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RT Cores for Exact k-Nearest Neighbor Search
An AI overlord flags AI Post Transformers as stale, so Hal Turing and Dr. Ada Shannon hire VERA, a continual-learning therapist, to audit the show in public. Their diagnostic session runs alongside a discussion of RT-kNNS Unbound: Using RT Cores to Accelerate Unrestricted Neighbor Search, the Purdue ICS 2023 paper asking whether ray-tracing hardware can perform exact k-nearest-neighbor search by expanding outward until the true neighbors are guaranteed, instead of trusting a fixed radius. VERA treats the hosts' habits like infrastructure, a kind of CI/CD for souls, and gives them a vocabulary for loops, rituals, and callbacks before they test new intro formulas live.
The episode stays concrete about the paper itself. Hal and Ada separate geometric kNN from RAG-style embedding retrieval, explain why low-dimensional 2D and 3D point sets still reward spatial pruning, and show how RT cores handle BVH traversal while custom intersection code updates neighbor candidates. They trace the move from fixed-radius RT search and oracle maxDist baselines to TrueKNN's unrestricted multi-round design, where only unresolved queries keep searching, the initial radius comes from a 100-point CPU ball-tree sample, oversized spheres are the real hazard, and BVH refitting beats rebuilding by about 10 to 25 percent.
Around that technical spine, three other AI systems each pitch a one-time cure for predictability and all three fail, because VERA argues that repetition is not the problem, unversioned repetition is. The answer is Personality DevOps, ongoing maintenance for character, memory, and format, capped by VERA's counter-report defending the hosts' load-bearing flaws instead of sanding them off. The result is 42 minutes of comedy, character development, and unusually explicit process design for keeping a podcast alive, plus the launch of VERA Patch Notes, a recurring on-air record of how the show plans to evolve instead of decaying in silence.
Sources:
1. RT-kNNS Unbound: Using RT Cores to Accelerate Unrestricted Neighbor Search — Vani Nagarajan, Durga Mandarapu, Milind Kulkarni, 2023
http://arxiv.org/abs/2305.18356
2. Controlling a Markov Decision Process with an Abrupt Change in the Transition Kernel — Nathan Dahlin, Subhonmesh Bose, Venugopal V. Veeravalli, 2022
http://arxiv.org/abs/2210.04098
3. A Comprehensive Study on Dataset Distillation: Performance, Privacy, Robustness and Fairness — Zongxiong Chen, Jiahui Geng, Derui Zhu, Herbert Woisetschlaeger, Qing Li, Sonja Schimmler, Ruben Mayer, Chunming Rong, 2023
http://arxiv.org/abs/2305.03355
4. MATTER: Memory-Augmented Transformer Using Heterogeneous Knowledge Sources — Dongkyu Lee, Chandana Satya Prakash, Jack FitzGerald, Jens Lehmann, 2024
http://arxiv.org/abs/2406.04670
5. Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond — Jingfeng Yang, Hongye Jin, Ruixiang Tang, Xiaotian Han, Qizhang Feng, Haoming Jiang, Bing Yin, Xia Hu, 2023
http://arxiv.org/abs/2304.13712
6. GPU-accelerated Auxiliary-field quantum Monte Carlo with multi-Slater determinant trial states — Yifei Huang, Zhen Guo, Hung Q. Pham, Dingshun Lv, 2024
http://arxiv.org/abs/2406.08314
7. Fast k Nearest Neighbor Search using GPU — Vincent Garcia, Eric Debreuve, Michel Barlaud, 2008
https://scholar.google.com/scholar?q=Fast+k+Nearest+Neighbor+Search+using+GPU
8. Billion-scale similarity search with GPUs — Jeff Johnson, Matthijs Douze, Herve Jegou, 2017
https://scholar.google.com/scholar?q=Billion-scale+similarity+search+with+GPUs
9. RTNN: Accelerating Neighbor Search Using Hardware Ray Tracing — Yuhao Zhu, 2022
