GPU Activity Prediction using Representation Learning
This addresses GPU performance prediction for developers and system optimizers, but it is incremental as it applies an existing method to a new domain.
The paper tackled GPU activity prediction by proposing a representation learning approach that models performance metrics as temporal functions of executed instructions, achieving high accuracy and non-trivial predictive power on a benchmark.
GPU activity prediction is an important and complex problem. This is due to the high level of contention among thousands of parallel threads. This problem was mostly addressed using heuristics. We propose a representation learning approach to address this problem. We model any performance metric as a temporal function of the executed instructions with the intuition that the flow of instructions can be identified as distinct activities of the code. Our experiments show high accuracy and non-trivial predictive power of representation learning on a benchmark.