GAMORRA: An API-Level Workload Model for Rasterization-based Graphics Pipeline Architecture

TL;DR

GAMORRA is a new API-level workload model for rasterization-based graphics pipelines that improves frame rendering time estimation accuracy by modeling each pipeline stage and using regression, outperforming existing models.

Contribution

The paper introduces GAMORRA, a novel workload model that accurately estimates graphics pipeline workload using stage complexity and regression, with a hybrid training scheme and benchmark tuning.

Findings

GAMORRA achieves 9.45% average frame time estimation error, lower than 13.23% of FCM.

GAMORRA improves frame time underestimations by 1.1% over FCM.

The model incurs a 4.58% increase in time complexity compared to FCM.

Abstract

The performance of applications that require frame rendering time estimation or dynamic frequency scaling, rely on the accuracy of the workload model that is utilized within these applications. Existing models lack sufficient accuracy in their core model. Hence, they require changes to the target application or the hardware to produce accurate results. This paper introduces a mathematical workload model for a rasterization-based graphics Application Programming Interface (API) pipeline, named GAMORRA, which works based on the load and complexity of each stage of the pipeline. Firstly, GAMORRA models each stage of the pipeline based on their operation complexity and the input data size. Then, the calculated workloads of the stages are fed to a Multiple Linear Regression (MLR) model as explanatory variables. A hybrid offline/online training scheme is proposed as well to train the model. A suite of benchmarks is also designed to tune the model parameters based on the performance of the target system. The experiments were performed on Direct3D 11 and on two different rendering platforms comparing GAMORRA to an AutoRegressive (AR) model, a Frame Complexity Model (FCM) and a frequency-based (FRQ) model. The experiments show an average of 1.27 ms frame rendering time estimation error (9.45%) compared to an average of 1.87 ms error (13.23%) for FCM which is the best method among the three chosen methods. However, this comes at the cost of 0.54 ms (4.58%) increase in time complexity compared to FCM. Furthermore, GAMMORA improves frametime underestimations by 1.1% compared to FCM.