Large Data Visualization on Distributed Memory Mulit-GPU Clusters
Data sets of immense size are regularly generated on large scale computing resources. Even among more traditional methods for acquisition of volume data, such as MRI and CT scanners, data which is too large to be effectively visualization on standard workstations is now commonplace. One solution to this problem is to employ a 'visualization cluster,' a small to medium scale cluster dedicated to performing visualization and analysis of massive data sets generated on larger scale supercomputers. These clusters are designed to fit a different need than traditional supercomputers, and therefore their design mandates different hardware choices, such as increased memory, and more recently, graphics processing units (GPUs). While there has been much previous work on distributed memory visualization as well as GPU visualization, there is a relative dearth of algorithms which effectively use GPUs at a large scale in a distributed memory environment. In this work, we study a common visualization technique in a GPU-accelerated, distributed memory setting, and present performance characteristics when scaling to extremely large data sets.
- Research Organization:
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- Computational Research Division
- DOE Contract Number:
- DE-AC02-05CH11231
- OSTI ID:
- 1016569
- Report Number(s):
- LBNL-4571E; TRN: US201112%%360
- Resource Relation:
- Conference: High Performance Graphics (2010)
- Country of Publication:
- United States
- Language:
- English
Similar Records
Distributed shared memory multiprocessor architecture MEMSY for high performance parallel computations
GPU-based Image Compression for Efficient Compositing in Distributed Rendering Applications