Name
Abstract | ||
|---|---|---|
We propose two hardware mechanisms to decrease energy consumption on massively parallel graphics processors for ray tracing while keeping performance high. First, we use a streaming data model and configure part of the L2 cache into a ray stream memory to enable efficient data processing through ray reordering. This increases the L1 hit rate and reduces off-chip memory accesses substantially. Second, we employ reconfigurable special-purpose pipelines than are constructed dynamically under program control. These pipelines use shared execution units (XUs) that can be configured to support the common compute kernels that are the foundation of the ray tracing algorithm, such as acceleration structure traversal and triangle intersection. This reduces the overhead incurred by memory and register accesses. These two synergistic features yield a ray tracing architecture that significantly reduces both power consumption and off-chip memory traffic when compared to a more traditional cache only approach. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2492045.2492058 | High Performance Graphics |
Keywords | Field | DocType |
off-chip memory,l1 hit rate,energy consumption,ray reordering,off-chip memory traffic,ray stream memory,data model,power consumption,bandwidth efficient ray,efficient data,l2 cache,ray tracing | Hit rate,Tree traversal,Massively parallel,Ray tracing (graphics),Cache,Computer science,CPU cache,Parallel computing,Energy consumption,Hardware architecture | Conference |
Citations | PageRank | References |
17 | 0.71 | 25 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Kopta | 1 | 82 | 5.73 |
| Konstantin Shkurko | 2 | 33 | 4.33 |
| Josef Spjut | 3 | 101 | 10.20 |
| Erik Brunvand | 4 | 509 | 66.09 |
| Al Davis | 5 | 986 | 54.47 |