HPC Cluster About: Difference between revisions
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ELSA (Electronic Laboratory for Science and Analysis) is TCNJ's heterogeneous High Performance Computing (HPC) cluster named after the famous [[wikipedia:Born_Free|“Born Free”]] lioness and our mascot. | ELSA (Electronic Laboratory for Science and Analysis) is TCNJ's heterogeneous High Performance Computing (HPC) cluster named after the famous [[wikipedia:Born_Free|“Born Free”]] lioness and our mascot. | ||
ELSA’s compute resources are currently comprised of over | ELSA’s compute resources are currently comprised of over 62 nodes, providing 2,916 central processing unit (CPU) cores, 25TB of RAM, 99 graphics processing units (GPUs), and approximately 6.3 petabytes of network-based storage. The cluster is housed in this dedicated Scientific Computing Center in TCNJ’s STEM Building. | ||
== Use Cases == | == Use Cases == | ||
Generally, the HPC cluster is used to run large simulations and process big data that is impractical to do on a desktop workstation or laptop computer. The HPC cluster provides large amounts of memory (RAM), CPU cores (processors), GPUs (specialized computational devices) and permanent storage (disk). | |||
Example areas of faculty and undergraduate research and teaching using the ELSA cluster include: | Example areas of faculty and undergraduate research and teaching using the ELSA cluster include: | ||
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The computing hardware comprising the ELSA cluster was provided in part by the State of New Jersey’s Building our Future bond, NJ ELF Bond, and grants from the National Science Foundation ([https://www.nsf.gov/awardsearch/showAward?AWD_ID=1826915&HistoricalAwards=false OAC-1826915] and [https://www.nsf.gov/awardsearch/showAward?AWD_ID=2320244 OAC-2320244]). The College of New Jersey and the School of Science gratefully acknowledge their support and partnership. | The computing hardware comprising the ELSA cluster was provided in part by the State of New Jersey’s Building our Future bond, NJ ELF Bond, and grants from the National Science Foundation ([https://www.nsf.gov/awardsearch/showAward?AWD_ID=1826915&HistoricalAwards=false OAC-1826915] and [https://www.nsf.gov/awardsearch/showAward?AWD_ID=2320244 OAC-2320244]). The College of New Jersey and the School of Science gratefully acknowledge their support and partnership. | ||
Latest revision as of 16:34, 6 September 2024
ELSA High Performance Computing Cluster
ELSA (Electronic Laboratory for Science and Analysis) is TCNJ's heterogeneous High Performance Computing (HPC) cluster named after the famous “Born Free” lioness and our mascot.
ELSA’s compute resources are currently comprised of over 62 nodes, providing 2,916 central processing unit (CPU) cores, 25TB of RAM, 99 graphics processing units (GPUs), and approximately 6.3 petabytes of network-based storage. The cluster is housed in this dedicated Scientific Computing Center in TCNJ’s STEM Building.
Use Cases
Generally, the HPC cluster is used to run large simulations and process big data that is impractical to do on a desktop workstation or laptop computer. The HPC cluster provides large amounts of memory (RAM), CPU cores (processors), GPUs (specialized computational devices) and permanent storage (disk).
Example areas of faculty and undergraduate research and teaching using the ELSA cluster include:
- Applied Mathematics
- Astrophysics
- Big Data
- Biochemistry
- Biophysics
- Biostatistics
- Catalytic Chemistry
- Encryption
- Evolution & Phylogeny
- Fluid Dynamics
- Genetics & Bioinformatics
- Machine Learning
- Mathematical Biology
- Natural Language Processing
- Synthetic Biology
Contact and Tour Information
To learn more about the ELSA cluster including how to gain access to this resource as well as scheduling class tours of the cluster, please contact Shawn Sivy in Physics 108A, ssivy@tcnj.edu, or 609-771-3475.
Acknowledgements
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The computing hardware comprising the ELSA cluster was provided in part by the State of New Jersey’s Building our Future bond, NJ ELF Bond, and grants from the National Science Foundation (OAC-1826915 and OAC-2320244). The College of New Jersey and the School of Science gratefully acknowledge their support and partnership.