Review



cuda-sim implementations of the gillespie algorithm  (MathWorks Inc)


Bioz Verified Symbol MathWorks Inc is a verified supplier  
  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
    • 90
      Buy from Supplier

    Structured Review

    MathWorks Inc cuda-sim implementations of the gillespie algorithm
    Timing comparisons. ( A – C ) Time taken to simulate a given number of realisations for a single core of an Intel Core i7-975 Extreme Edition Processor 3.33 GHz (solid line) and one Tesla C2050 GPU (dashed line) for (A) the <t>LSODA</t> (B) the Euler–Maruyama and (C) <t>the</t> <t>Gillespie</t> algorithm, respectively. The relative speed-ups for given numbers of simulations are indicated next to the GPU simulation results. ( D ) Summary of the relative speed-up of the three different algorithms.
    Cuda Sim Implementations Of The Gillespie Algorithm, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/cuda-sim implementations of the gillespie algorithm/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    cuda-sim implementations of the gillespie algorithm - by Bioz Stars, 2026-04
    90/100 stars

    Images

    1) Product Images from "GPU accelerated biochemical network simulation"

    Article Title: GPU accelerated biochemical network simulation

    Journal: Bioinformatics

    doi: 10.1093/bioinformatics/btr015

    Timing comparisons. ( A – C ) Time taken to simulate a given number of realisations for a single core of an Intel Core i7-975 Extreme Edition Processor 3.33 GHz (solid line) and one Tesla C2050 GPU (dashed line) for (A) the LSODA (B) the Euler–Maruyama and (C) the Gillespie algorithm, respectively. The relative speed-ups for given numbers of simulations are indicated next to the GPU simulation results. ( D ) Summary of the relative speed-up of the three different algorithms.
    Figure Legend Snippet: Timing comparisons. ( A – C ) Time taken to simulate a given number of realisations for a single core of an Intel Core i7-975 Extreme Edition Processor 3.33 GHz (solid line) and one Tesla C2050 GPU (dashed line) for (A) the LSODA (B) the Euler–Maruyama and (C) the Gillespie algorithm, respectively. The relative speed-ups for given numbers of simulations are indicated next to the GPU simulation results. ( D ) Summary of the relative speed-up of the three different algorithms.

    Techniques Used:



    Similar Products

    cuda-sim implementations of the gillespie algorithm  (MathWorks Inc)
    90
    MathWorks Inc cuda-sim implementations of the gillespie algorithm
    Timing comparisons. ( A – C ) Time taken to simulate a given number of realisations for a single core of an Intel Core i7-975 Extreme Edition Processor 3.33 GHz (solid line) and one Tesla C2050 GPU (dashed line) for (A) the <t>LSODA</t> (B) the Euler–Maruyama and (C) <t>the</t> <t>Gillespie</t> algorithm, respectively. The relative speed-ups for given numbers of simulations are indicated next to the GPU simulation results. ( D ) Summary of the relative speed-up of the three different algorithms.
    Cuda Sim Implementations Of The Gillespie Algorithm, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/cuda-sim implementations of the gillespie algorithm/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    cuda-sim implementations of the gillespie algorithm - by Bioz Stars, 2026-04
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    Timing comparisons. ( A – C ) Time taken to simulate a given number of realisations for a single core of an Intel Core i7-975 Extreme Edition Processor 3.33 GHz (solid line) and one Tesla C2050 GPU (dashed line) for (A) the LSODA (B) the Euler–Maruyama and (C) the Gillespie algorithm, respectively. The relative speed-ups for given numbers of simulations are indicated next to the GPU simulation results. ( D ) Summary of the relative speed-up of the three different algorithms.

    Journal: Bioinformatics

    Article Title: GPU accelerated biochemical network simulation

    doi: 10.1093/bioinformatics/btr015

    Figure Lengend Snippet: Timing comparisons. ( A – C ) Time taken to simulate a given number of realisations for a single core of an Intel Core i7-975 Extreme Edition Processor 3.33 GHz (solid line) and one Tesla C2050 GPU (dashed line) for (A) the LSODA (B) the Euler–Maruyama and (C) the Gillespie algorithm, respectively. The relative speed-ups for given numbers of simulations are indicated next to the GPU simulation results. ( D ) Summary of the relative speed-up of the three different algorithms.

    Article Snippet: But since in most applications of these algorithms, either in order to explore the parameter space or to perform inference, at least thousands of simulations will be needed for which the GPU outperforms the CPU even for the rather simple p53-Mdm2 model. We also compared the cuda-sim implementations of the LSODA and Gillespie algorithms with implementations in the Matlab package SBTOOLBOX2 ( ) and our Euler–Maruyama implementation with the native sde function within Matlab.

    Techniques: