Network acceleration techniques

Splintered offloading techniques with receive batch processing are described for network acceleration. Such techniques offload specific functionality to a NIC while maintaining the bulk of the protocol processing in the host operating system ("OS"). The resulting protocol implementation allows the application to bypass the protocol processing of the received data. Such can be accomplished this by moving data from the NIC directly to the application through direct memory access ("DMA") and batch processing the receive headers in the host OS when the host OS is interrupted to perform other work. Batch processing receive headers allows the data path to be separated from the control path. Unlike operating system bypass, however, the operating system still fully manages the network resource and has relevant feedback about traffic and flows. Embodiments of the present disclosure can therefore address the challenges of networks with extreme bandwidth delay products (BWDP)

Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI [electronic resource] : 17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020, Oak Ridge, TN, USA, August 26-28, 2020, Revised Selected Papers /

This book constitutes the revised selected papers of the 17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020, held in Oak Ridge, TN, USA*, in August 2020. The 36 full papers and 1 short paper presented were carefully reviewed and selected from a total of 94 submissions. The papers are organized in topical sections of computational applications: converged HPC and artificial intelligence; system software: data infrastructure and life cycle; experimental/observational applications: use cases that drive requirements for AI and HPC convergence; deploying computation: on the road to a converged ecosystem; scientific data challenges. *The conference was held virtually due to the COVID-19 pandemic.Computational Applications: Converged HPC and Artificial Intelligence -- System Software: Data Infrastructure and Life Cycle -- Experimental/Observational Applications: Use Cases That Drive Requirements for AI and HPC Convergence -- Deploying Computation: On the Road to a Converged Ecosystem -- Scientific Data Challenges.This book constitutes the revised selected papers of the 17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020, held in Oak Ridge, TN, USA*, in August 2020. The 36 full papers and 1 short paper presented were carefully reviewed and selected from a total of 94 submissions. The papers are organized in topical sections of computational applications: converged HPC and artificial intelligence; system software: data infrastructure and life cycle; experimental/observational applications: use cases that drive requirements for AI and HPC convergence; deploying computation: on the road to a converged ecosystem; scientific data challenges. *The conference was held virtually due to the COVID-19 pandemic