fast bvh construction on gpus (eurographics 2009)

Fast BVH Construction on GPUs(Eurographics 2009)

Park, Soonchan

KAIST (Korea Advanced Institute of Science and Technology)

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Contents

● What is BVH● Motivation● Three Algorithm to Construct BVH

● LBVH● SAH Hierarchy Construction● Hybrid GPU Construction Algorithm

● Results & Analysis

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Contents

● What is BVH● Motivation● Three Algorithm to Construct BVH

● LBVH● SAH Hierarchy Construction● Hybrid GPU Construction Algorithm

● Results & Analysis

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What is BVH?

● Bounding Volume Hierarchy● A tree structure on a set of geometric

objects● “Fast Computation”

● Ray tracing ● Collision detection● Visibility Culling

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What is BVH?

● Issues of BVH construction● Construction Time● Effectiveness of Construction

●How much improvement BVH makes– Median Subdivision & Surface Area Heuristic

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Motivation

● BVH Construction

Almost all prior works are about “Purely serial construction algorithms”

Make Efficient Parallel algorithms! on manycore processors

How to make processes of BVH construction appropriate for parallel computation

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Contents

● What is BVH● Motivation● Three Algorithm to Construct BVH

● LBVH● SAH Hierarchy Construction● Hybrid GPU Construction Algorithm

● Results & Analysis

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Contents

● What is BVH● Motivation● Three Algorithm to Construct BVH

● LBVH● SAH Hierarchy Construction● Hybrid GPU Construction Algorithm

● Results & Analysis

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LBVH

● Linear Bounding Volume Hierarchy● Simplest approach to parallelizing BVH

Construction● Sorting input primitives by Morton Codes● BVH Construction Sorting ( O(nlogn) )

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Morton Codes (Z-order)

● Space-filling curve

● Morton Codes (Z-order)● Good locality-preserving● Express space as bits

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Morton Codes (Z-order)

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LBVH

● Linear B.V.H.● Sorting primitives

along the curveparallel radix sort[SHG08]

● Each primitive hasbit expression of position

● How to makethe Hierarchy?

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LBVH

● Make Hierarchy● Test all Primitive i with Primitive i+1

●What levels they are separated●Make list ( (Primitive index) , ( separate level) )

● Resort the list by level

We can have intervals at each level!

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Example

(6, 1)

(3, 2) (6, 2)

(2,3) (3,3) (4,3) (5,3) (6,3) (7,3)

(1,4) (2,4) (3,4) (4,4) (5,4) (6,4) (7,4)

Split list (Prim.Index, Separate Lev.)

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7 81 2 3 4 5 6 LEVEL 1

1 2 3 4 5 6 7 8

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7 81 2 3 4 5 6

123 456

LEVEL 1

LEVEL 2

1 2 3 4 5 6 7 8

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7 81 2 3 4 5 6

123 456

312 4 5 6 7 8

LEVEL 1

LEVEL 2

LEVEL 3

1 2 3 4 5 6 7 8

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7 81 2 3 4 5 6

123 456

312 4 5 6 7 8

1 2

LEVEL 1

LEVEL 2

LEVEL 3

LEVEL 4

1 2 3 4 5 6 7 8

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LBVH

● Pros● Very fast – same complexity as sorting

●+ we use parallel radix sort [SHG 08]● Cons

● Constructed Hierarchy is not optimized●It uniformly subdivides space at the median

● Leaf can has multiple primitives

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Contents

● What is BVH● Motivation● Three Algorithm to Construct BVH

● LBVH● SAH Hierarchy Construction● Hybrid GPU Construction Algorithm

● Results & Analysis

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What is SAH

● Surface Area Heuristic● Answer for optimized architecture

●“which of a number of partitions of primitives will be better?●“which of a number of possible positions to split space will be better?”

