A Virtual Sample Generation Approach for Speculative Multithreading Using Feature Sets and Abstract Syntax Trees

Liu, Bin; Zhao, Yang; Li, Meirong; Liu, Yanzhao; Bao, Feng

doi:10.1109/pdcat.2012.33

Cited by 9 publications

(3 citation statements)

References 17 publications

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“…and Prophet [26][27][28] Framework GSpecPal [3] Algorithms [29,30] Related products [31][32][33][34][35] of programs while our approach based on machine learning proposes a program-aware partitioning scheme according to characteristics of the target program.…”

Section: Itemmentioning

confidence: 99%

IDaTPA:Importance Degree Based Thread Partitioning Approach in Thread Level Speculation

Li,

Zhang,

Wang

et al. 2023

Preprint

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As an auto-parallelization technique with the level of thread on multi-core,Thread-Level Speculation (TLS) which is also called Speculative Multithreading(SpMT), partitions programs into multiple threads and speculatively executesthem under conditions of ambiguous data and control dependence. Thread par-titioning approach plays a key role to the performance enhancement in TLS.The existing heuristic rules-based approach (HR-based approach) which is anone-size-fits-all strategy, can not guarantee to achieve the optimal thread parti-tioning. In this paper, an importance degree based thread partitioning approach(IDaTPA) is proposed to realize the partition of irregular programs into mul-tithreads. IDaTPA implements biasing partitioning for every procedure with amachine learning method. It mainly includes: constructing sample set, expres-sion of knowledge, calculation of similarity, prediction model and the partitionof the irregular programs is performed by the prediction model. Using IDaTPA,the subprocedures in unseen irregular programs can obtain their optimal parti-tion. On a generic SpMT processor (called Prophet) to perform the performanceevaluation for multithreaded programs, the IDaTPA is evaluated and averagelydelivers a speedup of 1.80 upon a 4-core processor. Furthermore, in order toobtain the portability evaluation of IDaTPA, we port IDaTPA to 8-core processorand obtain a speedup of 2.82 on average. Experiment results show that IDaTPAobtains a significant speedup increasement and Olden benchmarks respectively deliver a 5.75% performance improvement on 4-core and a 6.32% performanceimprovement on 8-core, and SPEC2020 benchmarks obtain a 38.20% performanceimprovement than the conventional HR-based approach.

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Section: Itemmentioning

confidence: 99%

IDaTPA:Importance Degree Based Thread Partitioning Approach in Thread Level Speculation

Li,

Zhang,

Wang

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…To overcome the problem of insufficient training programs, we propose a program generation approach to generate a lot of programs for compensating the existing Olden benchmarks [27]. Features influencing program's speedup are studied and used to characterize an irregular program.…”

Section: B Program Generation and Descriptionmentioning

confidence: 99%

“…In [27], we generated a lot of programs to solve the insufficiency of training data. In Section III, based on the existing thread partitioning algorithm, we find the optimal partitioning structure for each procedure by the repeated estimation on the Prophet simulator, these samples can provide sufficient training samples for prediction model.…”

Section: A Basic Ideamentioning

confidence: 99%

A Novel Thread Partitioning Approach Based on Machine Learning for Speculative Multithreading

Liu

Zhao

Zhong

et al. 2013

2013 IEEE 10th International Conference on High Performance Computing and Communications &Amp; 2013 IEEE International Conferen

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Speculative multithreading (SpMT) is a thread-level automatic parallelization technique to accelerate sequential programs on multi-core. The existing heuristic-based approaches are only suitable for one kind of programs and cannot guarantee to get the optimal solution of thread partitioning. In this paper, we propose a novel thread partitioning approach based on machine learning to partition irregular programs into multithreads. It mainly includes: generating sufficient training samples, building and applying the prediction model to partition the irregular programs. By using the thread partition approach, an unseen irregular program can obtain a stable, much higher speedup than the heuristic-based approaches. On the Prophet, which is a SpMT processor to evaluate the performance of multithreaded programs, the novel thread partitioning approach is evaluated and reaches an average speedup of 1.80 on 4-core processor. Experiments show that our proposed approach can obtain a significant increase in speedup and Olden benchmarks deliver a better performance improvement of 5.41% than the traditional heuristic-based approach.

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GbA: A graph‐based thread partition approach in speculative multithreading

Zhao

2017

Concurrency and Computation

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SummarySpeculative multithreading is an effective technique to automatically parallelize sequential programs. Conventional thread partition approaches primarily include heuristic rule–based (HR‐based) and machine learning–based. Heuristic rule–based approaches are effective to parallelize one type of programs and can seldom obtain the respective optimal partitions for different programs, and existing machine learning–based approaches usually use vector‐based characterization to represent a program, but easily ignore control information among basic blocks and partitions along other paths except the critical path. This paper proposes a novel graph‐based thread partition approach to overcome these 2 bottlenecks. Our approach characterizes programs with graphs, integrating feature and control informations, extracting good partition scheme successfully, and also applies a machine‐learning algorithm to predict partition for unseen programs. Prophet, which consists of an automatic parallelization compiler and a multicore simulator, evaluates the performance of multithreaded programs. Experiment results reveal that our approach delivers a maximum performance improvement of about 55.49% on an 8 core than HR‐based approach and a maximum 97.67% performance improvement over HR‐based partition for SPEC2000 benchmarks. This result suggests that graph‐based thread partition approach is effective for thread partition in speculative multithreading.

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A Virtual Sample Generation Approach for Speculative Multithreading Using Feature Sets and Abstract Syntax Trees

Cited by 9 publications

References 17 publications

IDaTPA:Importance Degree Based Thread Partitioning Approach in Thread Level Speculation

IDaTPA:Importance Degree Based Thread Partitioning Approach in Thread Level Speculation

A Novel Thread Partitioning Approach Based on Machine Learning for Speculative Multithreading

GbA: A graph‐based thread partition approach in speculative multithreading

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