Moka: Model-based Concurrent Kernel Analysis

Today's GPUs continue to increase the number of compute resources with each new generation. Many data-parallel applications have been re-engineered to leverage the thousands of cores on the GPU. But not every kernel can fully utilize all the resources available. Many applications contain multiple kernels that could potentially be run concurrently. To better utilize the massive resources on the GPU, device vendors have started to support Concurrent Kernel Execution (CKE). However, the application throughput provided by CKE is subject to a number of factors, including the kernel configuration attributes, the dynamic behavior of each kernel (e.g., compute-intentive vs. memory-intensive), the kernel launch order and inter-kernel dependencies. Minor changes in any of theses factors can have a large impact on the effectiveness of CKE. In this paper, we present Moka, an empirical model for tuning concurrent kernel performance. Moka allows us to accurately predict the resulting performance and scalability of multi-kernel applications when using CKE. We consider both static and dynamic workload characteristics that impact the utility of CKE, and leverage these metrics to drive kernel scheduling decisions on NVIDIA GPUs. The underlying data transfer pattern and GPU resource contention are analyzed in detail. Our model is able to accurately predict the performance ceiling of concurrent kernel execution. We validate our model using several real-world applications that have multiple kernels that can run concurrently, and evaluate CKE performance on a NVIDIA Maxwell GPU. Our model is able to predict the performance of CKE applications accurately, providing estimates that differ by less than 12% as compared to actual runtime performance. Using our estimates, we can quickly find the best CKE strategy for our applications to achieve improved application throughput. We believe we have developed a useful tool to aid application programmers to accelerate their applications using CKE.