Feedback-directed pipeline parallelism

Suleman, Muhammad; Qureshi, Moinuddin K.; Khubaib,; Patt, Yale N.

doi:10.1145/1854273.1854296

Cited by 51 publications

(48 citation statements)

References 18 publications

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“…Others have devised specific strategies to identify performance limiting stages [35]. Additional threads are assigned to the limiting stages and taken away from the others.…”

Section: Schedulingmentioning

confidence: 99%

Deterministic scale-free pipeline parallelism with hyperqueues

Vandierendonck

Chronaki

Nikolopoulos

2013

Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis

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Ubiquitous parallel computing aims to make parallel programming accessible to a wide variety of programming areas using deterministic and scale-free programming models built on a task abstraction. However, it remains hard to reconcile these attributes with pipeline parallelism, where the number of pipeline stages is typically hard-coded in the program and defines the degree of parallelism.This paper introduces hyperqueues, a programming abstraction that enables the construction of deterministic and scale-free pipeline parallel programs. Hyperqueues extend the concept of Cilk++ hyperobjects to provide thread-local views on a shared data structure. While hyperobjects are organized around private local views, hyperqueues require shared concurrent views on the underlying data structure. We define the semantics of hyperqueues and describe their implementation in a work-stealing scheduler. We demonstrate scalable performance on pipeline-parallel PARSEC benchmarks and find that hyperqueues provide comparable or up to 30% better performance than POSIX threads and Intel's Threading Building Blocks. The latter are highly tuned to the number of available processing cores, while programs using hyperqueues are scale-free.

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“…Others have devised specific strategies to identify performance limiting stages [35]. Additional threads are assigned to the limiting stages and taken away from the others.…”

Section: Schedulingmentioning

confidence: 99%

Deterministic scale-free pipeline parallelism with hyperqueues

Vandierendonck

Chronaki

Nikolopoulos

2013

Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis

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“…Pipeline parallelism 1 [6,16,17,25,27,28,31,33,35,37] is a well-known parallel-programming pattern that can be used to parallelize a variety of applications, including streaming applications from the domains of video, audio, and digital signal processing. Many applications, including the ferret, dedup, and x264 benchmarks from the PARSEC benchmark suite [4,5], exhibit parallelism in the form of a linear pipeline, where a linear sequence S = S 0 , .…”

Section: Introductionmentioning

confidence: 99%

On-the-fly pipeline parallelism

Lee¹,

Leiserson²,

Schardl³

et al. 2013

Proceedings of the Twenty-Fifth Annual ACM Symposium on Parallelism in Algorithms and Architectures

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Pipeline parallelism organizes a parallel program as a linear sequence of s stages. Each stage processes elements of a data stream, passing each processed data element to the next stage, and then taking on a new element before the subsequent stages have necessarily completed their processing. Pipeline parallelism is used especially in streaming applications that perform video, audio, and digital signal processing. Three out of 13 benchmarks in PARSEC, a popular software benchmark suite designed for shared-memory multiprocessors, can be expressed as pipeline parallelism.Whereas most concurrency platforms that support pipeline parallelism use a "construct-and-run" approach, this paper investigates "on-the-fly" pipeline parallelism, where the structure of the pipeline emerges as the program executes rather than being specified a priori. On-the-fly pipeline parallelism allows the number of stages to vary from iteration to iteration and dependencies to be data dependent. We propose simple linguistics for specifying on-the-fly pipeline parallelism and describe a provably efficient scheduling algorithm, the PIPER algorithm, which integrates pipeline parallelism into a work-stealing scheduler, allowing pipeline and fork-join parallelism to be arbitrarily nested. The PIPER algorithm automatically throttles the parallelism, precluding "runaway" pipelines. Given a pipeline computation with T 1 work and T ∞ span (critical-path length), PIPER executes the computation on P processors in T P ≤ T 1 /P + O(T ∞ + lg P) expected time. PIPER also limits stack space, ensuring that it does not grow unboundedly with running time.We have incorporated on-the-fly pipeline parallelism into a Cilkbased work-stealing runtime system. Our prototype Cilk-P implementation exploits optimizations such as lazy enabling and dependency folding. We have ported the three PARSEC benchmarks that exhibit pipeline parallelism to run on Cilk-P. One of these, x264, cannot readily be executed by systems that support only construct-and-run pipeline parallelism. Benchmark results indicate that Cilk-P has low serial overhead and good scalability. On x264, for example, Cilk-P exhibits a speedup of 13.87 over its respective serial counterpart when running on 16 processors.

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“…Thus, the data sharing as a source is available every time, and anywhere. The second type is the CPU power source, where it has a set of threads running in parallel computing (pipeline programming) to different clients over network [18]. According to the two previous types, the VML middleware uses the network resources to become a virtual supercomputer.…”

Section: Vml Middlewarementioning

confidence: 99%

Data Warehouse Signature: A Framework for Implementing Security Issues in Data Warehouses

AlMeghari¹

2017

JCSA

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Today, with the increase in the number of business web application users, the data size is obviously getting quiet huge. This data is composed together from integrated heterogeneous data sources to form Data Warehouse (DW). DW consists of critical and sensitive data used in many intelligent business models for decision support processes. Therefore, retaining data security for DW systems is a very important issue for researchers; also, it is the first priority for organizations. The current paper presents a new framework for implementing security issues in DWs named Data Warehouse Signature (DWS). The DWS framework ensures the security issues, which are Confidentiality, Integrity and Availability (CIA). In fact, this approach solves the security problems such as network overhead and data changing that occurs during transmission on Client-Server basis. The DWS approach achieves high performance by using parallel computing through a middleware named View Manager Layer (VML). Finally, this paper also presents the elastic architecture of the DWS framework with a vast applicability in multiple DW organizations depending on its network infrastructure.

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Feedback-directed pipeline parallelism

Cited by 51 publications

References 18 publications

Deterministic scale-free pipeline parallelism with hyperqueues

Deterministic scale-free pipeline parallelism with hyperqueues

On-the-fly pipeline parallelism

Data Warehouse Signature: A Framework for Implementing Security Issues in Data Warehouses

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