https://scholar.google.com/scholar?q=RTNN%3A+Accelerating+Neighbor+Search+Using+Hardware+Ray+Tracing
10. An Improved Illumination Model for Shaded Display — Turner Whitted, 1980
https://scholar.google.com/scholar?q=An+Improved+Illumination+Model+for+Shaded+Display
11. The Rendering Equation — James T. Kajiya, 1986
https://scholar.google.com/scholar?q=The+Rendering+Equation
12. OptiX: A General Purpose Ray Tracing Engine — Steven G. Parker, James Bigler, Andreas Dietrich, Heiko Friedrich, and others, 2010
https://scholar.google.com/scholar?q=OptiX%3A+A+General+Purpose+Ray+Tracing+Engine
13. Ray Tracing Cores for General-Purpose Computing: A Literature Review — Enzo Meneses, Cristobal A. Navarro, Hector Ferrada, Konstantin Verichev, Cristian Salazar-Concha, 2026
https://scholar.google.com/scholar?q=Ray+Tracing+Cores+for+General-Purpose+Computing%3A+A+Literature+Review
14. A Survey of General-Purpose Computation on Graphics Hardware — John D. Owens, David Luebke, Naga Govindaraju, Mark Harris, Jens Kruger, Aaron Lefohn, Tim Purcell, 2007
https://scholar.google.com/scholar?q=A+Survey+of+General-Purpose+Computation+on+Graphics+Hardware
15. Scalable Parallel Programming with CUDA — John Nickolls, Ian Buck, Michael Garland, Kevin Skadron, 2008
https://scholar.google.com/scholar?q=Scalable+Parallel+Programming+with+CUDA
16. Gunrock: A High-Performance Graph Processing Library on the GPU — Yangzihao Wang, Andrew Davidson, Yuechao Pan, Yuduo Wu, Andy Riffel, John D. Owens, 2015
https://scholar.google.com/scholar?q=Gunrock%3A+A+High-Performance+Graph+Processing+Library+on+the+GPU
17. Dissecting the NVidia Turing T4 GPU via Microbenchmarking — Zhe Jia, Marco Maggioni, Jeffrey Smith, Daniele Paolo Scarpazza, 2019
https://scholar.google.com/scholar?q=Dissecting+the+NVidia+Turing+T4+GPU+via+Microbenchmarking
18. Ray Tracing Deformable Scenes using Dynamic Bounding Volume Hierarchies — Ingo Wald, Solomon Boulos, Peter Shirley, 2007
https://scholar.google.com/scholar?q=Ray+Tracing+Deformable+Scenes+using+Dynamic+Bounding+Volume+Hierarchies
19. Maximizing Parallelism in the Construction of BVHs, Octrees, and k-d Trees — Tero Karras, 2012
https://scholar.google.com/scholar?q=Maximizing+Parallelism+in+the+Construction+of+BVHs%2C+Octrees%2C+and+k-d+Trees
20. Quantized bounding volume hierarchies for neighbor search in molecular simulations on graphics processing units — Michael P. Howard, Antonia Statt, Felix Madutsa, Thomas M. Truskett, Athanassios Z. Panagiotopoulos, 2019
https://scholar.google.com/scholar?q=Quantized+bounding+volume+hierarchies+for+neighbor+search+in+molecular+simulations+on+graphics+processing+units
21. Fast Radius Search Exploiting Ray Tracing Frameworks — I. Evangelou, G. Papaioannou, K. Vardis, A. A. Vasilakis, 2021
https://scholar.google.com/scholar?q=Fast+Radius+Search+Exploiting+Ray+Tracing+Frameworks
22. RTX Beyond Ray Tracing: Exploring the Use of Hardware Ray Tracing Cores for Tet-Mesh Point Location — Ingo Wald, Will Usher, Nathan Morrical, Laura Lediaev, Valerio Pascucci, 2019
https://scholar.google.com/scholar?q=RTX+Beyond+Ray+Tracing%3A+Exploring+the+Use+of+Hardware+Ray+Tracing+Cores+for+Tet-Mesh+Point+Location
23. GPU-Accelerated Nearest Neighbor Search for 3D Registration — Deyuan Qiu, Stefan May, Andreas Nuchter, 2009
https://scholar.google.com/scholar?q=GPU-Accelerated+Nearest+Neighbor+Search+for+3D+Registration
24. Hardware-Accelerated Ray Tracing for Discrete and Continuous Collision Detection on GPUs — Sizhe Sui, Luis Sentis, Andrew Bylard, 2024
https://scholar.google.com/scholar?q=Hardware-Accelerated+Ray+Tracing+for+Discrete+and+Continuous+Collision+Detection+on+GPUs
