TTNET: a dual bus high-performance LAN/MAN for bulk data transfers

1992 ◽  
Author(s):  
Maria C. Yuang ◽  
P. Y. Chan
Keyword(s):  
High Performance ◽  
Bulk Data ◽  
Data Transfers

I-DMAC: An Intelligent DMA Controller for Utilization - Aware Video Streaming used in AI Applications

2021 ◽  
pp. 60-70
Author(s):  
Piyush Kumar Shukla ◽  
◽  
Prashant Kumar Shukla ◽  
Keyword(s):  
Video Processing ◽  
High Performance ◽  
Data Transfer ◽  
Direct Memory Access ◽  
Large Data ◽  
Video Frame ◽  
Microprocessor System ◽  
Bulk Data ◽  
Xilinx Fpga ◽  
Vhdl Code

The interpretation of large data streams necessitates high-performance repeated transfers, which overload Microprocessor System on Chips (SoC). The effective direct memory access (DMA) controller performs bulk data transfers without the CPU's involvement. The Direct Memory Controller (DMAC) solves this by facilitating bulk data transfer and execution. In this work, we created an intelligent DMAC (I-DMAC) for accessing video processing data without using CPUs. The model includes Bus selection Module, User control signal, Status Register, DMA supported Address, and AXI-PCI subsystems for improved video frame analysis. These modules are experimentally verified in Xilinx FPGA SoC architecture using VHDL code simulation and results compared to the E-DMAC model.

scholarly journals A Partial Store-and-Forward Scheduling Method for Inter-Datacenter Bulk Data Transfers

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 128167-128181
Author(s):  
Xiao Lin ◽  
Shengnan Yue ◽  
Yuanlong Tan ◽  
Weiqiang Sun ◽  
Malathi Veeraraghavan ◽  
...  
Keyword(s):  
Bulk Data ◽  
Scheduling Method ◽  
Data Transfers ◽  
Store And Forward

scholarly journals Blockchain Networks – Security Aspects and Consensus Models

2019 ◽  
Vol 2 ◽  
pp. 46-52 ◽  
Author(s):  
Andrzej Wilczyński ◽  
Adrian Widłak
Keyword(s):  
Data Processing ◽  
High Performance ◽  
Distributed Networks ◽  
Modern Technology ◽  
Main Aspect ◽  
Computing Systems ◽  
Consensus Models ◽  
Architectural Model ◽  
Data Transfers ◽  
Performance Computing

Data integration and fast effective data processing are the primary challenges in today’s high-performance computing systems used for Big Data processing and analysis in practical scenarios. Blockchain (BC) is a hot, modern technology that ensures high security of data processes stored in highly distributed networks and ICT infrastructures. BC enables secure data transfers in distributed systems without the need for all operations and processes in the network to be initiated and monitored by any central authority (system manager). This paper presents the background of a generic architectural model of a BC system and explains the concept behind the consensus models used in BC transactions. Security is the main aspect of all defined operations and BC nodes. The paper presents also specific BC use cases to illustrate the performance of the system in practical scenarios..

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 A Dynamic Programmable Network for Large-Scale Scientific Data Transfer Using AmoebaNet

2019 ◽  
Vol 9 (21) ◽  
pp. 4541
Author(s):  
Syed Asif Raza Shah ◽  
Seo-Young Noh
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Data Transfer ◽  
Scientific Data ◽  
Network Service ◽  
Bulk Data ◽  
Experimental Facilities ◽  
Bulk Data Transfer ◽  
Programmable Network ◽  
Extreme Scale

Large scientific experimental facilities currently are generating a tremendous amount of data. In recent years, the significant growth of scientific data analysis has been observed across scientific research centers. Scientific experimental facilities are producing an unprecedented amount of data and facing new challenges to transfer the large data sets across multi continents. In particular, these days the data transfer is playing an important role in new scientific discoveries. The performance of distributed scientific environment is highly dependent on high-performance, adaptive, and robust network service infrastructures. To support large scale data transfer for extreme-scale distributed science, there is the need of high performance, scalable, end-to-end, and programmable networks that enable scientific applications to use the networks efficiently. We worked on the AmoebaNet solution to address the problems of a dynamic programmable network for bulk data transfer in extreme-scale distributed science environments. A major goal of the AmoebaNet project is to apply software-defined networking (SDN) technology to provide “Application-aware” network to facilitate bulk data transfer. We have prototyped AmoebaNet’s SDN-enabled network service that allows application to dynamically program the networks at run-time for bulk data transfers. In this paper, we evaluated AmoebaNet solution with real world test cases and shown that how it efficiently and dynamically can use the networks for bulk data transfer in large-scale scientific environments.

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