IN-SITU VIS IN THE AGE OF HPC+AI Peter Messmer, WOIV – ISC 2018, Frankfurt, 6/28/18 EXCITING TIMES FOR VISUALIZATION Huge dataset everywhere Novel workflows for HPC * AI Rendering technology leap 2 DEEP LEARNING COMES TO HPC Accelerates Scientific Discovery UIUC & NCSA: ASTROPHYSICS U. FLORIDA & UNC: DRUG DISCOVERY LBNL: High Energy Particle PHYSICS 5,000X LIGO Signal Processing 300,000X Molecular Energetics Prediction 100,000X faster simulated output by GAN PRINCETON & ITER: CLEAN ENERGY U.S. DoE: PARTICLE PHYSICS Univ. of Illinois: DRUG DISCOVERY 50% Higher Accuracy for Fusion Sustainment 33% More Accurate Neutrino Detection 2X Accuracy for Protein Structure Prediction 3 The most exciting phrase to hear in science, the one that heralds new discoveries, is not “Eureka!” but “That’s funny …” — Isaac Asimov 4 ANOMALY DETECTION Look at all the data and learn from it! Vast amount of data produced by simulations and experiments I/O limitations force throw-away Human in the loop infeasible “In-situ vis 2.0” Dataset: Visualization of historic cyclones from JWTC hurricane report from 1979 to 2016 Kim et al, 2017) Run inference on the simulation data to detect anomalies 5 Deep Learning for Climate Modeling ANOMALY DETECTION IN CLIMATE DATA Identifying “extreme” weather events in multi-decadal datasets with 5-layered Convolutional Neural Network. Reaching 99.98% of detection accuracy. (Kim et al, 2017) Dataset: Visualization of historic cyclones from JWTC hurricane report from 1979 to 2016 MNIST structure Systemic framework for detection and localization of extreme climate event DOI 10.1109/ICDMW.2017.476 OPTIMIZING PARAMETERIZED MODELS Learning from simulations to accelerate simulations Heuristics and semi-empirical models Examples: throughout simulation codes - Parameterized models: Often expensive, complex control flow - Atmospheric radiation transport, Use trained approximate models instead collisional cross-sections, chemical reaction chains, … Trained with past simulations - Simulation parameters - Grid refinement, load balancing, scheduling, … Use simulations to train approximate models 7 HPC SIMULATION OF THE FUTURE Concurrent simulation, training, inference Leads to - Improved approximate models - Better utilization of data - Faster results Coupling of HPC and DL software stack is needed 8 DATA SCIENCE DRIVES SOFTWARE-STACK Data science mission-critical to non-traditional HPC organizations Deep learning, graph analytics, in-core databases,.. Sustainability and performance by scale Frameworks supported by big corps, large communities Big market, big support by all vendors Economics drive performance portability and sustainability Happened in the past, but different situation today Trilinos: 274 stars, PyTorch: 16’615 stars Cast HPC algorithms in DL terms 9 EXAMPLE: WAVE EQUATION VIA CONV NEURAL NETWORK Applying stencils = Inference in Conv Network layer { name : “laplace_u” type : “convolution” bottom : “u_0” top : “laplace_u” u convolution_param { 0 num_output : 1 푑푣 kernel_size : 3 = 푐 ∆ 푢 stride : 1 푑푡 pad : 1 .. 푑푢 } = 푣 Du0 =ux-1–2u+ux+1 푑푡 layer { name : “velocity_update” type : “Eltwise” V3/2 = c dt Du0 + v1/2 bottom : “laplace_u” top : “velocity_update” } u1 = dt v1/2 + u0 layer { name : “position_update” type : “Eltwise” Simplified 1D example cartoon bottom : “velocity_update” Approach works for higher dimensions top : “position_update” } Other examples: Stencils, Spectral transforms, spectral elements, .. 10 EXAMPLE: MARCHING CUBES (well.. squares) Input Data Threshold Interpol Interpol X Y Label Cells Select Interpols 11 HPC CAN CONTRIBUTE TO EMERGING DATA SCIENCE NEEDS HPC solved lots of “new” problems in the past DL will need distributed memory parallelism New challenges for DL algorithms HPC has probably hit those challenges in the past https://www.hpcwire.com/2017/02 Better implementation, better algorithms /21/hpc-technique-benefits-deep- learning/ Collaborate with DL framework developers, contribute to DL frameworks First step: speak a common language 12 RENDERING TECHNOLOGIES 13 VISUALIZATION IN THE DATACENTER Benefits of Rendering on Supercomputer Scale with Simulation Cheaper Infrastructure Interactive High-Fidelity Rendering No Need to Scale Separate Vis Cluster All Heavy Lifting Performed on the Server Improves Perception and Scientific Insight 14 VISUALIZATION REQUIREMENTS Accuracy Responsiveness Visualization needs Interpretability 15 REAL-TIME RAY-TRACING Enabled by Volta V100 OptiX Ray-Tracing SDK State-of-the-art performance: 500M+ rays/sec Algorithm and hardware agnostic Shaders with single-ray programming model, recursion Free for private and commercial use https://developer.nvidia.com/optix 16 OptiX 5 with the CNN-based AI Denoiser 157x 54x 7x 1x 4x Pascal GP100 Volta V100 Volta V100 