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.

GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

Abstract: Scaling up deep neural network capacity has been known as an effective approach to improving model quality for several different machine learning tasks. In many cases, increasing model capacity beyond the memory limit of a single accelerator has required developing special algorithms or infrastructure. These solutions are often architecture-specific and do not transfer to other machine learning tasks. To address the need for efficient and task-independent model parallelism, we introduce TensorPipe, a pipeline parallelism library that allows scaling any network that can be expressed as a sequence of layers. By pipelining different sub-sequences of layers on separate accelerators, TensorPipe provides the flexibility of scaling a variety of different networks to gigantic sizes efficiently. Moreover, TensorPipe utilizes a novel batch-splitting pipelining algorithm, resulting in almost linear speedup when a model is partitioned across multiple accelerators. We demonstrate the advantages of TensorPipe by training large-scale neural networks on two different tasks with distinct network architectures: (i)Image Classification: We train a 557-million-parameter AmoebaNet model and attain a top-1 accuracy of 84.4% on ImageNet-2012, (ii)Multilingual Neural Machine Translation: We train a single 6-billion-parameter, 128-layer Transformer model on a corpus spanning over 100 languages and achieve better quality than all bilingual models.

Pipeline gating: speculation control for energy reduction

Abstract: Branch prediction has enabled microprocessors to increase instruction level parallelism (ILP) by allowing programs to speculatively execute beyond control boundaries. Although speculative execution is essential for increasing the instructions per cycle (IPC), it does come at a cost. A large amount of unnecessary work results from wrong-path instructions entering the pipeline due to branch misprediction. Results generated with the SimpleScalar tool set using a 4-way issue pipeline and various branch predictors show an instruction overhead of 16% to 105% for event instruction committed. The instruction overhead will increase in the future as processors use more aggressive speculation and wider issue widths. In this paper we present an innovative method for power reduction ,which, unlike previous work that sacrificed flexibility or performance reduces power in high-performance microprocessors without impacting performance. In particular we introduce a hardware mechanism called pipeline gating to control rampant speculation in the pipeline. We present inexpensive mechanisms for determining when a branch is likely to mispredict, and for stopping wrong-path instructions from entering the pipeline. Results show up to a 38% reduction in wrong-path instructions with a negligible performance loss (/spl ap/1%). Best of all, even in programs with a high branch prediction accuracy, performance does not noticeable degrade. Our analysis indicates that there is little risk in implementing this method in existing processors since it does not impact performance and can benefit energy reduction.

Deferred shading graphics pipeline processor having advanced features

Abstract: A deferred shading graphics pipeline processor and method are provided encompassing numerous substructures. Embodiments of the processor and method may include one or more of deferred shading, a tiled frame buffer, and multiple?stage hidden surface removal processing. In the deferred shading graphics pipeline, hidden surface removal is completed before pixel coloring is done. The pipeline processor comprises a command fetch and decode unit, a geometry unit, a mode extraction unit, a sort unit, a setup unit, a cull unit, a mode injection unit, a fragment unit, a texture unit, a Phong lighting unit, a pixel unit, and a backend unit.

ML confidential: machine learning on encrypted data

Abstract: We demonstrate that, by using a recently proposed leveled homomorphic encryption scheme, it is possible to delegate the execution of a machine learning algorithm to a computing service while retaining confidentiality of the training and test data. Since the computational complexity of the homomorphic encryption scheme depends primarily on the number of levels of multiplications to be carried out on the encrypted data, we define a new class of machine learning algorithms in which the algorithm's predictions, viewed as functions of the input data, can be expressed as polynomials of bounded degree. We propose confidential algorithms for binary classification based on polynomial approximations to least-squares solutions obtained by a small number of gradient descent steps. We present experimental validation of the confidential machine learning pipeline and discuss the trade-offs regarding computational complexity, prediction accuracy and cryptographic security.