Millions Of Floating Point Operations Research Articles

Deep neural networks in the cloud: Review, applications, challenges and research directions

Deep neural networks (DNNs) are currently being deployed as machine learning technology in a wide range of important real-world applications. DNNs consist of a huge number of parameters that require millions of floating-point operations (FLOPs) to be executed both in learning and prediction modes. A more effective method is to implement DNNs in a cloud computing system equipped with centralized servers and data storage sub-systems with high-speed and high-performance computing capabilities. This paper presents an up-to-date survey on current state-of-the-art deployed DNNs for cloud computing. Various DNN complexities associated with different architectures are presented and discussed alongside the necessities of using cloud computing. We also present an extensive overview of different cloud computing platforms for the deployment of DNNs and discuss them in detail. Moreover, DNN applications already deployed in cloud computing systems are reviewed to demonstrate the advantages of using cloud computing for DNNs. The paper emphasizes the challenges of deploying DNNs in cloud computing systems and provides guidance on enhancing current and new deployments.

Future computer technology for large power system simulation

A true technological explosion has taken place in the computer hardware industry in the last few years. Words such as parallel processing, vector processors, array processors, pipelined machines, ‘number crunching’, and megaflops ( Millions of FLoating-point OPerations per Second) are heard regularly. Computer manufacturers have responded to the needs of specific groups requiring, above all else, high speed arithmetic capability. The result is a host of new machines which are called in this paper ‘vector processors’. This paper will assess the applicability of vector processors to power flow and transient stability simulation programs and will indicate how these programs should be organized to run efficiently on these new machines. The approach taken will be to survey the entire class of vector processors available now and in the near future, to attempt to raise the reported low efficiency of sparsity-coded programs for large vector processors by reorganizing their sparse structure.

An Evaluation of Present and Future Computer Technology for Large Scale Power System Simulation

A true technological explosion has taken place in the computer hardware industry in the last few years. Words such as parallel processing, vector processors, array processors, pipelined machines, “number crunching”, and megaflops (Millions of FLoating-point OPerations per Second) are heard regularly. Computer manufacturers have responded to the needs of specific groups requiring, above all else, high speed arithmetic capability. The result is a host of new machines which are called in this paper “vector processors”. This paper will assess the applicability of vector processors to power flow and transient stability simulation programs and will indicate how these programs should be organized to run efficiently on these new machines. The approach taken will be to survey the entire class of vector processors available now and in the near future, to attempt to raise the reported low efficiency of sparsity-coded programs for large vector processors by reorganizing their sparse structure.