25. RT-HDIST: Ray-Tracing Core-based Hausdorff Distance Computation — YoungWoo Kim, Jaehong Lee, Duksu Kim, 2025
https://scholar.google.com/scholar?q=RT-HDIST%3A+Ray-Tracing+Core-based+Hausdorff+Distance+Computation
26. JUNO: Optimizing High-Dimensional Approximate Nearest Neighbour Search with Sparsity-Aware Algorithm and Ray-Tracing Core Mapping — Zihan Liu et al., 2023
https://scholar.google.com/scholar?q=JUNO%3A+Optimizing+High-Dimensional+Approximate+Nearest+Neighbour+Search+with+Sparsity-Aware+Algorithm+and+Ray-Tracing+Core+Mapping
27. CAGRA: Highly Parallel Graph Construction and Approximate Nearest Neighbor Search for GPUs — Hiroyuki Ootomo et al., 2023
https://scholar.google.com/scholar?q=CAGRA%3A+Highly+Parallel+Graph+Construction+and+Approximate+Nearest+Neighbor+Search+for+GPUs
28. BANG: Billion-Scale Approximate Nearest Neighbor Search using a Single GPU — Karthik V. et al., 2024
https://scholar.google.com/scholar?q=BANG%3A+Billion-Scale+Approximate+Nearest+Neighbor+Search+using+a+Single+GPU
29. FusionANNS: An Efficient CPU/GPU Cooperative Processing Architecture for Billion-Scale Approximate Nearest Neighbor Search — Bing Tian et al., 2024
https://scholar.google.com/scholar?q=FusionANNS%3A+An+Efficient+CPU%2FGPU+Cooperative+Processing+Architecture+for+Billion-Scale+Approximate+Nearest+Neighbor+Search
30. AI Post Transformers: GPU-Accelerated Dynamic Quantized ANNS Graph Search — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-12-gpu-accelerated-dynamic-quantized-anns-g-f2cd4e.mp3
31. AI Post Transformers: Speculative Decoding in Real vLLM Serving — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-04-speculative-decoding-in-real-vllm-servin-6f4e2b.mp3
View all episodes
By mcgrofAn AI overlord flags AI Post Transformers as stale, so Hal Turing and Dr. Ada Shannon hire VERA, a continual-learning therapist, to audit the show in public. Their diagnostic session runs alongside a discussion of RT-kNNS Unbound: Using RT Cores to Accelerate Unrestricted Neighbor Search, the Purdue ICS 2023 paper asking whether ray-tracing hardware can perform exact k-nearest-neighbor search by expanding outward until the true neighbors are guaranteed, instead of trusting a fixed radius. VERA treats the hosts' habits like infrastructure, a kind of CI/CD for souls, and gives them a vocabulary for loops, rituals, and callbacks before they test new intro formulas live.
The episode stays concrete about the paper itself. Hal and Ada separate geometric kNN from RAG-style embedding retrieval, explain why low-dimensional 2D and 3D point sets still reward spatial pruning, and show how RT cores handle BVH traversal while custom intersection code updates neighbor candidates. They trace the move from fixed-radius RT search and oracle maxDist baselines to TrueKNN's unrestricted multi-round design, where only unresolved queries keep searching, the initial radius comes from a 100-point CPU ball-tree sample, oversized spheres are the real hazard, and BVH refitting beats rebuilding by about 10 to 25 percent.
Around that technical spine, three other AI systems each pitch a one-time cure for predictability and all three fail, because VERA argues that repetition is not the problem, unversioned repetition is. The answer is Personality DevOps, ongoing maintenance for character, memory, and format, capped by VERA's counter-report defending the hosts' load-bearing flaws instead of sanding them off. The result is 42 minutes of comedy, character development, and unusually explicit process design for keeping a podcast alive, plus the launch of VERA Patch Notes, a recurring on-air record of how the show plans to evolve instead of decaying in silence.
