Towards scalable parellelism in Monte Carlo particle transport codes using remote memory access
- Los Alamos National Laboratory
- MIT
One forthcoming challenge in the area of high-performance computing is having the ability to run large-scale problems while coping with less memory per compute node. In this work, they investigate a novel data decomposition method that would allow Monte Carlo transport calculations to be performed on systems with limited memory per compute node. In this method, each compute node remotely retrieves a small set of geometry and cross-section data as needed and remotely accumulates local tallies when crossing the boundary of the local spatial domain. initial results demonstrate that while the method does allow large problems to be run in a memory-limited environment, achieving scalability may be difficult due to inefficiencies in the current implementation of RMA operations.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC52-06NA25396
- OSTI ID:
- 1027492
- Report Number(s):
- LA-UR-10-05488; LA-UR-10-5488; TRN: US201121%%391
- Resource Relation:
- Conference: SNA+MC - 2010 ; October 17, 2010 ; Tokyo, Japan
- Country of Publication:
- United States
- Language:
- English
Similar Records
High Performance Remote Memory Access Communication: The ARMCI Approach
Evaluating OpenSHMEM Explicit Remote Memory Access Operations and Merged Requests