Knowledge-Based Adaptive Self-Scheduling

Wang, Yizhuo; Ji, Weixing; Shi, Feng; Zuo, Qiting; Deng, Ning

doi:10.1007/978-3-642-35606-3_3

Cited by 8 publications

(18 citation statements)

References 13 publications

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“…LavaMD is an application kernel from Computational Fluid Dynamics (CFD) that performs N‐Body Simulations. N‐Body Simulations find applications in several scientific and engineering domains and are frequently studied within the context of loop scheduling . LavaMD was extracted from the Rodinia Benchmarks Suite, and it carries out a high‐resolution simulation of the pressure‐induced solidification of molten tantalum and quenched uranium atoms in a finitely‐sized three‐dimensional domain.…”

Section: Evaluation Methodologymentioning

confidence: 99%

“…Loop Sizes and Problem Sizes were chosen so as to reflect the full processing capacity of the experimental platform (detailed later in this section). Baseline strategies were selected to be consistent with related works . The GSS (guided) and CSS (dynamic) loop schedulers are shipped with libGOMP by default, thus we used them in our Synthetic Kernel Benchmarking and Application Kernel Benchmarking .…”

Section: Evaluation Methodologymentioning

confidence: 99%

“…Baseline strategies were selected to be consistent with related works. [13][14][15] The GSS (guided) and CSS (dynamic) loop schedulers are shipped with libGOMP by default # , thus we used them in our Synthetic Kernel Benchmarking and Application Kernel Benchmarking. However, the HSS scheduler is not shipped with libGOMP neither is publicly available.…”

Section: Experimental Designmentioning

confidence: 99%

“…However, since they do not consider any information about the underlying workload of the target parallel loop, scheduling strategies built upon them end up being inherently suboptimal . Therefore, to specifically address this limitation, workload‐aware strategies were introduced . In a nutshell, this latter class of strategies relies on some knowledge about the workload to adaptively partition the iteration space in chunks and schedule them, thereby further amortizing load imbalance and improving performance.…”

Section: Introductionmentioning

confidence: 99%

“…10,11 Therefore, to specifically address this limitation, workload-aware strategies were introduced. [12][13][14][15] In a nutshell, this latter class of strategies relies on some knowledge about the workload to adaptively partition the iteration space in chunks and schedule them, thereby further amortizing load imbalance and improving performance.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A comprehensive performance evaluation of the BinLPT workload‐aware loop scheduler

Penna

Gomes

Castro

et al. 2019

Concurrency and Computation

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Summary Workload‐aware loop schedulers were introduced to deliver better performance than classical loop scheduling strategies. However, they presented limitations such as inflexible built‐in workload estimators and suboptimal chunk scheduling. Targeting these challenges, we proposed previously a workload‐aware scheduling strategy called BinLPT, which relies on three features: (i) user‐supplied estimations of the workload of the loop; (ii) a greedy heuristic that adaptively partitions the iteration space in several chunks; and (iii) a scheduling scheme based on the Longest Processing Time (LPT) rule and on‐demand technique. In this paper, we present two new contributions to the state‐of‐the‐art. First, we introduce a multiloop support feature to BinLPT, which enables the reuse of estimations across loops. Based on this feature, we integrated BinLPT into a real‐world elastodynamics application, and we evaluated it running on a supercomputer. Second, we present an evaluation of BinLPT using simulations as well as synthetic and application kernels. We carried out this analysis on a large‐scale NUMA machine under a variety of workloads. Our results revealed that BinLPT is better at balancing the workloads of the loop iterations and this behavior improves as the algorithmic complexity of the loop increases. Overall, BinLPT delivers up to 37.15% and 9.11% better performance than well‐known loop scheduling strategies, for the application kernels and the elastodynamics simulation, respectively.

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Section: Evaluation Methodologymentioning

confidence: 99%

Section: Evaluation Methodologymentioning

confidence: 99%

Section: Experimental Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A comprehensive performance evaluation of the BinLPT workload‐aware loop scheduler

Penna

Gomes

Castro

et al. 2019

Concurrency and Computation

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An adaptive self‐scheduling loop scheduler

Booth

Lane

2021

Concurrency and Computation

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Many shared-memory parallel irregular applications, such as sparse linear algebra and graph algorithms, depend on efficient loop scheduling (LS) in a fork-join manner despite that the work per loop iteration can greatly vary depending on the application and the input. Because of the importance of LS, many different methods (e.g., workload-aware self-scheduling) and parameters (e.g., chunk size) have been explored to achieve reasonable performance, and many of these methods require expert prior knowledge about the application and input before runtime. This work proposes a new LS method that requires little to no expert knowledge to achieve speedups close to those of tuned LS methods by self-managing chunk size based on a heuristic of throughput and using work-stealing to recover from workload imbalances. This method, named iCh, is implemented into libgomp for testing. It is evaluated against OpenMP's guided, dynamic, and taskloop methods and is evaluated against BinLPT and generic work-stealing on an array of applications that includes: a synthetic benchmark, breadth-first search, K-Means, the molecular dynamics code LavaMD, and sparse matrix-vector multiplication. On a 28 thread Intel system, iCh is the only method to always be one of the top three LS methods. On average across all applications, iCh is within 5.4% of the best method and is even able to outperform other LS methods for breadth-first search and K-Means.

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OpenMP Loop Scheduling Revisited: Making a Case for More Schedules

Ciorba

Iwainsky

Buder

2018

Evolving OpenMP for Evolving Architectures

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In light of continued advances in loop scheduling, this work revisits the OpenMP loop scheduling by outlining the current state of the art in loop scheduling and presenting evidence that the existing OpenMP schedules are insufficient for all combinations of applications, systems, and their characteristics. A review of the state of the art shows that due to the specifics of the parallel applications, the variety of computing platforms, and the numerous performance degradation factors, no single loop scheduling technique can be a 'one-fits-all' solution to effectively optimize the performance of all parallel applications in all situations. The impact of irregularity in computational workloads and hardware systems, including operating system noise, on the performance of parallel applications results in performance loss and has often been neglected in loop scheduling research, in particular the context of OpenMP schedules. Existing dynamic loop self-scheduling techniques, such as trapezoid self-scheduling, factoring and weighted factoring, offer an unexplored potential to alleviate this degradation in OpenMP due to the fact that they explicitly target the minimization of load imbalance and scheduling overhead. Through theoretical and experimental evaluation, this work shows that these loop self-scheduling methods provide a benefit in the context of OpenMP. In conclusion, OpenMP must include more schedules to offer a broader performance coverage of applications executing on an increasing variety of heterogeneous shared memory computing platforms.

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Knowledge-Based Adaptive Self-Scheduling

Cited by 8 publications

References 13 publications

A comprehensive performance evaluation of the BinLPT workload‐aware loop scheduler

A comprehensive performance evaluation of the BinLPT workload‐aware loop scheduler

An adaptive self‐scheduling loop scheduler

OpenMP Loop Scheduling Revisited: Making a Case for More Schedules

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