A Parallel Implementation for Cellular Potts Model with Software Transactional Memory

Tomeu, Antonio J.; Gámez, A.; Salguero, Alberto

doi:10.1007/978-3-030-23873-5_7

Cited by 3 publications

(4 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Calculate the set of loop layers (s i ) that cover the direction vector of d i (5) end for (6) for q � 1 ⟶ N p do (7) for i � 1 ⟶ k do (8) Randomly choose a loop layer (l j ) from s i and add it to the α q set (9) end for (10) while α q ≠ ∅ do / * Delete the redundant loop layer * / (11) α q � α q − l j (l j ∈ α q ) / * randomly remove the l j loop layer from the α q set * / (12…”

Section: Basicmentioning

confidence: 99%

“…Calculate the set of direction vectors (D j ) covered by l j (5) L q � L q ∪ D j (6) end for (7) c q � D − L q / * Calculate the set of direction vectors (c q ) not covered by Chromosome…”

Section: Basicmentioning

confidence: 99%

“…However, the dependency analysis capability of the underlying hardware is limited and lacks flexibility, which makes it difficult for the TLS technique to analyze the complex dependencies of the multilevel nested loops and thus is impossible to provide effective parallelism [1][2][3][4][5][6]. e hardware TM technique has limitations on the transactional size (transactional buffer) and duration (operating system events and disruptions can abort the transaction); therefore, when applied to the thread-level parallelism of nested loops, it often leads to overflow of the underlying hardware resources due to excessive granularity of the transaction itself, which ultimately manifests poor parallel performance [7][8][9][10][11][12]. Another common approach is to use the traditional software-based static compilation techniques, in which the dependency analysis test module in the compiler is used to test whether there are dependencies between the loop iterations in a layerwise manner.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Loop Selection for Multilevel Nested Loops Using a Genetic Algorithm

Nie

Zhou

Huang

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Loop selection for multilevel nested loops is a very difficult problem, for which solutions through the underlying hardware-based loop selection techniques and the traditional software-based static compilation techniques are ineffective. A genetic algorithm- (GA-) based method is proposed in this study to solve this problem. First, the formal specification and mathematical model of the loop selection problem are presented; then, the overall framework for the GA to solve the problem is designed based on the mathematical model; finally, we provide the chromosome representation method and fitness function calculation method, the initial population generation algorithm and chromosome improvement methods, the specific implementation methods of genetic operators (crossover, mutation, and selection), the offspring population generation method, and the GA stopping criterion during the GA operation process. Experimental tests with the SPEC2006 and NPB3.3.1 standard test sets were performed on the Sunway TaihuLight supercomputer. The test results indicated that the proposed method can achieve a speedup improvement that is superior to that by the current mainstream methods, which confirm the effectiveness of the proposed method. Solving the loop selection problem of multilevel nested loops is of great practical significance for exploiting the parallelism of general scientific computing programs and for giving full play to the performance of multicore processors.

show abstract

Section: Basicmentioning

confidence: 99%

Section: Basicmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Loop Selection for Multilevel Nested Loops Using a Genetic Algorithm

Nie

Zhou

Huang

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…The model consists of three layers. The lowest layer is a microscale 3D cellular Potts model (CPM) layer which models the cells on a grid [14,[29][30][31]. The CPM is based on a Hamiltonian with local interactions.…”

Section: Model Descriptionmentioning

confidence: 99%

Emerging Tumor Development by Simulating Single-cell Events

Rosenbauer

Berghoff

Schug

2020

Preprint

View full text Add to dashboard Cite

Despite decades of substantial research, cancer remains a ubiquitous scourge in the industrialized world. Effective treatments require a thorough understanding of macroscopic cancerous tumor growth out of individual cells. Clinical imaging methods, however, only detect late-stage macroscopic tumors, while many quantitative experiments focus on small clusters of cancerous cells in microscopic detail but struggle to grow full tumors in-vitro. Here, we introduce the critical scale-bridging link between both these scopes. We are able to simulate the growth of mm-sized tumors composed of 1.5 million μm-resolved individual cells by employing highly parallelized code on a supercomputer. We observe the competition for resources and space, which can lead to hypoxic or necrotic tissue regions. Cellular mutations and tumor stem cells can lead to tissue heterogeneity and change tumor properties. We probe the effects of different chemotherapy and radiotherapy treatments and observe selective pressure. This improved theoretical understanding of cancer growth as emerging behavior from single-cells opens new avenues for various scientific fields, ranging from developing better early-stage cancer detection devices to testing treatment regimes in-silico for personalized medicine.

show abstract

Power Consumption Comparison of GPU Linear Solvers for Cellular Potts Model Simulations

De Luca,

Galletti,

Marcellino

2024

Applied Sciences

View full text Add to dashboard Cite

Power consumption is a significant challenge in the sustainability of computational science. The growing energy demands of increasingly complex simulations and algorithms lead to substantial resource use, which conflicts with global sustainability goals. This paper investigates the energy efficiency of different parallel implementations of a Cellular Potts model, which models cellular behavior through Hamiltonian energy minimization techniques, leveraging modern GPU architectures. By evaluating alternative solvers, it demonstrates that specific methods can significantly enhance computational efficiency and reduce energy use compared to traditional approaches. The results confirm notable improvements in execution time and energy consumption. In particular, the experiments show a reduction in terms of power of up to 53%, providing a pathway towards more sustainable high-performance computing practices for complex biological simulations.

show abstract

A Parallel Implementation for Cellular Potts Model with Software Transactional Memory

Cited by 3 publications

References 15 publications

Loop Selection for Multilevel Nested Loops Using a Genetic Algorithm

Loop Selection for Multilevel Nested Loops Using a Genetic Algorithm

Emerging Tumor Development by Simulating Single-cell Events

Power Consumption Comparison of GPU Linear Solvers for Cellular Potts Model Simulations

Contact Info

Product

Resources

About