| Chapter title |
Google-Accelerated Biomolecular Simulations.
|
|---|---|
| Chapter number | 12 |
| Book title |
Biomolecular Simulations
|
| Published in |
Methods in molecular biology, January 2019
|
| DOI | 10.1007/978-1-4939-9608-7_12 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-9607-0, 978-1-4939-9608-7
|
| Authors |
Kohlhoff, Kai J, Kohlhoff, Kai J., Kai J. Kohlhoff |
| Abstract |
Biomolecular simulations rely heavily on the availability of suitable compute infrastructure for data-driven tasks like modeling, sampling, and analysis. These resources are typically available on a per-lab and per-facility basis, or through dedicated national supercomputing centers. In recent years, cloud computing has emerged as an alternative by offering an abundance of on-demand, specialist-maintained resources that enable efficiency and increased turnaround through rapid scaling.Scientific computations that take the shape of parallel workloads using large datasets are commonplace, making them ideal candidates for distributed computing in the cloud. Recent developments have greatly simplified the task for the experimenter to configure the cloud for use and job submission. This chapter will show how to use Google's Cloud Platform for biomolecular simulations by example of the molecular dynamics package GROningen MAchine for Chemical Simulations (GROMACS). The instructions readily transfer to a large variety of other tasks, allowing the reader to use the cloud for their specific purposes.Importantly, by using Docker containers, a popular light-weight virtualization solution, and cloud storage, key issues in scientific research are addressed: reproducibility of results, record keeping, and the possibility for other researchers to obtain copies and directly build upon previous work for further experimentation and hypothesis testing. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 50% |
| United States | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Practitioners (doctors, other healthcare professionals) | 1 | 50% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 12 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 3 | 25% |
| Other | 1 | 8% |
| Professor | 1 | 8% |
| Student > Bachelor | 1 | 8% |
| Student > Master | 1 | 8% |
| Other | 1 | 8% |
| Unknown | 4 | 33% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemical Engineering | 2 | 17% |
| Computer Science | 2 | 17% |
| Nursing and Health Professions | 1 | 8% |
| Biochemistry, Genetics and Molecular Biology | 1 | 8% |
| Neuroscience | 1 | 8% |
| Other | 1 | 8% |
| Unknown | 4 | 33% |