Multi-CPU And Multi-GPU Collaborative Parallel Rendering In Cluster Node

Parallel rendering extends multiple graphics pipelines with separating graphics ren-deringfromtheunifiedbasicexecutionmodelofrenderingapplication,andrenderingtaskare computed in parallel after being sent to rendering resources. Parallel rendering is anefficient technical approach to improve performance of large-scale and complex scenesrendering.Parallel rendering system is composed of multiple distributed rendering nodes ingeneral. Rendering nodes normally use CPU as general computing processor and GPUas graphics rendering coprocessor. It was hard to produce enough data to make full useof GPU shaders with CPUs in an early rendering node, so only one GPU was deployed innode. With technological development of COTS multi-core processor and graphics hard-ware, rendering nodes could have multiple CPUs and multiple GPUs. Many researchesand applications show that researching on collaborative parallel rendering to fully usecomputing resources in multi-CPU and multi-GPU cluster node is not only a technicalapproach to improve performance of graphics workstation, but also a great foundational‘building block’to compose the larger systems capable of rendering very large data.Hardwarearchitectureofthemulti-CPUandmulti-GPUrenderingnodewasnotfullyconsidered in existing parallel rendering technology, so parallel rendering system couldnot collaboratively do the rendering task in parallel with high performance. To make fulluse of computing resources in node, non-uniform computing units and memory accessarchitecture was considered in character for multi-CPU and multi-GPU rendering node inour researches. And the researches were mainly on multi-CPU multi-GPU collaborativeparallel rendering model and approaches to improve the model’s performance when dorenderingtasksinsort-lastmode. Themainresearchachievementsaredetailedasfollows:(1)To solve the problem that the composition and display stage is coupled with hard-warerenderingstageinexistingparallelrenderingmodelsforclusternode,anovelparallelhybrid rendering model was introduced. And it could make full use of multi-core CPUsand multiple GPUs in cluster node. In order to ensure easy configuration and good s-calability, the model separates graphics rendering from application’s main event loop.With asynchronous DMA transfer and decoupling hardware rendering stage from compo-sition stage by hybrid software and hardware rendering, a parallel rendering pipeline with rendering, readback and composition stages is constructed in node to obtain high render-ing performance. Theoretical analysis and Experiment results show that the model haseasy configuration and good scalability, and it can efficiently improve parallel renderingperformance of multi-CPU and multi-GPU rendering node.(2)To solve the problem of low efficiency and redundant operations of composi-tion on CPU, a novel composition method accelerated by GPGPU computing was intro-duced. The method generated active pixels composition index list with GPGPU tech-nology and totally avoided inactive pixels composition operations on CPU. Theoreticalanalysis shows that speedup of the method is equal to the radio of active pixels percent-age of image and number of the GPU deployed in node. Experiment results show that themethod performance is about3to5times to original one when compositing high resolu-tion images with12%to76%active pixels percentage in the node with4GPUs.(3) To solve the problem of high computing and communication cost of compositionmethod based on CPU-GPU communication model, a novel composition method basedon GPU P2P direct communication model was introduced. It not only avoided lots of dataexchangebetweenGPUandCPU,butalsofullyusedGPUhighspeedmemorybandwidthand powerful computing ability. To optimize local and remote GPU memory access effi-ciencyofthemethodimplementation, PushCompositingOperationandPullCompositingOperation were presented. A novel bitmap-based composition method was also proposedto reduce data transfer and composition operation discrimination. It made compositiononly operate on overlap regions of GPU images, which got by doing set operation on ac-tive pixels lists. Experiment results show that image composition with the bitmap-basedmethod can raise efficiency about40%.(4)To solve the problem that parallel graphics pipeline of existing parallel render-ing framework could not make full use of computing resources in multi-CPU and multi-GPU rendering node, a novel hierarchical sort-last parallel rendering framework betweenmulti-CPU and multi-GPU rendering nodes was introduced. The framework classifiedGPUs into in-node and out-node, and it composited image in two steps with hierarchicalcomposition pipeline. In each step, composition communication model was decided bycharacter of the topology of GPUs interconnect. And inactive pixels were totally avoidedbeing composited and transmitted by using inactive pixels rejection algorithm. Experi-ment results show that the framework could efficiently avoid inactive pixels transfer and has a good rendering and composition performance.