Cuda meetup presentation 5
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CUDA meetup January 2015 presentation
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Cuda meetup presentation 5
- 1. CUDA PARELLEL PROGRAMMING AND GAMING MEETUP RIGA, LATVIA, JANUARY 12, 2015
- 3. • Personal Introductions; • CES Nvidia news; • Jetson TK1; • Anouncements; • Summary Agenda
- 4. Who am I What I am doing What is my parallel programming experience [CUDA] experience Introduction
- 5. Exponential Technologies Drones AUTOMOTIVE 3D Printing Artificial intelligence Biotechnology Design ROBOTICS ComputerVision
- 6. CES 2015
- 8. Parrot drone + Jetson TK1
- 12. Featured events
- 14. The Startup University Latvia Tech and Entrepreneurship meetup network
- 15. Riga BioTechnology Meetup UI/UX Riga Meetup Riga Drone Meetup Riga Mobile App Developer Meetup Kick-off Meetup December 12, THE Mill December Meetup December 15, THE Mill Drone Kick-off Meetup December 16, THE Mill Kick-off Meetup December 17, THE Mill
- 16. Riga Startup: Idea to IPO CUDA parallel programming and gaming meetup Riga 3D Printing Riga Meetup Bitcoin and Cryptocurrencies Meetup Find you Co-founder December 18, THE Mill December CUDA Meetup December 22, THE Mill January 3D Printing January 8, RTU Design Factory JanuARY Meetup jANUARy 26, THE Mill
- 17. Sponsors
- 18. Technology and Entrepreneurship education
- 19. Paralell computing Research Center CUDA LABORATORY
- 20. Jetson TK1
- 25. Whats in the Box
- 27. Login Credentials Username: ubuntu Password: ubuntu
- 28. Install the NVIDIA Linux driver binary release on your target located in: ${HOME}/NVIDIA-INSTALLER Step 1) Change directories into the NVIDIA installation directory: cd ${HOME}/NVIDIA-INSTALLER Step 2) Run the installer script to extract and install the Linux driver binary release: sudo ./installer.sh Step 3) Reboot the system to have the graphical desktop UI come up.
- 29. CUDA SDK Demo Samples • Particles • Nbody • Smokeparticles • waves
- 37. Jetson TEGRA TK1 Tegra K1 SOC • Kepler GPU with 192 CUDA cores • 4-Plus-1 quad-core ARM Cortex A15 CPU • 2 GB x16 memory with 64 bit width • 16 GB 4.51 eMMC memory • 1 Half mini-PCIE slot • 1 Full size SD/MMC connector • 1 Full-size HDMI port • 1 USB 2.0 port, micro AB • 1 USB 3.0 port, A • 1 RS232 serial port • 1 ALC5639 Realtek Audio codec with Mic in and Line out • 1 RTL8111GS Realtek GigE LAN • 1 SATA data port • SPI 4MByte boot flash
- 39. • IT industryexperiencesanincreasinggrowth for displaysurfaceswith high resolution • Usecasesfor suchsurfacesincludesatellite and map data,x-rayand microscopeimages,multimedia,CCTV,etc. • Existing solutions arenot scalable, do notoffer hardwareabstraction,suffer fromwiring limitations Proposed Virtual Machine Based Monitor Wall Architecture Introduction Scalability Conclusions • The current experiments show that this architecture is very feasible for non FPS intensiveusecases where the displaywall can bedriven byasingle physicalGPU • The total resolution provided by this architecture even using the currently available compression technology greatly exceeds the resolutions of existing solutions, it would be expected for the resolutionto grow inthe future • The architecture itself scales very good, it is limited mainly by OS support for multiple monitors (this can be overcome by simulating a single high resolution display in the virtual machine that spans the whole resolution of the physical wall) and the possibility to stack multiple