Graphics on a Stream Processor

1. Graphics on a Stream Processor Peter Djeu March 20, 2003

2. Polygon Rendering on a Stream Architecture Owens, Dally, Kapasi, Rixner, Mattson, Mowery Stanford Computer Sciences Lab

3. Goals • Create a working version of OpenGL on the Imagine Stream Processor • Why Imagine? Imagine is programmable. Contemporary graphics cards were not. • Use programmability and flexibility to head towards the goal of 80 million poly’s / frame. • The trend is more flexibility: over 200 proposed extensions to OpenGL, people already used complex software rendering (like multipass hacks e.x: shadows)

4. Imagine Review 1

5. Imagine Review 2

6. Basic Definitions • Streams are sets of data elements. All elements are a single data type. • Index streams • Kernels are pieces of code that operate on streams. They take a stream as input and produce a stream as output. Kernels can be chained together.

7. Basic Definitions (page 2) • Instruction-Level Parallelism - issuing independent instructions in the same cycle (ex: 6 functional units in an ALU cluster, VLIW) • Data-Level Parallelism - performing the same operation on multiple pieces of data (ex: 8 ALU clusters operating on a single stream, vector computing)

8. Basic Definitions (page 3) • Produce-Consumer Locality • Occurs when one component of a system is producing something that is immediately consumed by another component of the system. • In the polygon rendering pipeline, occurs when one kernel produces a stream that is immediately consumed by another kernel • the Stream Register File (SRF) and local registers exploit producer-consumer locality

9. Why are Streams Good for Graphics? • Rendering is inherently parallel • triangles can be processed in parallel, but may need to be ordered when drawn to the screen • No need for caching, primitives are rendered and then discarded (not textures) • Latency not as important with streams. Stream proc’s emphasize max throughput. • VLSI has allowed us to build stream proc’s.

10. Homogenous Streams • Kernels are most efficient if all elements in the stream require identical operations (less complexity and no stalls from conditionals). • Use conditional streams to separate heterogeneous streams into homogenous. • Ex: backface culling (forward, backward)

11. Benefits of Using Imagine • Memory hierarchy • local registers are great for ALU’s; SRF is a big cache to exploit producer-consumer locality • SIMD architecture exploits parallelism and homogeneity in streams • One kernel at a time, and it is run on all 8 ALU clusters (data-level parallelism on streams) • Granularity on the stream level, so less instruction issues, less bandwidth needed.

12. 3 Stage Pipeline • Geometry - generates triangles • Rasterization - generates fragments • Composite - generates pixels See figure: each node is a kernel, each edge is a stream

13. Diagram of General Polygon Pipeline

14. Load Balancing (A Problem) • Large triangles take longer to be rasterized, potentially slowing down the pipeline. • Sol’n: every cycle, ALU clusters fetch if they are idle and keep processing otherwise • Sol’n may not be good for all situations, since we incur fetch cost even if no one really needs to fetch (ex: when triangles are all large but roughly equal in size)

15. Ordering (A Problem) • OpenGL requires in-order completion • What situations require in-order completion? • What situations do not require in-order comp.? • Can we solve this ordering problem without having to sort? For instance, ask user to label triangles with priority. Or assign priority based on orig. order and use a second Z-buffer test using the priority val.

16. Ordering (A Problem) (page 2) • Paper’s sol’n: Create 2 streams, then concatenate ordered onto unordered • Assign in-order ID to each triangle in the stream (now extra ID value is in the stream) • Since overlaps are rare, use a hash func. to find when two fragments overlap on the screen • Resolve overlaps by using the ID number • Unclear: how did they implement a hash table with 2-bits per hash entry (32 * 8 words total)?

17. Consumer-Producer Locality(An Advantage) • Break up the entire input into batches, then make sure the batches fit inside the SRF • Trips to main memory can really be reduced • But Owens et al. batch their input before they beginning processing-- is this fair? Dynamic batching may not be as effective as their hand-crafted batches.

18. Producer Consumer Locality Using the Stream Register File (SRF)

19. Other Advantages to the Imagine Implementation • Latency Tolerance - ALU clusters process the current batch while the memory system fetches the next batch • Pipeline reordering - ex: turn off blending and move the texturing kernel after the depth test kernel, cull non-visible fragments • Flexible resource allocation - hardware is reused at each stage, no a priori allocation

20. 4 Testing Platforms • Software - no hardware acceleration • Imagine • Nvidia Quadro (real) • Nvidia Quadro (ideal) - a scaling of the Quadro’s real-world performance to its advertised peak performance • is this a good idea or a bad idea?

21. Frame Rates for the 4 Scenes

22. 4 Test Scenes and Batch Sizes • Sphere - 81920 total triangles, B: 80 triangles • Advs-1 - point-sampled 512 x 512 texture mapped, B: 256 vertices • Advs-8 - mipmapped 512 x 512 base level texture, B: 24 vertices • Fill - fills entire window, 512 x 512 texture map, B: 16 vertices Why is there such a dramatic drop in batch size between Advs-1 and Advs-8?

23. Results • Low (Memory to SRF traffic), as compared to (SRF to local register files traffic) means current memory hierarchy is being well utilized (i.e. hardware is exploiting producer-consumer locality) • Hash function produces too many false conflicts, leading to too much computation (see Fig. 9). Future work: better hash func.

