scholarly journals An Efficient Prototype for Gals Systems in Asynchronous Network-on-Chips through Multiclocking

2017 ◽  
Vol 6 (7) ◽  
pp. 1179-1183
Keyword(s):  
Asynchronous Network ◽  
Gals Systems

Investigation of transient fault effects in synchronous and asynchronous Network on Chip router

2011 ◽  
Vol 57 (1) ◽  
pp. 61-68 ◽  
Author(s):  
Pooria M. Yaghini ◽  
Ashkan Eghbal ◽  
Hossein Pedram ◽  
Hamid Reza Zarandi
Keyword(s):  
Network On Chip ◽  
Transient Fault ◽  
Asynchronous Network ◽  
On Chip

scholarly journals JAMPI: Efficient Matrix Multiplication in Spark Using Barrier Execution Mode

2020 ◽  
Author(s):  
Tamas Foldi ◽  
Chris von Csefalvay ◽  
Nicolas A. Perez
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Message Passing ◽  
Matrix Multiplication ◽  
Map Reduce ◽  
Distributed Training ◽  
Learning Tasks ◽  
Memory Footprint ◽  
Asynchronous Network ◽  
Execution Mode

The new barrier mode in Apache Spark allows embedding distributed deep learning training as a Spark stage to simplify the distributed training workflow. In Spark, a task in a stage doesn’t depend on any other tasks in the same stage, and hence it can be scheduled independently. However, several algorithms require more sophisticated inter-task communications, similar to the MPI paradigm. By combining distributed message passing (using asynchronous network IO), OpenJDK’s new auto-vectorization and Spark’s barrier execution mode, we can add non-map/reduce based algorithms, such as Cannon’s distributed matrix multiplication to Spark. We document an efficient distributed matrix multiplication using Cannon’s algorithm, which improves significantly on the performance of the existing MLlib implementation. Used within a barrier task, the algorithm described herein results in an up to 24% performance increase on a 10,000x10,000 square matrix with a significantly lower memory footprint. Applications of efficient matrix multiplication include, among others, accelerating the training and implementation of deep convolutional neural network based workloads, and thus such efficient algorithms can play a ground-breaking role in faster, more efficient execution of even the most complicated machine learning tasks

Power analysis for asynchronous network-on-chip

2013 ◽  
Vol 8 (S1) ◽  
pp. S72-S80
Author(s):  
Mohamed A. Abd El Ghany ◽  
Gursharan Reehal ◽  
Mohammed Ismail
Keyword(s):  
Power Analysis ◽  
Network On Chip ◽  
Asynchronous Network ◽  
On Chip
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