desktop computing grids. Based on the code migration experiments, transfer policy for computation was determined and several simulations for location policies were examined, and the design methodologies for distributed desktop computing grids are derived from the simulation results. The language for distributed desktop computing is designed to accomplish the design methodologies.

High Performance Computing Design by Code Migration for Distributed Desktop Computing Grids

2013 ◽  
pp. 185-203
Author(s):  
Makoto Yoshida ◽  
Kazumine Kojima
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Workload Management ◽  
Transfer Policy ◽  
Loosely Coupled ◽  
Design Methodologies ◽  
Processing Power ◽  
Network Delays ◽  
Desktop Computing ◽  
Code Migration

Large scale loosely coupled PCs can organize clusters and form desktop computing grids on sharing each processing power; power of PCs, transaction distributions, network scales, network delays, and code migration algorithms characterize the performance of the computing grids. This article describes the design methodologies of workload management in distributed desktop computing grids. Based on the code migration experiments, transfer policy for computation was determined and several simulations for location policies were examined, and the design methodologies for distributed desktop computing grids are derived from the simulation results. The language for distributed desktop computing is designed to accomplish the design methodologies.

Synchronizing Execution of Big Data in Distributed and Parallelized Environments

Big Data ◽  
2016 ◽  
pp. 1555-1581
Author(s):  
Gueyoung Jung ◽  
Tridib Mukherjee
Keyword(s):  
Big Data ◽  
Distributed System ◽  
Data Analytics ◽  
High Performance ◽  
Large Scale ◽  
Big Data Analytics ◽  
Loosely Coupled ◽  
Current Trends ◽  
Distributed Computing Infrastructures ◽  
Performance Computing

In the modern information era, the amount of data has exploded. Current trends further indicate exponential growth of data in the future. This prevalent humungous amount of data—referred to as big data—has given rise to the problem of finding the “needle in the haystack” (i.e., extracting meaningful information from big data). Many researchers and practitioners are focusing on big data analytics to address the problem. One of the major issues in this regard is the computation requirement of big data analytics. In recent years, the proliferation of many loosely coupled distributed computing infrastructures (e.g., modern public, private, and hybrid clouds, high performance computing clusters, and grids) have enabled high computing capability to be offered for large-scale computation. This has allowed the execution of the big data analytics to gather pace in recent years across organizations and enterprises. However, even with the high computing capability, it is a big challenge to efficiently extract valuable information from vast astronomical data. Hence, we require unforeseen scalability of performance to deal with the execution of big data analytics. A big question in this regard is how to maximally leverage the high computing capabilities from the aforementioned loosely coupled distributed infrastructure to ensure fast and accurate execution of big data analytics. In this regard, this chapter focuses on synchronous parallelization of big data analytics over a distributed system environment to optimize performance.

Data Storage, Retrieval and Management

2010 ◽  
pp. 111-140
Author(s):  
Valentin Cristea ◽  
Ciprian Dobre ◽  
Corina Stratan ◽  
Florin Pop
Keyword(s):  
Data Storage ◽  
Resource Sharing ◽  
High Performance ◽  
Large Scale ◽  
Workflow Management ◽  
Large Data ◽  
Data Retrieval ◽  
Distributed Data Storage ◽  
Processing Power ◽  
Data Transfers

The latest advances in network and distributedsystem technologies now allow integration of a vast variety of services with almost unlimited processing power, using large amounts of data. Sharing of resources is often viewed as the key goal for distributed systems, and in this context the sharing of stored data appears as the most important aspect of distributed resource sharing. Scientific applications are the first to take advantage of such environments as the requirements of current and future high performance computing experiments are pressing, in terms of even higher volumes of issued data to be stored and managed. While these new environments reveal huge opportunities for large-scale distributed data storage and management, they also raise important technical challenges, which need to be addressed. The ability to support persistent storage of data on behalf of users, the consistent distribution of up-to-date data, the reliable replication of fast changing datasets or the efficient management of large data transfers are just some of these new challenges. In this chapter we discuss how the existing distributed computing infrastructure is adequate for supporting the required data storage and management functionalities. We highlight the issues raised from storing data over large distributed environments and discuss the recent research efforts dealing with challenges of data retrieval, replication and fast data transfers. Interaction of data management with other data sensitive, emerging technologies as the workflow management is also addressed.

