Effect of Parallel Workload on Dynamic Voltage Frequency Scaling for Dark Silicon Ameliorating

Harini, S.; Ravikumar, Aswathy

doi:10.1109/icosec49089.2020.9215262

Cited by 8 publications

(3 citation statements)

References 7 publications

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“…Utilizing high-performance hardware isn't the only option; one potential alternative is to parallelize and provide DNN training operations on many nodes instead. Under these circumstances, the amount of work that each node contributes to the calculation is minimal at best [ [3] , [4] , [5] , [6] , [7] ]. Despite this, the communication delay is a critical obstacle in distributed training due to the frequent communication requirements for delivering vast volumes of data across multiple computing nodes.…”

Section: Introductionmentioning

confidence: 99%

DPro-SM – A distributed framework for proactive straggler mitigation using LSTM

Ravikumar,

Sriraman

2024

Heliyon

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Section: Introductionmentioning

confidence: 99%

DPro-SM – A distributed framework for proactive straggler mitigation using LSTM

Ravikumar,

Sriraman

2024

Heliyon

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“…Applications in the real world of natural language processing are its principal use. Several strategies have been offered to speed up the learning process in recent years [3][4][5]. Initially, they focus on investigating the fluctuation of network characteristics, like loss and activation functions.…”

Section: Introductionmentioning

confidence: 99%

Circumventing Stragglers and Staleness in Distributed CNN using LSTM

Ravikumar,

Sriraman,

Lokesh

et al. 2024

EAI Endorsed Trans IoT

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INTRODUCTION: Using neural networks for these inherently distributed applications is challenging and time-consuming. There is a crucial need for a framework that supports a distributed deep neural network to yield accurate results at an accelerated time. METHODS: In the proposed framework, any experienced novice user can utilize and execute the neural network models in a distributed manner with the automated hyperparameter tuning feature. In addition, the proposed framework is provided in AWS Sage maker for scaling the distribution and achieving exascale FLOPS. We benchmarked the framework performance by applying it to a medical dataset. RESULTS: The maximum performance is achieved with a speedup of 6.59 in 5 nodes. The model encourages expert/ novice neural network users to apply neural network models in the distributed platform and get enhanced results with accelerated training time. There has been a lot of research on how to improve the training time of Convolutional Neural Networks (CNNs) using distributed models, with a particular emphasis on automating the hyperparameter tweaking process. The study shows that training times may be decreased across the board by not just manually tweaking hyperparameters, but also by using L2 regularization, a dropout layer, and ConvLSTM for automatic hyperparameter modification. CONCLUSION: The proposed method improved the training speed for model-parallel setups by 1.4% and increased the speed for parallel data by 2.206%. Data-parallel execution achieved a high accuracy of 93.3825%, whereas model-parallel execution achieved a top accuracy of 89.59%.

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“…Several research on performance of multithreading libraries have been carried out [4]. For example, the paper "A New Parallel Benchmark for Performance Evaluation and Energy Consumption" measures the energy and performance of each application in a single architecture, with number of threads changing [5].…”

Section: Introductionmentioning

confidence: 99%

A study on impacts of hardware changes on performance of different multithreading libraries based on Gem5

2023

J. Phys.: Conf. Ser.

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This paper presents a thorough investigation into the performance of various multithreading libraries on different hardware configurations utilizing gem5. The study compares the performance of commonly-used multithreading libraries, including OpenMP, Pthread, TBB, and Dlib, through several benchmarks representing distinct types of workloads. Key hardware factors that directly affect the performance of each library are identified and analyzed. The research findings provide valuable insights and recommendations for selecting an appropriate multithreading library for a given application and hardware platform. Results indicate that the performance of multithreading libraries is influenced by numerous hardware factors, such as L1 cache size, L1 cache associate, L1 cache replacement policy, number of threads, and memory type. The libraries respond differently to these factors, and the performance variability of each library depends on the nature of the workload. The study offers a detailed analysis and valuable insights into the effects of these hardware factors on each library. This study contributes by presenting a comprehensive comparison of several widely used multithreading libraries across numerous hardware configurations, identifying the key hardware factors that influence the performance of each library, and providing insights and recommendations for selecting an appropriate multithreading library for a given application and hardware platform. Ultimately, the study results can help software developers make informed decisions when selecting a multithreading library and optimizing performance for their applications.

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Effect of Parallel Workload on Dynamic Voltage Frequency Scaling for Dark Silicon Ameliorating

Cited by 8 publications

References 7 publications

DPro-SM – A distributed framework for proactive straggler mitigation using LSTM

DPro-SM – A distributed framework for proactive straggler mitigation using LSTM

Circumventing Stragglers and Staleness in Distributed CNN using LSTM

A study on impacts of hardware changes on performance of different multithreading libraries based on Gem5

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