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What is SAH

● SAH optimized construction can also be achieved in O(nlogn) [WH06]

● Processes for SAH● Recursively splitting the set of geometric

primitives (usually two parts per step-binary tree)

● Evaluate with “cost function” ●Cost function can be defined

● Find the one with lowest cost● Check all possible split position can be

costly● Sampling method can be applied

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GPU SAH Construction

● Breadth-first construction using work queues

● Parallelization!

Input queue

Output queue

Output queue

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Data-Parallel SAH Split

● Two steps for performing SAH split● Determine the best split position by

evaluating the SAH● Reorder the primitives ( corresponds to

the new split )

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Data-Parallel SAH Split

● Determine the best split position● Approximate SAH computation● Generate k uniformly sampled split

candidates for three axes ( test all the samples in parallel by using 3k threads )

● Each thread computes the SAH cost for its split candidate

● Find split candidate with lowest cost● Reorder the Primitives

● In corresponds to the new splits● Only reorder the indices

●No copy of geometry

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Small Split Operation

● Two main bottleneck● Initial split at the top level of hierarchy is very

slow ● Large # of primitives at Top level

– By using hybrid method (discussed later)● Large # of small splits at Low level

● Problems●Higher compaction costs generated by large # of splits●Vector utilizing is low (Few primitive per split)

● Large # of small size of split makes problem Use different split kernel for small size

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Small Split Operation

● Main Idea● Set Thresh hold to define “Small split”

●Depends on geometry data & cache size (32)

● Use processor’s local memory●to maintain a local work queue●Keep all the geometric primitives

● Pros● Reduce memory bandwidth● Decrease # of Thread

●Maximize utilization of vector operation● Avoid waiting for memory access

15~20% speed up

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Small Split Operation

Times# of

active splits

Level of splits

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Contents

● What is BVH● Motivation● Three Algorithm to Construct BVH

● LBVH● SAH Hierarchy Construction● Hybrid GPU Construction Algorithm

● Results & Analysis

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Hybrid GPUConstruction Algorithm

● LBVH● Not optimized at last

● Shallow hierarchy● Large # of primitives at the leafs

● But FAST● Problem of GPU SAH Construction

● Relatively Slow● Overhead at first level● But it can build optimized hierarchy

● Solution● Top level use LBVH● Others use GPU SAH Construction

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Contents

● What is BVH● Motivation● Three Algorithm to Construct BVH

● LBVH● SAH Hierarchy Construction● Hybrid GPU Construction Algorithm

● Results & Analysis

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Results

● Render several scenes● Comparing with other environments

● One-core not optimized CPU SAH● Full SAH

● Standard CPU BVH ray tracer using ray packets

● Compare with● Construction time, Well Optimized, fps

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Results

Construction TimeAbsolute/relative r.t. perf.

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Results

Construction TimeAbsolute/relative r.t. perf.

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Results

Construction TimeAbsolute/relative r.t. perf.

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Results

● GPU SAH● Show better performance than CPU SAH● Good optimization

● LBVH● Fast, not optimized● Scene dependent

● Hybrid● Middle of GPU SAH & LBVH● can be customized

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Analysis

● Current GPU architecture several features for constructing hierarchy● Special Graphics memory

significantly higher memory bandwidth● Manage fast local memory

●Discussed in Small Split Operation

● Memory● 113 bytes/triangle

●Worst case: when one triangle per leaf It allows multi-million triangle models on current GPU

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Analysis

● Bottleneck Analysis

Core overhead

Memory overhead

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Analysis

● Time Distribution

*Rest = read/write BVH node information,

setting up splits, join rest of steps

“Note that Hybrid build is 10 times faster”

Full SAH build Hybrid build

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Video

● Youtube Video

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Reference

● [SHG08] SATISH N., HARRIS M., GARLAND M.: Designing efficient sorting algorithms for manycore GPUs. Under review (2008).

● [WH06] WALD I., HAVRAN V.: On building fast kd-trees for ray tracing, and on doing that in O(N log N). In Proc. of IEEE Symp.on Interactive Ray Tracing (2006), pp. 61–69.