A High-Performance Heterogeneous Critical Path Analysis Framework

Zamani, Yasin; Huang, Tsung‐Wei

doi:10.1109/hpec49654.2021.9622872

Cited by 8 publications

(2 citation statements)

References 14 publications

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“…For heterogeneous parallelism, balancing the workload between the CPU and GPU with different arithmetic capabilities for efficient computing is critical [69,70]. There are three main methods to evaluate the best workload balancing ratio α: the enumeration method, the formula method, and the pre-run method [71,72].…”

Section: Dynamic Workload Balancingmentioning

confidence: 99%

A Hybrid Parallel Strategy for Isogeometric Topology Optimization via CPU/GPU Heterogeneous Computing

Xia,

Gao,

et al. 2024

CMES

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This paper aims to solve large-scale and complex isogeometric topology optimization problems that consume significant computational resources. A novel isogeometric topology optimization method with a hybrid parallel strategy of CPU/GPU is proposed, while the hybrid parallel strategies for stiffness matrix assembly, equation solving, sensitivity analysis, and design variable update are discussed in detail. To ensure the high efficiency of CPU/GPU computing, a workload balancing strategy is presented for optimally distributing the workload between CPU and GPU. To illustrate the advantages of the proposed method, three benchmark examples are tested to verify the hybrid parallel strategy in this paper. The results show that the efficiency of the hybrid method is faster than serial CPU and parallel GPU, while the speedups can be up to two orders of magnitude.

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Section: Dynamic Workload Balancingmentioning

confidence: 99%

A Hybrid Parallel Strategy for Isogeometric Topology Optimization via CPU/GPU Heterogeneous Computing

Xia,

Gao,

et al. 2024

CMES

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“…We use the opensource tool, OpenTimer, to generate 1024 different timing reports for a large circuit, netcard, of 1.5M gates [23], [24]. The correlation layer implements a CPU-based algorithm to extract graph information (critical paths [27], [28], CPPR [29], [30], [31]) and a GPU-based algorithm to perform logistic regression with gradient descent. Part of CPU and GPU tasks are dependent to each other.…”

Section: A Vlsi Timing Analysismentioning

confidence: 99%

Concurrent CPU-GPU Task Programming using Modern C++

Huang¹,

Lin²

2022

Preprint

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In this paper, we introduce Heteroflow, a new C++ library to help developers quickly write parallel CPU-GPU programs using task dependency graphs. Heteroflow leverages the power of modern C++ and task-based approaches to enable efficient implementations of heterogeneous decomposition strategies. Our new CPU-GPU programming model allows users to express a problem in a way that adapts to effective separation of concerns and expertise encapsulation. Compared with existing libraries, Heteroflow is more cost-efficient in performance scaling, programming productivity, and solution generality. We have evaluated Heteroflow on two real applications in VLSI design automation and demonstrated the performance scalability across different CPU-GPU numbers and problem sizes. At a particular example of VLSI timing analysis with million-scale tasking, Heteroflow achieved 7.7× runtime speed-up (99 vs 13 minutes) over a baseline on a machine of 40 CPU cores and 4 GPUs.

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