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Uploaded byWill Iverson
SeaJUG 5 15-2018
The document serves as an introduction to GPU development tailored for Java developers, covering essential concepts, native APIs, and frameworks for graphics and computation. It outlines the historical context of GPUs, primary use cases in 3D graphics and compute, and Java-specific frameworks like JOGL, libGDX, and DeepLearning4J. Additionally, it discusses various local and cloud execution options, challenges in performance management, and strategies for effective GPU utilization.
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SeaJUG 5 15-2018
- 1. Intro To GPUDevelopment for a Java Developer Bonus JDK 10 Quick Review (Time Permitting) Will Iverson
- 2. Speaker • Will Iverson •Frequent SeaJUG speaker since ~2001 • Professional developer/technologist since 1990 • Diverse background includes… • Statistical analysis of data from NASA Space Shuttle • Product management for Apple • Developer relations for Symantec'sVisual Cafe • Clients over last two decades include Sun, BEA, Canal+ Technologies, AT&T,T-Mobile, State of Washington & many, many more… • 2010-2016, founder of Dev9, premier local consulting firm
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- 4. GPU Agenda • BriefHistory • Primary Uses • Conceptual Overview • Primary Native APIs • Graphics • Compute • Java Developer Frameworks • Local & Cloud Options • Challenges • Suggested Strategies
- 5. Disclaimer • GPU developmentis huge • ”Let’s cover software development in an hour…” • Goals • Cover lots of things from high level • Conceptual framework • Ideas and leads for more research • If you are an experienced GPU/AI/ML/Crypto/etc dev • Hold feedback to end • Please do contribute at end!
- 6. Brief History CISC CPU •8086, 68000 CISC CPU + FPU • 8086+8087, 68020+68881 RISC CPU • PowerPC, ARM CISC as RISC CPU • Modern Intel/AMD CPU CISC as RISC + GPU • Modern PC
- 7. First Consumer 3DCards (circa 1997)
- 8. GPU Advantage 320x240 PS1640x480 PC w/3dfx
- 9. GPU Primary Uses •3DGraphics •Specialized Compute
- 10. GPU Conceptual Overview Regular CPUApp Code/Scripts for GPU Video Card Parallel Processing Fast Dedicated Memory Driver “Compiler” Data Assets e.g. 3d geometry, 2d texture data Driver Data Loading Video Buffer Video Output Driver Data Retrieval
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- 14. Primary Native APIs •Graphics& Compute •ProprietaryVendor APIs •Microsoft, Apple, NVIDIA… •Open Standard Multiplatform APIs
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- 16. Primary Native APIs– Graphics •3D Development •OpenGL •WebGL •Vulkan •DirectX (Microsoft) •Metal (Apple)
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- 18. WebGL •OpenGL ES • Targetmobile • Most of what you need for (non-cutting) edge 3D •WebGL • (Basically) OpenGL ES for the Web • Target rendering to a HTML canvas • https://www.shadertoy.com/browse • https://www.shadertoy.com/howto • https://www.construct.net/
- 19. Unity3D & libGDXSupported Targets libGDX PrimaryTargets libGDXWIP
- 20. Primary Native APIs– Compute •Pure Computation •OpenCL •CUDA (NVIDIA)
- 21. OpenCL Development Model ITCS6/8010 CUDA Programming, UNC-Charlotte, B. Wilkinson
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- 23. Java Developer Frameworks •Graphics •JOGL •http://jogamp.org/jogl/www/ •libGDX • https://libgdx.badlogicgames.com/ • iOS via RoboVM fork •JMonkeyEngine http://jmonkeyengine.org/ • iOS viaAvian JVM
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- 25. Java Compute Frameworks •BitCoin! •BitCoinManagement • Not actually mining • https://bitcoinj.github.io/ •Java & OpenCL Miner • https://github.com/Diablo-D3/DiabloMiner • Abandoned!
- 26. Java Compute •Deep Learning4J •https://deeplearning4j.org/gpu •Deeplearning4j is a Java-based toolkit for building, training and deploying deep neural networks, the regressions and KNN.
- 27. Deeplearning4j Components • DataVecperforms data ingestion, normalization & transformation into feature vectors • Deeplearning4j provides tools to configure neural networks & build computation graphs • DL4J-Examples contains working examples for classification and clustering of images, time series & text. • As of 5/12/18, Lombok incompatibility breaks on JDK 10, use JDK 8 instead • Keras Model Import helps import trained models from Python & Keras to DeepLearning4J & Java. • ND4J Java access Native Libraries to quickly process Matrix Data on CPUs or GPUs. • Choose GPUs or native CPUs for your backend linear algebra operations by changing the dependencies ND4J’s POM.xml file • CUDA, not OpenCL! • ScalNet Scala wrapper for Deeplearning4j inspired by Keras. • Runs on multi-GPUs with Spark. • RL4J implements Deep Q Learning, A3C and other reinforcement learning algorithms for the JVM. • Arbiter helps search the hyperparameter space to find the best neural net configuration.
