Data Management Techniques Sung-Eui Yoon KAIST URL:

Data Avalanche (or Data Explosions) There are too much data out data!!! cmsc838b/Project/Parija_Spacco/images/

Geometric Data Avalanche ● Massive geometric data ● Due to advances of modeling, simulation, and data capture techniques ● Time-varying data (4D data sets)

CAD Model: Double Eagle Oil Tanker 82 million triangles (4 gigabyte)

CAD Model: Boeing 777 Ray Tracing Boeing 777, 470 million triangles Excerpted from SIGGRAPH course note on massive model rendering

Scanned Model: ST. Matthew Model 372 million triangles (10GB)

Possible Solutions? ● Hardware improvement will address the data avalanche? ● Moore’s law: the number of transistor is roughly double every 18 months

Current Architecture Trends Accumulated growth rate during 1999~2009 (log scale) access speed disk access speed Data access time becomes the major computational bottleneck!

Four Orthogonal Approaches ● Cache-coherent layouts ● Random-accessible compressed meshes ● Cache-oblivious ray reordering ● Hybrid parallel continuous collision detection

Overview ● Cache-coherent layouts ● Random-accessible compressed meshes ● Cache-oblivious ray reordering ● Hybrid parallel continuous collision detection

Cache-Coherent Layouts of Meshes ● One dimensional data layout of a mesh ● Reduce the number of cache misses ● Cache-aware or cache-oblivious layouts ● Minimize the number of cache misses for a specific or various cache parameters (e.g., cache block size) [Yoon et al. SIG05, VIS06, Euro06] vava vbvb vdvd vcvc vava vbvb vdvd vcvc One dimensional layout

Block-based I/O Model [Aggarwal and Vitter 88] CPU or GPU Fast memory or cache Slow memory Block transfer Disk 1 sec Access time: sec10 -6 sec

Applications ● View-dependent meshes ● View-dependent rendering ● Triangle meshes ● Isocontour extractions ● Hierarchies ● Ray tracing ● Collision detection

View-Dependent Rendering using LODs Improving GPU vertex cache Utilization GeForce 6800 (January 2005)

Applications ● View-dependent meshes ● View-dependent rendering ● Triangle meshes ● Isocontour extractions ● Hierarchies ● Ray tracing ● Collision detection Puget sound, 134 M triangles Isocontour z(x,y) = 500m Achieve up to 20X improvement on iso-contouring

Applications ● View-dependent meshes ● View-dependent rendering ● Triangle meshes ● Isocontour extractions ● Hierarchies ● Ray tracing ● Collision detection Achieve 30% ~ 300% performance improvement

Advantages ● General ● Works well for various applications ● Cache-oblivious ● Can have benefit for all levels of the memory hierarchy (e.g. CPU/GPU caches, memory, and disk) ● No modification of runtime applications ● Only layout computation Source codes are available as a library called OpenCCL

Overview ● Cache-coherent layouts ● Random-accessible compressed meshes ● Cache-oblivious ray reordering ● Hybrid parallel continuous collision detection

Random-Accessible Compressed Data ● Compression methods of meshes and hierarchies ● Reduce the memory requirements ● Supports random accesses on meshes and hierarchies ● Can be useful to many different applications [Kim et al. Tech. Report 09; Kim et al., TVCG 09; Yoon and Lindstrom, VIS 07]

Hierarchical-Culling oriented Compact Meshes (HCCMeshes) ● Consists of two parts: ● i-HCCMeshes (in-core representation) ● o-HCCMeshes (out-of-core representation)

21 Data Access Framework Main memory User Request Data Data pool

22 Data Access Framework - Out-of-Core Technique Main memory User Request Data Cached data External drive Data pool Cluster c 0 Cluster c 1 Cluster c 2 Cluster c 3 Cluster c 4 Cluster c 5 … Cluster c n cluster ID cluster

23 HCCMeshes Main memory User Request Data Cached data External drive Data pool cluster ID Decomp. cluster compressed cluster Decomp. Compressed Data Cluster c m Cluster c 0 Cluster c 1 Cluster c 2 Cluster c 3 Cluster c 4 Cluster c 5 Cluster c 6 Cluster c 7 Cluster c 8 Cluster c 9 Cluster c 10 Cluster c 11 Cluster c 12 Cluster c 13 … o-HCCMeshi-HCCMesh Support hierarchical random access!