GPU’sin thehostsystem • Future work should focus on the ability to virtualize OpenGL and Direct3Dto removethe advantages ofnon-virtualized architectures OS GPU GPU Monit or Monit or Monit or Monit or OS GPU Monit or Monit or Monit or Monit or Splitter / Scaler Currentlythere aretwo mainalternatives to beused asthe H.264encoderin this architecture –Intel Quick Syncand NVENC.NVCENCis morefeasiblebecause: • Thetotal encoding powercanbeincreasedbystacking up multiple GPUsthat supportNVENCwithout penalties while notall Intel QuickSyncGPUshavebuilt in video memory so scaling thesecardsintroduceaperformancepenalty of using systemmemory • NVENCdoes notput anylimitations onother componentsofthe system,while Intel Quick Syncsupportsalimited amountofCPUs • Currentbenchmarksseemto showthat the overallFPS performancefor asingle GPU (whichisthe main criteriafor this architecture)is better for NVENCthan Intel QuickSync Why NVENC? Pro:OScannatively managethe displays Con: Powerconsumption,supportedmonitorcountlimited bythe output countof theGPUsand expansionslots for theGPUsonthe motherboard,deploymentislimited bywiring Pro:Softwarecomplexityisreducedsinceit doesnot haveto bemultiple monitor aware Con: SmallresolutionandDPI, visualization is notdisplayed in it’s nativeresolution Con: Expensive Currently Popular Monitor Wall Architectures Pro:Scalable,hostmachinecanrunmultiplevirtual machines,multiple virtualized GPU’s mapto physicalGPU’s to maximizeefficiency Pro:LANconnectionto thedisplaywall removeswire length limitations forcedbyDVI/HDMI cables Pro:Total resolutionof thewall goes beyondtheones that canbe achievedusing physical hardware Con: Lossycompression Con: NoDirect3D,OpenGLsupport • Thehostmachinecollects the framebuffer datafrom thevirtual machineGPUsand performsH.264 encodingof thevideo stream onthe physicalhost GPUthus thearchitectureheavilyreliesonafast hardwareH.264encoderallowing thehosted virtual machinesto fully usetheCPU • NonFPS intensiveusecasesallow agreatnumberof virtual monitors to behosted onasingle physicalGPU thus reducingthe power consumption 0 50 100 Using Video… Maximum Thegraphbelowdemonstratesthescalability possibilities in termsof possiblemaximalamountof connectedmonitors for thetraditional architectureversustheproposedoneonaQuadroK4000 cardthat has 4 outputs. Displaywallarchitecturewhereeachoutput of theGPU mapsto atileon thedisplaywall Displaywallarchitecturewhereeachoutput of theGPU issplit/upscaled among the tileson thedisplaywall Host Machine Virtual MachineG P U G P U G P U G P UG P U G P U G P U G P UG P U G P U G P U G P UG P U G P U G P U G P U H.264/RTP/LAN GPU Theproposed displaywallarchitecturewhereeachoutput of avirtualGPU mapsto a tileonthedisplaywallandistransmittedasaH.264streamoverLAN Virtualmachinebasedmonitor wallrunningGooglemapsinsideChromewebbrowser on16tilesat1920x1080pixelseachgivingatotal resolutionof 32megapixels Eachtilehasadedicated LANconnectionandH.264decoder Scalabilityof supported monitor count NVENC BasedH.264 Encoding forVirtual Machine BasedMonitorWall Architecture R.Bundulis(rudolfs.bundulis@lu.lv),G.Arnicans (guntis.arnicans@lu.lv), and R.Gailums (rihards.gailums@rhtu.edu.lv) UniversityofLatvia/RigaHighTechUniversity,Latvia
- 40. For Startups by Meetup members: $1800 per year of FREE Azure cloud services Free Microsoft software and tools
- 41. Latvijas Garantiju Aģentūra Government corporations which supports Latvian entepreneurs and helps in realisation of their business ideas.
