The prevalence of real time multimedia delivery appliances has led to the developments of a variety of efficient architectures and supporting software technologies. Especially, Ray-Tracing, a well-known physically-based rendering algorithm, has been receiving great attention in research and development. Unfortunately, Ray-Tracing algorithm, being one of the irregular applications, suffers from the performance penalty on SIMT-based machines such as GPGPUs. Specifically, the branch divergence and early-termination issues caused by the irregularity severely degrade the overall hardware utilization, which makes the computation on GPGPU inefficient while traversing through each iterative stages of the algorithm. The probabilistic termination of ray-tracing paths poses a critical issue for efficient parallel execution on GPGPUs. Moreover, the conventional overhead of memory transfer limits the effectiveness of data marshaling for GPU computing. To address these problems, we proposed a pipeline-based Runtime technique which leverages the feature of Shared-Virtual-Memory (SVM) of the HSA-compliant systems that combines and regroups the workload from different stages into one kernel computation in Ray-Tracing and greatly improved the resource utilization of GPGPU. Our experiments illustrate that the proposed runtime technique can boost ray-tracing performance significantly while effectively increase the utilization of HSA-compliant heterogeneous systems.
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