Bulk data transfer distributer: a high performance multicast model in ALMA ACS

2006 ◽  
Author(s):  
R. Cirami ◽  
P. Di Marcantonio ◽  
G. Chiozzi ◽  
B. Jeram
Keyword(s):  
High Performance ◽  
Data Transfer ◽  
Bulk Data ◽  
Bulk Data Transfer

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.

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 Normalized Transfer of Bulk Data By Using UDP In Dedicated Networks

2012 ◽  
pp. 243-246
Author(s):  
Kurmachalam Ajay Kumar ◽  
Saritha Vemuri ◽  
Ralla Suresh
Keyword(s):  
High Speed ◽  
Data Transfer ◽  
Long Distance ◽  
Memory Processing ◽  
Data Intensive ◽  
Bulk Data ◽  
Network Links ◽  
Bulk Data Transfer ◽  
Dedicated Networks ◽  
Effective Buffer

High speed bulk data transfer is an important part of many data-intensive scientific applications. TCP fails for the transfer of large amounts of data over long distance across high-speed dedicated network links. Due to system hardware is incapable of saturating the bandwidths supported by the network and rise buffer overflow and packet-loss in the system. To overcome this there is a necessity to build a Performance Adaptive-UDP (PA-UDP) protocol for dynamically maximizing the implementation under different systems. A mathematical model and algorithms are used for effective buffer and CPU management. Performance Adaptive-UDP is a supreme protocol than other protocols by maintaining memory processing, packetloss processing and CPU utilization. Based on this protocol bulk data transfer is processed with high speed over the dedicated network links.

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