Memory Management and Parallelization

Paul Arthur Navrátil

The University of Texas at Austin

Overview

• Uniprocessor Coherent Ray Tracing– Pharr et al., 1997

• Parallel Ray Tracing Summary– Chalmers, et al. 2002

• Demand-Driven Ray Tracing– Wald, et al. 2001

• Hybrid Scheduling– Reinhard, et al. 1999

Background: Reyes [Cook et al. 87]

Inspirations– Texture cache, CATs

– Programmable shader

– Single primitive type

– Dicing

– Memory effects of scan-line architecture

Pharr: System

• Use both texture and geometry ‘cache’– Lazy loading, LRU replacement

• One internal primitive – triangles– Optimize ray intersection calculation

– Known space requirements to represent

– Tessellation of other primitives increases space reqs

– Procedurally generated geometry

Pharr: Geometry Cache

• Geometry grids – regular grid of voxels– Few thousand triangles per voxel– Acceleration grid of few hundred triangles for ray

intersection calculation– All geometry of voxel stored in contiguous block of

memory, independent of geometry in other voxelsspatial locality in scene tied to spatial locality in mem

– Different voxel sizes causes memory fragmentation– Adaptive voxel sizes?

Voxel size bounded by cache size for hardware impl?

Pharr: Ray Grouping

• Scheduling grid -- Queue all rays inside voxel– Dependencies in ray tree prevent perfect scheduling

– Store all information needed for computation with rayeach ray can be independently calculated (parallelism!)

– Exploits coherence from beam of rays, disparate rays that move through same space

– Superior to: fixed-order traversal of ray tree; ray clustering

Pharr: Radiance Calculation

• Outgoing radiance is emitted radiance plus weighted average of incoming radiances

• fr is bidirectional reflectance distribution function (BRDF)• At intersection, weights calculated for each spawned

secondary ray• Final weight is product of all BRDF values of all surfaces

on path from point on ray to the image plane

Pharr: Voxel Scheduling

• Naïve – iterate across voxels• Better – weight voxels by cost and benefit

– Cost: how expensive to process the rays in the voxel?• High geometry in voxel has higher cost

• Much voxel geometry not in memory has higher cost

– Benefit: how much progress to completion from voxel?• Many rays in voxel yields more benefit

• Large weights on rays yields more benefit

Pharr: System Summary

Pharr: Lazy Loading Results

Pharr: Reordering Results

Pharr: Scheduling Results

Pharr: Discussion

• Parallelization– Ray independence, load balanced geometry, lazy

geometry loading helps– Will cache results hold in distributed model?

• Modern architecture– Testing on 190 MHz MIPS R 10000 w/ 1GB RAM– Can modern arch hold scenes in memory

(no secondary storage usage)

• Hardware Acceleration– Use memory/cache/GPU rather than disk/memory/CPU

Chalmers: Parallel Ray Tracing• Demand Driven

– Scene divided into subregions, or tasks– Processors given tasks statically or by a master– Balance with task balancing or adaptive regions [Fig 3.4]

• Data Parallel– Object data distributed across processors– Distribute objects according to spatial locality; a hierarchical

spatial subdivision; or randomly [Fig 3.7]

• Hybrid Scheduling– Run demand-driven and data-parallel tasks on same processors– DD ray traversal/DP ray-object intersect [Scherson and Caspary 88]

– DD intersection/DP ray generation [Jevans 89]

– Ray coherence [Reinhard and Jansen 99]

Wald: Demand Driven Ray Tracing [Wald et al. 01]

• Exploit cache and space coherence with modern processors (Dual Pentium III 800 MHz, 256 MB)

• Use SIMD instruction set to achieve data-parallelism (e.g., Barycentric coordinate test)

Wald: Performance [Wald et al. 01]

Wald: Performance [Wald et al. 01]

Reinhard: Hybrid Scheduling [Reinhard et al. 99]

• Data-parallel approach with demand-driven subtasks to load balance– Data-parallel tasks preferred, DD subtasks requested

from master when no DP tasks are available

Reinhard: Hybrid Scheduling [Reinhard et al. 99]

Reinhard: Performance [Reinhard et al. 99]