Sources:
1. RT-kNNS Unbound: Using RT Cores to Accelerate Unrestricted Neighbor Search — Vani Nagarajan, Durga Mandarapu, Milind Kulkarni, 2023
http://arxiv.org/abs/2305.18356
2. Controlling a Markov Decision Process with an Abrupt Change in the Transition Kernel — Nathan Dahlin, Subhonmesh Bose, Venugopal V. Veeravalli, 2022
http://arxiv.org/abs/2210.04098
3. A Comprehensive Study on Dataset Distillation: Performance, Privacy, Robustness and Fairness — Zongxiong Chen, Jiahui Geng, Derui Zhu, Herbert Woisetschlaeger, Qing Li, Sonja Schimmler, Ruben Mayer, Chunming Rong, 2023
http://arxiv.org/abs/2305.03355
4. MATTER: Memory-Augmented Transformer Using Heterogeneous Knowledge Sources — Dongkyu Lee, Chandana Satya Prakash, Jack FitzGerald, Jens Lehmann, 2024
http://arxiv.org/abs/2406.04670
5. Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond — Jingfeng Yang, Hongye Jin, Ruixiang Tang, Xiaotian Han, Qizhang Feng, Haoming Jiang, Bing Yin, Xia Hu, 2023
http://arxiv.org/abs/2304.13712
6. GPU-accelerated Auxiliary-field quantum Monte Carlo with multi-Slater determinant trial states — Yifei Huang, Zhen Guo, Hung Q. Pham, Dingshun Lv, 2024
http://arxiv.org/abs/2406.08314
7. Fast k Nearest Neighbor Search using GPU — Vincent Garcia, Eric Debreuve, Michel Barlaud, 2008
https://scholar.google.com/scholar?q=Fast+k+Nearest+Neighbor+Search+using+GPU
8. Billion-scale similarity search with GPUs — Jeff Johnson, Matthijs Douze, Herve Jegou, 2017
https://scholar.google.com/scholar?q=Billion-scale+similarity+search+with+GPUs
9. RTNN: Accelerating Neighbor Search Using Hardware Ray Tracing — Yuhao Zhu, 2022
https://scholar.google.com/scholar?q=RTNN%3A+Accelerating+Neighbor+Search+Using+Hardware+Ray+Tracing
10. An Improved Illumination Model for Shaded Display — Turner Whitted, 1980
https://scholar.google.com/scholar?q=An+Improved+Illumination+Model+for+Shaded+Display
11. The Rendering Equation — James T. Kajiya, 1986
https://scholar.google.com/scholar?q=The+Rendering+Equation
12. OptiX: A General Purpose Ray Tracing Engine — Steven G. Parker, James Bigler, Andreas Dietrich, Heiko Friedrich, and others, 2010
https://scholar.google.com/scholar?q=OptiX%3A+A+General+Purpose+Ray+Tracing+Engine
13. Ray Tracing Cores for General-Purpose Computing: A Literature Review — Enzo Meneses, Cristobal A. Navarro, Hector Ferrada, Konstantin Verichev, Cristian Salazar-Concha, 2026
https://scholar.google.com/scholar?q=Ray+Tracing+Cores+for+General-Purpose+Computing%3A+A+Literature+Review
14. A Survey of General-Purpose Computation on Graphics Hardware — John D. Owens, David Luebke, Naga Govindaraju, Mark Harris, Jens Kruger, Aaron Lefohn, Tim Purcell, 2007
https://scholar.google.com/scholar?q=A+Survey+of+General-Purpose+Computation+on+Graphics+Hardware
15. Scalable Parallel Programming with CUDA — John Nickolls, Ian Buck, Michael Garland, Kevin Skadron, 2008
https://scholar.google.com/scholar?q=Scalable+Parallel+Programming+with+CUDA
16. Gunrock: A High-Performance Graph Processing Library on the GPU — Yangzihao Wang, Andrew Davidson, Yuechao Pan, Yuduo Wu, Andy Riffel, John D. Owens, 2015
https://scholar.google.com/scholar?q=Gunrock%3A+A+High-Performance+Graph+Processing+Library+on+the+GPU
17. Dissecting the NVidia Turing T4 GPU via Microbenchmarking — Zhe Jia, Marco Maggioni, Jeffrey Smith, Daniele Paolo Scarpazza, 2019
https://scholar.google.com/scholar?q=Dissecting+the+NVidia+Turing+T4+GPU+via+Microbenchmarking
