Efficient usage of heterogeneous computing architectures requires distribution of the workload on available processing elements. Traditionally, the mapping is based on information acquired from application profiling and utilized in architecture exploration. To reduce the amount of manual work required, statistical application modeling and architecture modeling can be combined with exploration heuristics. While the application modeling side of the problem has been studied extensively, architecture modeling has received less attention. Linear System Level Architecture (LSLA) is a Model of Architecture that aims at separating the architectural concerns from algorithmic ones when predicting performance. This work builds on the LSLA model and introduces non-linear semantics, specifically to support GPU performance and power modeling, by modeling also the degree of parallelism. The model is evaluated with three signal processing applications with various workload distributions on a desktop GPU and mobile GPU. The measured average fidelity of the new model is 93% for performance, and 84% for power, which can fit design space exploration purposes.

异构计算体系结构的有效利用需要在可用处理元素上分配工作负载。传统上，映射基于从应用程序分析获取的信息，并在体系结构探索中利用。为了减少所需的手工工作量，可以将统计应用程序建模和体系结构建模与探索启发式方法结合使用。尽管对该问题的应用程序建模方面已进行了广泛研究，但体系结构建模却很少受到关注。线性系统级体系结构（LSLA）是一种体系结构模型，旨在在预测性​​能时将体系结构问题与算法问题分开。这项工作建立在LSLA模型的基础上，并引入了非线性语义，专门用于支持GPU性能和功耗建模，通过对并行度进行建模。该模型通过在台式机GPU和移动GPU上具有各种工作负载分布的三个信号处理应用程序进行评估。新模型测得的平均保真度在性能上为93％，在功率上为84％，可以满足设计空间探索的目的。