DGX Station CPU OptiX 4.1 OptiX 5 OptiX 5 W/ AI OptiX 5 W/ AI C.A. Chaitanya, A. Kaplanyan, C. Schied, M. Salvi, A. Lefohn, D. Nowrouzezahrai, T. Aila. “Interactive Reconstruction of Monte Carlo Image Sequences Using a Recurrent Denoising Autoencoder.” SIGGRAPH 2017 17 OPTIX FOR SCIENCE Ray-Tracing for extra visual cues Enhanced visual perception Single workflow for science and outreach Fast rendering with AI denoiser Integrated into leading vis tools With Denoiser 18 WILL HPC TRANSFORM AI OR WILL AI TRANSFORM HPC? Combination of HPC and AI will lead to better exploitation of data, faster models Both communities need each other - AI will need distributed memory technologies at one point - HPC needs AI for analysis and parameterized models Economic interest in AI drives software and hardware development Expressing HPC algorithms in AI terms helps leveraging AI features Applying HPC techniques to scale AI => Continue with joint events! Simulation and streamline data courtesy of Christoph Garth19 OptiX AI Denoiser in ParaView Without Denoiser With Denoiser 20 NVIDIA IndeX SDK Large scale and distributed data rendering Scene management with volume data Transparent support for NVLink Higher-order filtering, advanced lighting & transfer functions https://developer.nvidia.com/index 21 ParaView Plugin NVDIA IndeX ParaView Plugin Distributed with ParaView (pvNvidiaIndeX) Mixing with ParaView primitives Support for multiple slice planes Structured and unstructured meshes In-situ capable via Catalyst https://developer.nvidia.com/index 22 NVIDIA INDEX PARAVIEW PLUGIN Growing user base • Single GPU version bundled with ParaView • Over 20,000 downloads in 2 months. • Cluster version • 10+ labs and institutions actively working with us. https://www.nvidia.com/en-us/data-center/index-paraview-plugin/ 23 NVIDIA INDEX PARAVIEW PLUGIN Licensing and Availability SINGLE GPU ACADEMIC COMMERCIAL Free license Free license License fee Supports single GPU Multi-node Multi-GPU Multi-node Multi-GPU Plugin binaries part of Plugin source part of Plugin source with ParaView v5.5 download ParaView v5.5 download customer specific features New releases synced with Latest features in Premium support ParaView release cycle ParaView-Master 24 FLEXIBLE GPU ACCELERATION ARCHITECTURE Independent CUDA Cores & Video Engines Decode HW* Encode HW* Formats: Formats: • MPEG-2 CPU • VC1 • H.264 • VP8 • H.265 • VP9 • Lossless • H.264 • H.265 Bit depth: • Lossless • 8 bit NVDEC Buffer NVENC • 10 bit Bit depth: • 8 bit Color** • 10/12 bit • YUV 4:4:4 • YUV 4:2:0 Color** • YUV 4:2:0 CUDA Cores Resolution • Up to 8K*** Resolution • Up to 8K*** * See support diagram for previous NVIDIA HW generations ** 4:2:2 is not natively supported on HW 25 *** Support is codec dependent VIDEO CODEC SDK Hardware Accelerated Video Encoding/Decoding Encode and Decode API Industry-standard codecs: H.265(HEVC), H.264(AVCHD), VP9, VP8, VC-1 & MPEG-2 Supports up to 8K x 8K encoding Lossy & lossless compression https://developer.nvidia.com/nvidia-video-codec-sdk 26 Streaming of Large Tile Counts Frame rates / Latency / Bandwidth CASE STUDY Synchronization Comparison against CPU-based Compressors Strong Scaling (Direct-Send Sort-First Compositing) Hardware-Accelerated Multi-Tile Streaming for Realtime Remote Visualization, Biedert et al, EGPGV 2018, https://doi.org/10.2312/pgv.20181093 27 CONCEPTUAL OVERVIEW Asynchronous Pipelines 28 BENCHMARK SCENES NASA Synthesis 4K Space Orbit Ice Streamlines Low Complexity Medium Complexity High Complexity Extreme Complexity 29 HARDWARE LATENCY Decreases with Resolution 16 14 12 ] s 10 m [ y c 8 n e t a L 6 4 2 0 3840x2160 2720x1530 1920x1080 1360x766 960x540 672x378 480x270 Resolution Encode (HEVC) Encode (H.264) Decode (HEVC) Decode (H.264) 30 N:N STREAMING Pipeline Latencies Server: Piz Daint 45 Client: Piz Daint 40 Ice 4K 35 H.264: 32 Mbps 30 ] s m MPI-based [ 25 synchronization y c n e 20 t a L 15 10 5 0 1 2 4 8 16 32 64 128 256 Tiles Synchronize (Servers) Encode Network Decode Synchronize (Clients) 31 N:N STREAMING Client-Side Frame Rates and Bandwidths Server: Piz Daint 90 900 Client: Piz Daint 80 800 Ice 4K 70 700 ] s H.264: 32 Mbps ] / z 60 600 B HEVC: 16 Mbps H [ M [ e t 50 500 a h t R d i e 40 400 w m d a n r a F 30 300 B 20 200 10 100 0 0 1 2 4 8 16 32 64 128 256 Tiles HEVC (Bandwidth) H.264 (Bandwidth) HEVC (Frame Rate) H.264 (Frame Rate) 32 N:1 STREAMING Client-Side Frame Rate Server: Piz Daint 90 Clients: Site A (5 ms) Site B (25 ms) 80 Ice 4K 70 ] z 60 H.264: 32 Mbps H [ e t 50 a R e 40 m a r F 30 20 10 0 1 2 4 8 16 32 64 128 256 Tiles Site A (1x GP100) Site B (1x GP100) Site B (2x GP100) 33 N:1 STRONG SCALING Client-Side Frame Rate Server: Piz Daint 400 Clients: Site A (5 ms) Site B (25 ms) 350 Ice 4K 300 ] z H.264: 32 Mbps H 250