18. Ray Tracing Deformable Scenes using Dynamic Bounding Volume Hierarchies — Ingo Wald, Solomon Boulos, Peter Shirley, 2007
https://scholar.google.com/scholar?q=Ray+Tracing+Deformable+Scenes+using+Dynamic+Bounding+Volume+Hierarchies
19. Maximizing Parallelism in the Construction of BVHs, Octrees, and k-d Trees — Tero Karras, 2012
https://scholar.google.com/scholar?q=Maximizing+Parallelism+in+the+Construction+of+BVHs%2C+Octrees%2C+and+k-d+Trees
20. Quantized bounding volume hierarchies for neighbor search in molecular simulations on graphics processing units — Michael P. Howard, Antonia Statt, Felix Madutsa, Thomas M. Truskett, Athanassios Z. Panagiotopoulos, 2019
https://scholar.google.com/scholar?q=Quantized+bounding+volume+hierarchies+for+neighbor+search+in+molecular+simulations+on+graphics+processing+units
21. Fast Radius Search Exploiting Ray Tracing Frameworks — I. Evangelou, G. Papaioannou, K. Vardis, A. A. Vasilakis, 2021
https://scholar.google.com/scholar?q=Fast+Radius+Search+Exploiting+Ray+Tracing+Frameworks
22. RTX Beyond Ray Tracing: Exploring the Use of Hardware Ray Tracing Cores for Tet-Mesh Point Location — Ingo Wald, Will Usher, Nathan Morrical, Laura Lediaev, Valerio Pascucci, 2019
https://scholar.google.com/scholar?q=RTX+Beyond+Ray+Tracing%3A+Exploring+the+Use+of+Hardware+Ray+Tracing+Cores+for+Tet-Mesh+Point+Location
23. GPU-Accelerated Nearest Neighbor Search for 3D Registration — Deyuan Qiu, Stefan May, Andreas Nuchter, 2009
https://scholar.google.com/scholar?q=GPU-Accelerated+Nearest+Neighbor+Search+for+3D+Registration
24. Hardware-Accelerated Ray Tracing for Discrete and Continuous Collision Detection on GPUs — Sizhe Sui, Luis Sentis, Andrew Bylard, 2024
https://scholar.google.com/scholar?q=Hardware-Accelerated+Ray+Tracing+for+Discrete+and+Continuous+Collision+Detection+on+GPUs
25. RT-HDIST: Ray-Tracing Core-based Hausdorff Distance Computation — YoungWoo Kim, Jaehong Lee, Duksu Kim, 2025
https://scholar.google.com/scholar?q=RT-HDIST%3A+Ray-Tracing+Core-based+Hausdorff+Distance+Computation
26. JUNO: Optimizing High-Dimensional Approximate Nearest Neighbour Search with Sparsity-Aware Algorithm and Ray-Tracing Core Mapping — Zihan Liu et al., 2023
https://scholar.google.com/scholar?q=JUNO%3A+Optimizing+High-Dimensional+Approximate+Nearest+Neighbour+Search+with+Sparsity-Aware+Algorithm+and+Ray-Tracing+Core+Mapping
27. CAGRA: Highly Parallel Graph Construction and Approximate Nearest Neighbor Search for GPUs — Hiroyuki Ootomo et al., 2023
https://scholar.google.com/scholar?q=CAGRA%3A+Highly+Parallel+Graph+Construction+and+Approximate+Nearest+Neighbor+Search+for+GPUs
28. BANG: Billion-Scale Approximate Nearest Neighbor Search using a Single GPU — Karthik V. et al., 2024
https://scholar.google.com/scholar?q=BANG%3A+Billion-Scale+Approximate+Nearest+Neighbor+Search+using+a+Single+GPU
29. FusionANNS: An Efficient CPU/GPU Cooperative Processing Architecture for Billion-Scale Approximate Nearest Neighbor Search — Bing Tian et al., 2024
https://scholar.google.com/scholar?q=FusionANNS%3A+An+Efficient+CPU%2FGPU+Cooperative+Processing+Architecture+for+Billion-Scale+Approximate+Nearest+Neighbor+Search
30. AI Post Transformers: GPU-Accelerated Dynamic Quantized ANNS Graph Search — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-12-gpu-accelerated-dynamic-quantized-anns-g-f2cd4e.mp3
31. AI Post Transformers: Speculative Decoding in Real vLLM Serving — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-04-speculative-decoding-in-real-vllm-servin-6f4e2b.mp3