Performance Evaluation and Optimization Mechanisms for Inter-operable Graphics and Computation on GPUs

Abstract

Graphics Processing Units (GPUs) have gained recognition as the primary form of accelerators for graphics rendering in the gaming domain. They have also been widely accepted as the computing platform of choice in many scientific and high performance computing domains. The parallelism offered by the GPUs is used for simultaneous processing of compute and graphics by applications belonging to a range of domains. The availability of programming standards such as OpenCL and OpenGL has been leveraged to achieve the compute-graphics interoperability in the same application. However, given the increasing demands in both compute and graphics for emerging scientific visualization and immersive gaming applications, degradation in efficiency can be seen due to the continual switching between compute/graphics, swapping in and out of their associated runtime environments. We need to better understand how to tune this interoperable environment in order to allow compute and graphics to run both efficiently and simultaneously. Presently we evaluate each of these domains in isolation. In this paper, we evaluate the performance and efficiency of the OpenCL-OpenGL(CL-GL) interoperability(interop) mode. We explore different methods to improve the execution performance of the CL-GL interop-based applications. We propose a slot-based rendering mechanism for CL-GL interop to increase the efficiency of the application. To evaluate CL-GL and our slot-based scheme, we study five scientific applications using OpenCL and OpenGL for compute and graphics rendering. Our study covers two AMD Radeon discrete GPUs and one shared memory AMD APU as test platforms. We demonstrate that leveraging the CL-GL interop interface results in a 2.2X performance increase, and our slot-based rendering provides 60% increase in performance by providing a 24% improvement in L2 cache hit rate on GPUs and APUs.