Improving performance by matching imbalanced workloads with heterogeneous platforms
Jie Shen, Ana Lucia Vărbănescu, Peng Zou, Yutong Lu, Henk J. Sips · 2014
Although GPUs are considered ideal to accelerate massively data-parallel applications, there are still exceptions to this rule. For example, imbalanced applications cannot be efficiently processed by GPUs: despite the massive data parallelism, a varied computational workload per data point remains GPU-unfriendly. To efficiently process imbalanced applications, we exploit the use of heterogeneous platforms (GPUs and CPUs) by partitioning the workload to fit the usage patterns of the processors. In this work, we present our flexible and adaptive method that predicts the optimal partitioning. Our method aims to match a quantitative model of the application with the hardware capabilities of the platform, and calculates the optimal match according to a user-given criterion. We evaluate our method in terms of overall performance gain, prediction accuracy, flexibility and adaptivity. Our results, gathered from both synthetic and real-world workloads, show performance gains of up to 60%, accurate predictions for more than 90% of all the 1395 imbalanced workloads we have tested, and confirm that the method adapts correctly to application, dataset, and platform changes (both hardware and software). We conclude that model-based prediction of workload partitioning for heterogeneous platforms is feasible and useful for performance improvement.