24. Results (page 2) • Not enough ALU’s - ADVS-8 needed ~2.5 times the ALU ops than ADVS-1, but performance dropped > 50% (however, batch size also dropped significantly) • Batch size is often too small to be efficient • startup costs, paid per batch • priming and draining inner loops (sub-optimal saturation as loops are starting/winding down?)

25. Measure of Relative Performance (Logarithmic)

26. Batch Processing, Clearing Z Buffer in Background

27. Future Work via Hardware Extensions • Problem: The Imagine hardware cannot currently compete with the powerful rasterizers in Graphics Cards • Sol’n: Add hardware that is dedicated to Graphics and polygon rendering, such as: • texture cache • ALU in memory system for doing on-the-spot depth test, alpha blend, filtering

28. Final Impressions • Impressive that they got the OpenGL pipeline working on a general-purpose stream processor • Not enough ALU power, and things may not improve with more hardware (ordering and communication constraints may still bottleneck) • Programmability is good, but not worth it if the overall performance on existing applications is worse • Limited size SRF very limiting when triangle size (and hence generated fragments) is unbounded

29. Comparing Reyes and OpenGL on a Stream Architecture Owens, Khailany, Towles, Dally Stanford Computer Sciences Lab

30. Motivation • Recent trends in graphics: • Smaller triangles in modeling • Demand for more complex shading • Memory bandwidth becoming more scarce • The Reyes Rendering Pipeline addresses these issues.

31. OpenGL Pipeline Stages • Transformations and vertex ops - 1st pass for shading (interpolation, light, glColor) • Assemble / Clip / Project - view frustum • Rasterize - triangles become fragments • Fragment ops - 2nd pass for shading (texturing, blending) • Visibility / Filter - depth buffer, composite

32. Reyes Rendering Pipeline • Dice / Split - primitives are subdivided into micropolygons • Shade - shading done in eye space (in the paper, a screen space calculation is used) • Sample - projection to screen space • Visibility / Filter - depth-buffer, composite

33. The Reyes and OpenGL Pipeline Stages

34. Differences Between OpenGL and Reyes

35. Why are We Using Imagine? (again?) • Imagine is parallel, exploits producer-consumer locality • Imagine is not specifically designed for either pipeline so it is a good platform to use to compare and contrast 2 pipelines • Better than OpenGL optimized graphics cards because it is not so specialized

36. Stanford Real-Time Shading Language (RTSL) • High level language for writing shader programs for targeted hardware (Imagine) • For OpenGL: Generates code for a Vertex Program and a Fragment Program • For Reyes: Generates code for a Vertex Program (4 vertices to a micropolygon)

37. Subdividing Micropolygons • Primitives start out as a collection of B-Spline Control Points • 4 control points are treated as 1 quad • If all edges of a quad are less than a threshold length, than return the the quad • Else subdivide the quad into 4 quads and repeat the test.

38. Fixing Subdivision Cracks • Store equations of edges rather than the coordinates of vertices • Each edge is finalized immediately when it falls under the acceptable tolerance length, even if adjoining edges are still too long • Edge equations are extended to fill in cracks created by Catmull-Clark subdivision

39. Subdivision Algorithm that Avoids Cracking

40. The Rest of the Reyes Pipeline • Shading - Perform all shading in this stage. Use Coherent Access Textures if possible: • requires power-of-two sized diced subdivision • good: no texture filtering due to alignment • bad: can result in ~ twice as many micropolygons • Sampling - Better load balancing, primitives are bounded and flat shaded, unlike OpenGL • Composite and Filter - Identical to OpenGL

41. Experimental Testing • Isim - Full Imagine simulator used to test OpenGL • Idebug - Faster Imagine simulator (lack kernel stalls or cluster occupancy effects) used to test Reyes

42. Problems with the Experimental Testing System • The paper is unclear on why it chose to test Reyes on Idebug rather than Isim. • The application of the +20% rule may be faulty here since it is not shown Reyes is an “average” application. • Extra scratchpad memory, more microcode store, and more local registers were added to Reyes. Were they added to OpenGL?

43. Test Scenes • Teapot-N - N is either 20, 30, 40, or 64. Contains 2N2 triangles and 3 light sources • Pin-1 - 5 textures, point sampled textures • Pin-8 - 5 textures, mipmapped textures • Armadillo - Complex marble procedural shader (1200 flops / fragment!)

44. Results • Scenes rendered by OpenGL have a much higher frame rate than scenes rendered by Reyes (an order of magnitude difference)

45. Frame Rate of OpenGL vs. Reyes (logarithmic)

46. Results (page 2) • Subdivision (in the geometry stage) is consuming most of the processing time. Recall this is a stage unique to Reyes. • Even excluding the subdivision stage, Reyes is still twice as slow as OpenGL. • The next largest bulk of total execution time is the vertex processor. Since it is now doing both forms of shading, it is reasonable that it will take longer.

47. Results (page 3) • Too many 0-coverage quads are generated. These quads usually have extreme aspect ratios and cover no pixels at all in screen space. • Cause: 2-way split at each subdivision iteration, producing 4 child quads • Sol’n: Use a 1-way split at each subdivision iteration, producing 2 child quads instead.

48. Work Distribution in OpenGL and Reyes

49. Results (page 4) • Small triangles bog down OpenGL • Less opportunity to interpolate on small triangles, so full shading computation needs to be used more often • High triangle volume means that the start-up cost of the rasterizer is incurred more often

50. OpenGL Runtime as a Function of Triangle Size