Dynamic memory allocation in massively parallel systems often suffers from drastic performance decreases due to the required global synchronization. This is especially true when many allocation or deallocation requests occur in parallel. We propose a method to alleviate this problem by making use of the SIMD parallelism found in most current massively parallel hardware. More specifically, we propose a hybrid dynamic memory allocator operating at the SIMD parallel warp level. Using additional constraints that can be fulfilled for a large class of practically relevant algorithms and hardware systems, we are able to significantly speed-up the dynamic allocation. We present and evaluate a prototypical implementation for modern CUDA-enabled graphics cards, achieving an overall speedup of up to several orders of magnitude.
We propose a unified rendering approach that jointly handles motion and defocus blur for transparent and opaque objects at interactive frame rates. Our key idea is to create a sampled representation of all parts of the scene geometry that are potentially visible at any point in time for the duration of a frame in an initial rasterization step. We store the resulting temporally-varying fragments (t-fragments) in a bounding volume hierarchy which is rebuild every frame using a fast spatial median construction algorithm. This makes our approach suitable for interactive applications with dynamic scenes and animations. Next, we perform spatial sampling to determine all t-fragments that intersect with a specific viewing ray at any point in time. Viewing rays are sampled according to the lens uv-sampling for depth-of-field effects. In a final temporal sampling step, we evaluate the predetermined viewing ray/t-fragment intersections for one or multiple points in time. This allows us to incorporate all standard shading effects including transparency. We describe the overall framework, present our GPU implementation, and evaluate our rendering approach with respect to scalability, quality, and performance.
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