- 43. Featured Startup
- 44. Speakers
- 45. Mobile APP Startup pitch
- 47. GPUCPU Add GPUs: Accelerate Science Applications © NVIDIA 2013
- 48. Small Changes, Big Speed-up Application Code + GPU CPU Use GPU to Parallelize Compute-Intensive Functions Rest of Sequential CPU Code © NVIDIA 2013
- 49. Fastest Performance on Scientific Applications Tesla K20X Speed-Up over Sandy Bridge CPUs CPU results: Dual socket E5-2687w, 3.10 GHz, GPU results: Dual socket E5-2687w + 2 Tesla K20X GPUs *MATLAB results comparing one i7-2600K CPU vs with Tesla K20 GPU Disclaimer: Non-NVIDIA implementations may not have been fully optimized 0.0x 5.0x 10.0x 15.0x 20.0x AMBER SPECFEM3D Chroma MATLAB (FFT)*Engineering Earth Science Physics Molecular Dynamics © NVIDIA 2013
- 50. Why Computing Perf/Watt Matters? Traditional CPUs are not economically feasible 2.3 PFlops 7000 homes 7.0 Megawatts 7.0 Megawatts CPU Optimized for Serial Tasks GPU Accelerator Optimized for Many Parallel Tasks 10x performance/socket > 5x energy efficiency Era of GPU-accelerated computing is here © NVIDIA 2013
- 51. World’s Fastest, Most Energy Efficient Accelerator Tesla K20X Tesla K20 Xeon CPU, E5-2690 Xeon Phi 225W 0.0 1.0 2.0 3.0 0.0 0.5 1.0 1.5 SGEMM(TFLOPS) DGEMM (TFLOPS) Tesla K20X vs Xeon CPU 8x Faster SGEMM 6x Faster DGEMM Tesla K20X vs Xeon Phi 90% Faster SGEMM 60% Faster DGEMM © NVIDIA 2013
- 52. Introduction to the CUDA Platform
- 53. CUDA Parallel Computing Platform Hardware Capabilities GPUDirectSMX Dynamic Parallelism HyperQ Programming Approaches Libraries “Drop-in” Acceleration Programming Languages OpenACC Directives Maximum Flexibility Easily Accelerate Apps Development Environment Nsight IDE Linux, Mac and Windows GPU Debugging and Profiling CUDA-GDB debugger NVIDIA Visual Profiler Open Compiler Tool Chain Enables compiling new languages to CUDA platform, and CUDA languages to other architectures www.nvidia.com/getcuda © NVIDIA 2013
- 54. Applications Libraries “Drop-in” Acceleration Programming Languages OpenACC Directives Easily Accelerate Applications 3 Ways to Accelerate Applications Maximum Flexibility © NVIDIA 2013
- 55. 3 Ways to Accelerate Applications Applications Libraries “Drop-in” Acceleration Programming Languages OpenACC Directives Maximum Flexibility Easily Accelerate Applications © NVIDIA 2013
- 56. Libraries: Easy, High-Quality Acceleration • Ease of use: Using libraries enables GPU acceleration without in-depth knowledge of GPU programming • “Drop-in”: Many GPU-accelerated libraries follow standard APIs, thus enabling acceleration with minimal code changes • Quality: Libraries offer high-quality implementations of functions encountered in a broad range of applications • Performance: NVIDIA libraries are tuned by experts © NVIDIA 2013
- 57. Some GPU-accelerated Libraries NVIDIA cuBLAS NVIDIA cuRAND NVIDIA cuSPARSE NVIDIA NPP Vector Signal Image Processing GPU Accelerated Linear Algebra Matrix Algebra on GPU and Multicore NVIDIA cuFFT C++ STL Features for CUDAIMSL Library Building-block Algorithms for CUDA ArrayFire Matrix Computations Sparse Linear Algebra © NVIDIA 2013
- 58. 3 Steps to CUDA-accelerated application • Step 1: Substitute library calls with equivalent CUDA library calls saxpy ( … ) cublasSaxpy ( … ) • Step 2: Manage data locality - with CUDA: cudaMalloc(), cudaMemcpy(), etc. - with CUBLAS: cublasAlloc(), cublasSetVector(), etc. • Step 3: Rebuild and link the CUDA-accelerated library nvcc myobj.o –l cublas © NVIDIA 2013
- 59. Explore the CUDA (Libraries) Ecosystem • CUDA Tools and Ecosystem described in detail on NVIDIA Developer Zone: developer.nvidia.com/cuda-tools-ecosystem © NVIDIA 2013
- 60. 3 Ways to Accelerate Applications Applications Libraries “Drop-in” Acceleration Programming Languages OpenACC Directives Maximum Flexibility Easily Accelerate Applications © NVIDIA 2013
- 61. OpenACC Directives © NVIDIA 2013 Program myscience ... serial code ... !$acc kernels do k = 1,n1 do i = 1,n2 ... parallel code ... enddo enddo !$acc end kernels ... End Program myscience CPU GPU Your original Fortran or C code Simple Compiler hints Compiler Parallelizes code Works on many-core GPUs & multicore CPUs OpenACC compiler Hint