scholarly journals CHRONO: a parallel multi-physics library for rigid-body, flexible-body, and fluid dynamics

2013 ◽  
Vol 4 (1) ◽  
pp. 49-64 ◽  
Author(s):  
H. Mazhar ◽  
T. Heyn ◽  
A. Pazouki ◽  
D. Melanz ◽  
A. Seidl ◽  
...  
Keyword(s):  
Parallel Computing ◽  
Large Scale ◽  
Flexible Structures ◽  
Particulate Flows ◽  
Loosely Coupled ◽  
Processing Power ◽  
Select Group ◽  
Physics Simulation ◽  
Physics Modeling ◽  
Many Core

Abstract. The last decade witnessed a manifest shift in the microprocessor industry towards chip designs that promote parallel computing. Until recently the privilege of a select group of large research centers, Teraflop computing is becoming a commodity owing to inexpensive GPU cards and multi to many-core x86 processors. This paradigm shift towards large scale parallel computing has been leveraged in CHRONO, a freely available C++ multi-physics simulation package. CHRONO is made up of a collection of loosely coupled components that facilitate different aspects of multi-physics modeling, simulation, and visualization. This contribution provides an overview of CHRONO::Engine, CHRONO::Flex, CHRONO::Fluid, and CHRONO::Render, which are modules that can capitalize on the processing power of hundreds of parallel processors. Problems that can be tackled in CHRONO include but are not limited to granular material dynamics, tangled large flexible structures with self contact, particulate flows, and tracked vehicle mobility. The paper presents an overview of each of these modules and illustrates through several examples the potential of this multi-physics library.

scholarly journals Hierarchical algorithms on hierarchical architectures

2020 ◽  
Vol 378 (2166) ◽  
pp. 20190055 ◽  
Author(s):  
D. E. Keyes ◽  
H. Ltaief ◽  
G. Turkiyyah
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Fast Multipole Method ◽  
Numerical Algorithms ◽  
Building Blocks ◽  
Linear Operators ◽  
Low Rank ◽  
Processing Power ◽  
Storage Complexity ◽  
Time And Energy

A traditional goal of algorithmic optimality, squeezing out flops, has been superseded by evolution in architecture. Flops no longer serve as a reasonable proxy for all aspects of complexity. Instead, algorithms must now squeeze memory, data transfers, and synchronizations, while extra flops on locally cached data represent only small costs in time and energy. Hierarchically low-rank matrices realize a rarely achieved combination of optimal storage complexity and high-computational intensity for a wide class of formally dense linear operators that arise in applications for which exascale computers are being constructed. They may be regarded as algebraic generalizations of the fast multipole method. Methods based on these hierarchical data structures and their simpler cousins, tile low-rank matrices, are well proportioned for early exascale computer architectures, which are provisioned for high processing power relative to memory capacity and memory bandwidth. They are ushering in a renaissance of computational linear algebra. A challenge is that emerging hardware architecture possesses hierarchies of its own that do not generally align with those of the algorithm. We describe modules of a software toolkit, hierarchical computations on manycore architectures, that illustrate these features and are intended as building blocks of applications, such as matrix-free higher-order methods in optimization and large-scale spatial statistics. Some modules of this open-source project have been adopted in the software libraries of major vendors. This article is part of a discussion meeting issue ‘Numerical algorithms for high-performance computational science’.

