.png){kind=icon}
.png){kind=icon}
Return
Optimal Split Point Placement for Predictable GPU Wavefront Splitting
.png){kind=icon}
OA
AI
DOI：10.1145/3769118.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Predictable wavefront splitting (PWS) is an optimization technique for graphics processing units (GPUs) to address the performance and worst-case execution time (WCET) impacts of branch divergence. PWS relies on manual annotation by the GPU programmer; these choices affect the resulting WCET. This work automates this process with two key approaches. First, we formulate the optimal annotation as an integer quadratic programming (IQP) problem such that the solution guarantees the lowest WCET. Second, we show that the problem can be solved with an optimal polynomial-time dynamic programming algorithm that achieves the same solutions as the IQP. We implement our algorithm in a compiler flow for an AMD GPU, and we deploy the annotated executable on a gem5 micro-architectural implementation of the AMD GCN3 GPU. We evaluate our implementation on a benchmark suite provided by AMD and supplement it with an extensive set of synthetic benchmarks. Our evaluation shows that these two approaches are able to reduce the WCET by between 13% and 31% compared to five baseline algorithms.
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
2.6
Papers:
227
Citations:
2.3K
Organization
No organization information available