- 62. • Easy: Directives are the easy path to accelerate compute intensive applications • Open: OpenACC is an open GPU directives standard, making GPU programming straightforward and portable across parallel and multi-core processors • Powerful: GPU Directives allow complete access to the massive parallel power of a GPU OpenACC The Standard for GPU Directives © NVIDIA 2013
- 63. Real-Time Object Detection Global Manufacturer of Navigation Systems Valuation of Stock Portfolios using Monte Carlo Global Technology Consulting Company Interaction of Solvents and Biomolecules University of Texas at San Antonio Directives: Easy & Powerful Optimizing code with directives is quite easy, especially compared to CPU threads or writing CUDA kernels. The most important thing is avoiding restructuring of existing code for production applications. ” -- Developer at the Global Manufacturer of Navigation Systems “ 5x in 40 Hours 2x in 4 Hours 5x in 8 Hours © NVIDIA 2013
- 64. Start Now with OpenACC Directives Free trial license to PGI Accelerator Tools for quick ramp www.nvidia.com/gpudirectives Sign up for a free trial of the directives compiler now! © NVIDIA 2013
- 65. 3 Ways to Accelerate Applications Applications Libraries “Drop-in” Acceleration Programming Languages OpenACC Directives Maximum Flexibility Easily Accelerate Applications © NVIDIA 2013
- 66. GPU Programming Languages OpenACC, CUDA FortranFortran OpenACC, CUDA CC Thrust, CUDA C++C++ PyCUDA, CopperheadPython Alea.cuBaseF# MATLAB, Mathematica, LabVIEWNumerical analytics © NVIDIA 2013
- 67. // generate 32M random numbers on host thrust::host_vector<int> h_vec(32 << 20); thrust::generate(h_vec.begin(), h_vec.end(), rand); // transfer data to device (GPU) thrust::device_vector<int> d_vec = h_vec; // sort data on device thrust::sort(d_vec.begin(), d_vec.end()); // transfer data back to host thrust::copy(d_vec.begin(), d_vec.end(), h_vec.begin()); Rapid Parallel C++ Development • Resembles C++ STL • High-level interface • Enhances developer productivity • Enables performance portability between GPUs and multicore CPUs • Flexible • CUDA, OpenMP, and TBB backends • Extensible and customizable • Integrates with existing software • Open source http://developer.nvidia.com/thrust or http://thrust.googlecode.com
- 68. MATLAB http://www.mathworks.com/discovery/ matlab-gpu.html Learn More These languages are supported on all CUDA-capable GPUs. You might already have a CUDA-capable GPU in your laptop or desktop PC! CUDA C/C++ http://developer.nvidia.com/cuda-toolkit Thrust C++ Template Library http://developer.nvidia.com/thrust CUDA Fortran http://developer.nvidia.com/cuda-toolkit GPU.NET http://tidepowerd.com PyCUDA (Python) http://mathema.tician.de/software/pycuda Mathematica http://www.wolfram.com/mathematica/new -in-8/cuda-and-opencl-support/ © NVIDIA 2013
- 69. Getting Started © NVIDIA 2013 • Download CUDA Toolkit & SDK: www.nvidia.com/getcuda • Nsight IDE (Eclipse or Visual Studio): www.nvidia.com/nsight • Programming Guide/Best Practices: • docs.nvidia.com • Questions: • NVIDIA Developer forums: devtalk.nvidia.com • Search or ask on: www.stackoverflow.com/tags/cuda • General: www.nvidia.com/cudazone
- 70. © NVIDIA 2013 https://www.youtube.com/watch?v=IzU4AVcMFys Intro to CUDA - An introduction, how-to, to NVIDIA's GPU parallel Mythbusters Demo GPU versus CPU: http://youtu.be/-P28LKWTzrI
- 74. Jetson TEGRA TK1 Tegra K1 SOC • Kepler GPU with 192 CUDA cores • 4-Plus-1 quad-core ARM Cortex A15 CPU • 2 GB x16 memory with 64 bit width • 16 GB 4.51 eMMC memory • 1 Half mini-PCIE slot • 1 Full size SD/MMC connector • 1 Full-size HDMI port • 1 USB 2.0 port, micro AB • 1 USB 3.0 port, A • 1 RS232 serial port • 1 ALC5639 Realtek Audio codec with Mic in and Line out • 1 RTL8111GS Realtek GigE LAN • 1 SATA data port • SPI 4MByte boot flash
- 75. NVIDIA GTX 750Ti • Nvidia MAXWELL technology • Cost – 170 USD • Only 60 W of power, no dedicated power connections • 250 MHash/sek Vs • Nvidia GTX 780 – 350 MHash/sek + Power cosumption • Nvidia TESLA K40 – 560 MHash/sek + Power cosumption
- 76. Latvian CUDA & parallel programming ecosystem Next meetups, frequency Speakers Topics Group marketing channels