DtCraft: A distributed execution engine for compute-intensive applications

Huang, Tsung‐Wei; Lin, Chun-Xun; Wong, D.F.

doi:10.1109/iccad.2017.8203853

Cited by 12 publications

(2 citation statements)

References 7 publications

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“…Our design philosophy is to provide users a centralized viewpoint of the entire distributed system. The distributed timer consists of a master daemon that manages agent daemons running on each cluster node, and timer instances that are coordinated by these agents [11] [3] [4]. A timer instance is a local timer that runs on a single design partition and persists in memory through an eventdriven loop.…”

Section: Proposed Distributed Timermentioning

confidence: 99%

Distributed Timing Analysis at Scale

Huang¹,

Lin²,

Wong³

2019

Proceedings of the 56th Annual Design Automation Conference 2019

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As the design complexities continue to grow, the need to efficiently analyze circuit timing with billions of transistors is quickly becoming the major bottleneck to the overall chip design flow. In this work we introduce a distributed timer that (1) has scalable performance, (2) can be seamless integrable to existing EDA applications, (3) enables transparent resource management, (4) has robust faulttolerant control. We evaluate the distributed timer using a set of large industry benchmarks on a cluster with 24 nodes. The results show that the proposed timer achieves full accuracy over all designs with high performance and good scalability.

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Section: Proposed Distributed Timermentioning

confidence: 99%

Distributed Timing Analysis at Scale

Huang¹,

Lin²,

Wong³

2019

Proceedings of the 56th Annual Design Automation Conference 2019

Self Cite

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“…Traditionally, based on the different utilization of resources, jobs can be generally classified into three categories: I/O intensive, memory intensive, and compute-intensive [20]. By far, a majority of previous studies treat CPU as the main resource consumed by compute-intensive jobs [21][22][23]. But, as the emergence of deep learning, the job type can be specifically divided into CPU-intensive and GPU-intensive.…”

Section: Introductionmentioning

confidence: 99%

An Improved ANN-based Power Prediction Algorithm for Multi-type Jobs on Heterogeneous Computing Server

Chao

2022

Preprint

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This paper exploits the advantage of Artificial Neural Network (ANN) to design and implement an improved algorithm of power prediction on heterogeneous computing server with various types of jobs.First, the impact of multi-type of jobs has been taken into account. We divide the jobs into four categories: CPU-intensive, memory-intensive, I/O-intensive and GPU-intensive. We collect the data trace from these jobs by a set of benchmarks.Then, based on the collected data trace, power prediction algorithm has been established, so that developing an improved ANN-based power prediction algorithm with CNN-BiLSTM neural network and attention mechanism.Last, the experiment has been conducted via real-world heterogeneous computing based platform.Compared with the traditional neural network based prediction algorithms and non-neural network based prediction algorithms, our proposed prediction algorithm has better performance in prediction accuracy on power consumption.

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CBLA_PM: an improved ann-based power consumption prediction algorithm for multi-type jobs on heterogeneous computing server

Chao

2023

Cluster Comput

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DtCraft: A distributed execution engine for compute-intensive applications

Cited by 12 publications

References 7 publications

Distributed Timing Analysis at Scale

Distributed Timing Analysis at Scale

An Improved ANN-based Power Prediction Algorithm for Multi-type Jobs on Heterogeneous Computing Server

CBLA_PM: an improved ann-based power consumption prediction algorithm for multi-type jobs on heterogeneous computing server

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