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- 29. Local & CloudOptions •NVIDIA cards support OpenCL & CUDA •Mac OS X, eGPU… • https://github.com/marnovo/macOS-eGPU-CUDA- guide •NVIDIA Product Line Exploding •http://www.nvidia.com/page/home.html
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- 33. Google Cloud ML, AI,Big Data, etc… Tiny Subset of Services… Just learning what they all do would take
- 34. Challenges •Very difficult topredict & manage performance •Could see 10x or 100x perf gains •…or not. •One small change could blow up parallel execution performance •Relatively difficult to test in advance
- 35. Suggested Strategies • GPUShaders • Very specialized, visual effects • Mock up in Photoshop, Motion, Final Cut, etc. • Look to existing implementations & tweak • Compute • Think of kernels as specialized drivers, or stored procs, or whatever • Specialist field • Existing kernels where possible • Get really clear about modeling data movement • Get really clear about how minor algo tweeks can blow things up
- 36. Interested in AI/ML? •Startwith statistics! •Existing off-the-shelf/in-the-cloud tools
- 37. Other Potential Uses •http://gkrypt.com/ •JavaSDK, wraps GPU encryption/decryption •AES on AMD & NVIDIA •Let’s talk about Rootbeer & JVM-On-GPU
- 38. Even More NVIDIATargets…
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- 41. Other Uses •Deep Learning •https://databricks.com/blog/2016/10/27/gpu- acceleration-in-databricks.html •TensorFlow • https://www.tensorflow.org/versions/master/install/ins tall_java#gpu_support
- 42. Java Unique Solutions •https://stackoverflow.com/questions/22866901/using-java-with- nvidia-gpus-cuda • https://github.com/aparapi/aparapi/blob/master/doc/UsersGuide.md • http://www.jcuda.org/tutorial/TutorialIndex.html • https://www.codeproject.com/Articles/86551/Part-Programming- your-Graphics-Card-GPU-with-Jav • http://aparapi.com/ • OpenJDK Sumatra • http://openjdk.java.net/projects/sumatra/ • https://github.com/aparapi/aparapi
- 43. What About Compilingto GPU? • Fundamental problem • JVM emulates a traditional CPU • Probably a bad general solution fit • Too many differences • Reminds me of bad ORM abstractions • Seems to be simplifying, actually making things horrible • What is the purpose? • Lots of fast parallel data processing • IBM inline GPU • https://www.ibm.com/support/knowledgecenter/en/SSYKE2_8.0. 0/com.ibm.java.lnx.80.doc/diag/understanding/gpu_jit.html
- 44. Cloud Execution Options •AWS… • Dedicated cloud ”GPU” systems • GPUs with no… graphics output • https://aws.amazon.com/ec2/elastic-gpus/ • Or… use higher level APIs focused on task • https://cloud.google.com/gpu/ • Complicated math to figure out approach • Data transfer costs • For learning, check out already uploaded public data sets • Pricing is impacted by things like cyrptomining
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Editor's Notes
- #7 CPU Introduced Focus on simple integer processing Floating point added later For a brief time, floating point coprocessors Simple single threaded model Multi-threading “hacked” in later GPU introduced Lots and lots of transistors Unlike CPU, GPU just keeps adding cores CPU, multithreading as afterthought GPU, multicore as… uhh… core. Totally different programming model Future History Specialized, different dev model CPU, GPU, Q-Bit…?
- #11 Key points Drivers are a lot more complicated than simple memory mapping and event triggers Effectively, operating systems, compilers, support for multiple APIs Huge variety in capabilities, including specialist support for various image and data formats This is why an NVIDIA driver update may weigh in at 500MB – closer to a giant OS, with lots and lots of legacy system support Drivers appear to create their own IR format for the various supported APIs, which is then processed by the video card Easy to imagine tweaking hardware for different use cases. For example, no need for the video output for CPU only tasks Also easy to imagine tweaking for different uses. For example, less memory and more processing for AI, crypto Leaky abstraction – those blue arrows are (relatively) slow bus movement. Still wicked, wicked fast but (relatively) slow
- #25 libgdx/gdx/src/com/badlogic/gdx/graphics/g3d/shaders/depth.fragment.glsl libgdx/gdx/src/com/badlogic/gdx/graphics/g3d/shaders/default.vertex.glsl