Pipeline (computing)

About: Pipeline (computing) is a research topic. Over the lifetime, 26760 publications have been published within this topic receiving 204305 citations. The topic is also known as: data pipeline & computational pipeline.

Method to detect a stalled instruction stream and serialize micro-operation execution

Abstract: A computer implemented method, apparatus, and computer usable program code for ensuring forward progress of instructions in a pipeline of a processor. Instructions are received in the pipeline. Instruction flushes are counted in the pipeline to determine a flush count. A single step mode in the pipeline is entered in response to the flush count exceeding a threshold. The single step mode instructions are issued in serial such that an instruction is not issued for execution until a prior instruction has completed execution.

Coarse-grain pipelining on multiple FPGA architectures

Abstract: Reconfigurable systems, and in particular, FPGA-based custom computing machines, offer a unique opportunity to define application-specific architectures. These architectures offer performance advantages for application domains such as image processing, where the use of customized pipelines exploits the inherent coarse-grain parallelism. In this paper we describe a set of program analyses and an implementation that map a sequential and un-annotated C program into a pipelined implementation running on a set of FPGAs, each with multiple external memories. Based on well-known parallel computing analysis techniques, our algorithms perform unrolling for operator parallelization, reuse and data layout for memory parallelization and precise communication analysis. We extend these techniques for FPGA-based systems to automatically partition the application data and computation into custom pipeline stages, taking into account the available FPGA and interconnect resources. We illustrate the analysis components by way of an example, a machine vision program. We present the algorithm results, derived with minimal manual intervention, which demonstrate the potential of this approach for automatically deriving pipelined designs from high-level sequential specifications.

Openflow match and action pipeline structure

Abstract: An embodiment of the invention includes a packet processing pipeline. The packet processing pipeline includes match and action stages. Each match and action stage in incurs a match delay when match processing occurs and each match and action stage incurs an action delay when action processing occurs. A transport delay occurs between successive match and action stages when data is transferred from a first match and action stage to a second match and action stage.

rTPC and nls.multstart: A new pipeline to fit thermal performance curves in r

Abstract: 1. The quantification of thermal performance curves (TPCs) for biological rates has many applications to problems such as predicting species9 responses to climate change. There is currently no widely used open-source pipeline to fit mathematical TPC models to data, which limits the transparency and reproducibility of the curve fitting process underlying applications of TPCs. 2. We present a new pipeline in R that currently allows for reproducible fitting of 24 different TPC models using non-linear least squares (NLLS) regression. The pipeline consists of two packages - rTPC and nls.multstart - that allow multiple start values for NLLS fitting and provides helper functions for setting start parameters. This pipeline overcomes previous problems that have made NLLS fitting and estimation of key parameters difficult or unreliable. 3. We demonstrate how rTPC and nls.multstart can be combined with other packages in R to robustly and reproducibly fit multiple models to multiple TPC datasets at once. In addition, we show how model selection or averaging, weighted model fitting, and bootstrapping can easily be implemented within the pipeline. 4. This new pipeline provides a flexible and reproducible approach that makes the challenging task of fitting multiple TPC models to data accessible to a wide range of users.