Towards accelerating irregular EDA applications with GPUs

Qian, Hao; Deng, Yangdong; Wang, Bo; Mu, Shuai

doi:10.1016/j.vlsi.2011.05.004

Cited by 7 publications

(2 citation statements)

References 26 publications

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“…A final NR Algorithm 1 is composed of the following 4 steps repeated in the NR loop until a convergence is reached. The solutions of ( 5), ( 6), (7) and the result of the matrix multiplications indicated in (4) are obtained with the help of the sparse matrix techniques. Moreover, a two-level bypassing process may be easily implemented: bypassing of nonlinear elements in step 1 of Algorithm 1 during the calculation of stamps for nonlinear elements and bypassing of blocks in step 4 during the calculation of their stamps indicated in (4).…”

Section: The Parallelizable Bbd-based Algorithmmentioning

confidence: 99%

DC Large-Scale Simulation of Nonlinear Circuits on Parallel Processors

Udave

Ogrodzki²,

Anda

2012

International Journal of Electronics and Telecommunications

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Newton-Raphson DC analysis of large-scale nonlinear circuits may be an extremely time consuming process even if sparse matrix techniques and bypassing of nonlinear models calculation are used. A slight decrease in the time required for this task may be enabled on multi-core, multithread computers if the calculation of the mathematical models for the nonlinear elements as well as the stamp management of the sparse matrix entries is managed through concurrent processes. In this paper it is shown how the numerical complexity of this problem (and thus its solution time) can be further reduced via the circuit decomposition and parallel solution of blocks taking as a departure point the Bordered-Block Diagonal (BBD) matrix structure. This BBD-parallel approach may give a considerable profit though it is strongly dependent on the system topology. This paper presents a theoretical foundation of the algorithm, its implementation, and numerical complexity analysis in virtue of practical measurements of matrix operations.

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Section: The Parallelizable Bbd-based Algorithmmentioning

confidence: 99%

DC Large-Scale Simulation of Nonlinear Circuits on Parallel Processors

Udave

Ogrodzki²,

Anda

2012

International Journal of Electronics and Telecommunications

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“…These commodity chips have enhanced their programmability to perform more general computational tasks than the graphics processing they were originally designed for. Examples include scientific computing [3] , image processing [4] , computational biology [5] , electronic design automation (EDA) [6] and data science [7] , etc. The computer video game market have driven the evolution of GPUs to yield relatively cheaper price per unit and very rapid iteration of hardware architectures.…”

Section: Introductionmentioning

confidence: 99%

Computing Platforms for Big Biological Data Analytics: Perspectives and Challenges

Yin

Lan

Tan

et al. 2017

Computational and Structural Biotechnology Journal

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The last decade has witnessed an explosion in the amount of available biological sequence data, due to the rapid progress of high-throughput sequencing projects. However, the biological data amount is becoming so great that traditional data analysis platforms and methods can no longer meet the need to rapidly perform data analysis tasks in life sciences. As a result, both biologists and computer scientists are facing the challenge of gaining a profound insight into the deepest biological functions from big biological data. This in turn requires massive computational resources. Therefore, high performance computing (HPC) platforms are highly needed as well as efficient and scalable algorithms that can take advantage of these platforms. In this paper, we survey the state-of-the-art HPC platforms for big biological data analytics. We first list the characteristics of big biological data and popular computing platforms. Then we provide a taxonomy of different biological data analysis applications and a survey of the way they have been mapped onto various computing platforms. After that, we present a case study to compare the efficiency of different computing platforms for handling the classical biological sequence alignment problem. At last we discuss the open issues in big biological data analytics.

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GPU-Friendly FRW Algorithms for Electrostatic Computation Problems

Mascagni

2022

Monte Carlo Methods for Partial Differential Equations With Applications to Electronic Design Automation

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Towards accelerating irregular EDA applications with GPUs

Cited by 7 publications

References 26 publications

DC Large-Scale Simulation of Nonlinear Circuits on Parallel Processors

DC Large-Scale Simulation of Nonlinear Circuits on Parallel Processors

Computing Platforms for Big Biological Data Analytics: Perspectives and Challenges

GPU-Friendly FRW Algorithms for Electrostatic Computation Problems

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