Synchronizing Execution of Big Data in Distributed and Parallelized Environments

2013 ◽  
pp. 47-71 ◽  
Author(s):  
Gueyoung Jung ◽  
Tridib Mukherjee
Keyword(s):  
Big Data ◽  
Distributed System ◽  
Data Analytics ◽  
High Performance ◽  
Large Scale ◽  
Big Data Analytics ◽  
Loosely Coupled ◽  
Current Trends ◽  
Distributed Computing Infrastructures ◽  
Performance Computing

In the modern information era, the amount of data has exploded. Current trends further indicate exponential growth of data in the future. This prevalent humungous amount of data—referred to as big data—has given rise to the problem of finding the “needle in the haystack” (i.e., extracting meaningful information from big data). Many researchers and practitioners are focusing on big data analytics to address the problem. One of the major issues in this regard is the computation requirement of big data analytics. In recent years, the proliferation of many loosely coupled distributed computing infrastructures (e.g., modern public, private, and hybrid clouds, high performance computing clusters, and grids) have enabled high computing capability to be offered for large-scale computation. This has allowed the execution of the big data analytics to gather pace in recent years across organizations and enterprises. However, even with the high computing capability, it is a big challenge to efficiently extract valuable information from vast astronomical data. Hence, we require unforeseen scalability of performance to deal with the execution of big data analytics. A big question in this regard is how to maximally leverage the high computing capabilities from the aforementioned loosely coupled distributed infrastructure to ensure fast and accurate execution of big data analytics. In this regard, this chapter focuses on synchronous parallelization of big data analytics over a distributed system environment to optimize performance.

The Structure and Properties of MoSi2 Thin Film in Mos Process

1980 ◽  
Vol 38 ◽  
pp. 326-327
Author(s):  
C.K. Wu ◽  
P. Chang ◽  
N. Godinho
Keyword(s):  
Thin Film ◽  
Integrated Circuits ◽  
High Performance ◽  
Large Scale ◽  
Process Development ◽  
Structure And Properties ◽  
Metal Silicides ◽  
High Oxidation ◽  
Important Approach ◽  
High Oxidation Resistance

Recently, the use of refractory metal silicides as low resistivity, high temperature and high oxidation resistance gate materials in large scale integrated circuits (LSI) has become an important approach in advanced MOS process development (1). This research is a systematic study on the structure and properties of molybdenum silicide thin film and its applicability to high performance LSI fabrication.

RECOMMENDATIONS FOR THE CHOICE OF MILKING INSTALLATIONS IN LOOSE HOUSING SYSTEMS OF COWS

2020 ◽  
Author(s):  
В.В. ГОРДЕЕВ ◽  
В.Е. ХАЗАНОВ
Keyword(s):  
Dairy Cows ◽  
High Performance ◽  
Large Scale ◽  
Dairy Farms ◽  
Economic Indicators ◽  
Technical Level ◽  
Housing Systems ◽  
Working Shift ◽  
Technical And Economic Indicators

При выборе типа доильной установки и ее размера необходимо учитывать максимальное планируемое поголовье дойных коров и размер технологической группы, кратность и время одного доения, продолжительность рабочей смены дояров. Анализ технико-экономических показателей наиболее распространенных на сегодняшний день типов доильных установок одинакового технического уровня свидетельствует, что наилучшие удельные показатели имеет установка типа «Карусель» (1), а установка типа «Елочка» (2) требует более высоких затрат труда и средств. Установка «Параллель» (3) занимает промежуточное положение. Из анализа пропускной способности и количества необходимых операторов: установка 2 рекомендована для ферм с поголовьем дойного стада до 600 голов, 3 — не более 1200 дойных коров, 1 — более 1200 дойных коров. «Карусель» — наиболее рациональный, высокопроизводительный, легко автоматизируемый и, следовательно, перспективный способ доения в залах, особенно для крупных молочных ферм. The choice of the proper type and size of milking installations needs to take into account the maximum planned number of dairy cows, the size of a technological group, the number of milkings per day, and the duration of one milking and the operator's working shift. The analysis of technical and economic indicators of currently most common types of milking machines of the same technical level revealed that the Carousel installation had the best specific indicators while the Herringbone installation featured higher labour inputs and cash costs. The Parallel installation was found somewhere in between. In terms of the throughput and the required number of operators Herringbone is recommended for farms with up to 600 dairy cows, Parallel — below 1200 dairy cows, Carousel — above 1200 dairy cows. Carousel was found the most practical, high-performance, easily automated and, therefore, promising milking system for milking parlours, especially on the large-scale dairy farms.

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