Run‐Time and Compiler Support for Programming in Adaptive Parallel Environments

Edjlali, Guy; Agrawal, Gagan; Sussman, Alan; Humphries, Jim; Saltz, Joel

doi:10.1155/1997/926796

Cited by 17 publications

(8 citation statements)

References 3 publications

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“…Adaptive Thread Management [21] optimizes the number of resources to data-parallel loops in automatically parallelized programs [20] during execution, according to the online comparison of current speedup against expect values. Adaptive Multiblock PARTI runtime library [15] follows a similar approach, providing great portability. Dynamic feedback [13] provides a compiler assisted optimization scheme, adapting to different compiler generated synchronization policy on alternating phases.…”

Section: Related Workmentioning

confidence: 99%

Palirria: accurate on‐line parallelism estimation for adaptive work‐stealing

Varisteas

Brorsson

2015

Concurrency and Computation

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We present Palirria, a self-adapting work-stealing scheduling method for nested fork/join parallelism that can be used to estimate the number of utilizable workers and self-adapt accordingly. The estimation mechanism is optimized for accuracy, minimizing the requested resources without degrading performance. We implemented Palirria for both the Linux and Barrelfish operating systems and evaluated it on two platforms: a 48-core NUMA multiprocessor and a simulated 32-core system. Compared to state-of-the-art, we observed higher accuracy in estimating resource requirements. This leads to improved resource utilization and performance on par or better to executing with fixed resource allotments.

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Section: Related Workmentioning

confidence: 99%

Palirria: accurate on‐line parallelism estimation for adaptive work‐stealing

Varisteas

Brorsson

2015

Concurrency and Computation

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“…Adaptive task scheduling without parallelism feedback has also been studied empirically in the context of data-parallel languages [20,21]. This work focuses on compiler and runtime support for environments where the number of processors changes while the program executes.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Scheduling with Parallelism Feedback

Agrawal

Hsu

et al. 2007

2007 IEEE International Parallel and Distributed Processing Symposium

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Multiprocessor scheduling in a shared multiprogramming environment is often structured as two-level scheduling, where a kernellevel job scheduler allots processors to jobs and a user-level task scheduler schedules the work of a job on the allotted processors. In this context, the number of processors allotted to a particular job may vary during the job's execution, and the task scheduler must adapt to these changes in processor resources. For overall system efficiency, the task scheduler should also provide parallelism feedback to the job scheduler to avoid the situation where a job is allotted processors that it cannot use productively.We present an adaptive task scheduler for multitasked jobs with dependencies that provides continual parallelism feedback to the job scheduler in the form of requests for processors. Our scheduler guarantees that a job completes near optimally while utilizing at least a constant fraction of the allotted processor cycles. Our scheduler can be applied to schedule data-parallel programs, such as those written in High Performance Fortran (HPF), *Lisp, C*, NESL, and ZPL.Our analysis models the job scheduler as the task scheduler's adversary, challenging the task scheduler to be robust to the system environment and the job scheduler's administrative policies. For example, the job scheduler can make available a huge number of processors exactly when the job has little use for them. To analyze the performance of our adaptive task scheduler under this stringent adversarial assumption, we introduce a new technique called "trim analysis," which allows us to prove that our task scheduler performs poorly on at most a small number of time steps, exhibiting nearoptimal behavior on the vast majority.To be precise, suppose that a job has work T1 and critical-path length T∞ and is running on a machine with P processors. Using trim analysis, we prove that our scheduler completes the job in O(T1/ P + T∞ + L lg P ) time steps, where L is the length of a scheduling quantum and P denotes the O(T∞ + L lg P )-trimmed availability. This quantity is the average of the processor availabilThis research was supported in part by the Singapore-MIT Alliance and NSF Grant ACI-0324974. Yuxiong He is a Visiting Scholar at MIT CSAIL and a Ph.D. candidate at the National University of Singapore. Wen Jing Hsu is a Visiting Scientist at MIT CSAIL and Associate Professor at Nanyang Technological University.Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. ity over all time steps excluding the O(T∞ + L lg P ) time steps with the highest processor availability. When T1/T∞ P (the job's parallelism dominates the O(T∞ + L lg P )-trimmed availability), the job ach...

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“…With respect to them, ASSIST is able to target heterogeneous architectures, at a higher level of abstraction. The extensions of parallel programming languages (OpenMP [15], HPF [16]) proposed, are not enough flexible for a grid-like environment (e.g. they cannot acquire new PEs at run-time).…”

Section: Related Workmentioning

confidence: 99%

Dynamic Reconfiguration of Grid-Aware Applications in ASSIST

Aldinucci

Petrocelli

Pistoletti

et al. 2005

Euro-Par 2005 Parallel Processing

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Abstract. Current grid-aware applications are implemented on top of low-level libraries by developers who are experts on grid middleware architecture. This approach can hardly support the additional complexity of QoS control in real applications. We discuss a novel approach used in the ASSIST programming environment to implement/guarantee user provided QoS contracts in a transparent and effective way. Our approach is based on the implementation of automatic run-time reconfiguration of ASSIST application executions triggered by mismatch between the user provided QoS contract and the actual performance values achieved.

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Run‐Time and Compiler Support for Programming in Adaptive Parallel Environments

Cited by 17 publications

References 3 publications

Palirria: accurate on‐line parallelism estimation for adaptive work‐stealing

Palirria: accurate on‐line parallelism estimation for adaptive work‐stealing

Adaptive Scheduling with Parallelism Feedback

Dynamic Reconfiguration of Grid-Aware Applications in ASSIST

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