24 Main Benefits ● Use a lower memory space and working set size ● o-HCCMeshes have 20:1 compression ratios ● i-HCCMeshes have 6:1 compression ratios ● Improve runtime performance

25 Applications ● Whitted-style ray tracing ● LOD-based ray tracing ● Collision detection ● Photon mapping ● Non-photorealistic rendering Source codes are available as OpenRACM

26 Results

27 Overview ● Multi-resolution representations ● Random-accessible compressed meshes ● Cache-oblivious ray reordering ● Hybrid parallel continuous collision detection

28 Challenges ● Secondary rays generated show low ray coherence ● Result in low cache utilizations ● In case of ray tracing massive models, expensive cache misses occur (e.g. L1/L2, main memory) Landscape ( >1000 M ) St.Matthew ( 372 M )

29 Goal ● Design an efficient algorithm for converting incoherent secondary rays to coherent ● Achieve a high cache coherence of these rays ● The performance improvement of ray tracing

30 Ray Reordering Framework Camera information Ray generation Ray reordering Ray buffer Hit points and material information Ray processing Disk Caches L1 Main memory Scene information [Moon et al., under review]

31 Applications ● Path tracing ● Photon mapping

32 Result – Path Tracing (Video)Video ● 104 M triangles ● (12.8 GB) ● 512*512 resolution ● 100 path ● 8 area lights

33 Result – Photon Mapping ● 128 M triangles ● (15.7 GB) ● Cache 19% of all the data ● 4 area lights ● 13 X speedup

34 Overview ● Multi-resolution representations ● Random-accessible compressed meshes ● Cache-oblivious ray reordering ● Hybrid parallel continuous collision detection

35 Collision Detection ● Collision detection is used in various fields ● Game, movie, scientific simulation and robotics

36 Discrete collision detection (DCD) Discrete VS Continuous Time step (i-1) Time step (i)

37 Continuous collision detection(CCD) Discrete VS Continuous Time step (i-1) Time step (i)

38 Discrete collision detection (DCD) Discrete VS Continuous Time step (i-1) Time step (i) ?

39 Discrete VS Continuous Continuous CDDiscrete CD AccuracyAccurateMay miss collisions Computation time ExpensiveVery fast

40 Motivation ● Continuous collision detection ● Accurate, but slow for complex models ● Hardware trend ● CPUs and GPUs are increasing the # of cores ● Heterogeneous architectures ● Intel Larabee architecture ● Previous approaches ● Utilize either multi-core CPUs or GPUs ● Not enough performance for interactive applications

41 Hybrid Parallel CCD [Kim et al. PG 09] ● Takes advantages of both: ● Multi-core CPU architectures ● GPU architectures ● Achieves interactive performance for various deforming models consisting of tens or hundreds of thousand triangles CCD Multi-core CPU Multi-core CPU Multi-core CPU Multi-core CPU GPU … …

42 Results ● Performance of HPCCD utilizing both CPUs and GPUs Source codes are available as a library called OpenCCD

43 Results

44 Conclusions ● Data explosion and lower growth rate of data access time ● Discussed three different techniques as a data management method ● Cache-coherent layouts ● Random-accessible compressed data ● Cache-oblivious ray reordering ● Hybrid continuous collision detection ● Applied to rendering and collision detection ● Observed meaningful performance improvement

45 Acknowledgements ● Research collaborators ● TaeJoon Kim, DukSu Kim, Pio Claudio, BooChang Moon, YongYoung Byun, JaePil Heo, SeungYong Lee, YongJin Kim, JaeHyuk Heo, John Kim, Peter Lindstrom, Valerio Pascucci, Dinesh Manocha ● Funding sources ● Microsoft Research Asia ● KAIST seed grant ● Ministry of Knowledge Economy ● Samsung ● Korea Research Foundation