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CUDA Parallel Programming on NVIDIA GPUs (HW and SW)

سرفصل های دوره

Performance Optimization and Analysis for High-Performance Computing


1 - Introduction to the Nvidia GPUs hardware
  • 1 - 01
  • 1 - 01-CPUs-and-GPUs.pptx
  • 1 - GPU vs CPU very important
  • 1 - Top500.txt
  • 2 - NVidias history How Nvidia started dominating the GPU sector
  • 3 - Architectures and Generations relationship Hopper Ampere GeForce and Tesla
  • 4 - A100 techpowerup.txt
  • 4 - How to know the Architecture and Generation
  • 4 - RTX 3090.txt
  • 5 - The difference between the GPU and the GPU Chip
  • 6 - The architectures and the corresponding chips
  • 7 - A100.txt
  • 7 - Nvidia GPU architectures From Fermi to hopper
  • 7 - RTX 3090.txt
  • 7 - The history of Nvidia.txt
  • 7 - V100.txt
  • 8 - Main-Parameters-to-evaluate-the-GPU-performance.pdf
  • 8 - Parameters required to compare between different Architectures
  • 9 - Half single and double precision operations
  • 10 - Compute capability and utilizations of the GPUs
  • 11 - Before reading any whitepapers look at this
  • 12 - VoltaAmperePascalSIMD Dont skip
  • 12 - research paper 01.txt
  • 12 - research paper 02.txt
  • 12 - research paper 03.txt

  • 2 - Installing Cuda and other programs
  • 13 - What features installed with the CUDA toolkit
  • 14 - Installing CUDA on Windows
  • 15 - Installing WSL to use Linux on windows OS
  • 16 - Installing Cuda toolkits on Linux

  • 3 - Introduction to CUDA programming
  • 17 - Mapping SW from CUDA to HW introducing CUDA
  • 17 - S3-01-Introduction-to-CUDA.pdf
  • 18 - 001 Hello World program threads Blocks
  • 18 - 4.txt
  • 18 - 64.txt
  • 18 - L2-cache-forums.txt
  • 18 - picture2.zip
  • 19 - Compiling Cuda on Linux
  • 20 - 002 Hello World program WarpIDs
  • 20 - picture1.zip
  • 20 - test02.txt
  • 21 - 003 Vector addition the Steps for any CUDA project
  • 22 - 004 Vector addition blocks and thread indexing GPU performance
  • 23 - 005 levels of parallelization Vector addition with Extralarge vectors

  • 4 - Profiling
  • 24 - Query the device properties using the Runtime APIs
  • 24 - S4-01-Quering-the-device-props.pdf
  • 25 - Nvidiasmi and its configurations Linux User
  • 25 - S4-02-nvidia-smi.pdf
  • 25 - thth.txt
  • 26 - S4-03-occupancy.pdf
  • 26 - The GPUs Occupancy and Latency hiding
  • 27 - Allocated active blocks per SM important
  • 27 - S4-04-Allocated-Active-Blocks-Per-SM.pdf
  • 28 - Starting with the nsight compute first issue
  • 29 - All profiling tools from NVidia Nsight systems compute nvprof
  • 30 - Error checking APIs look at chat GPU there is an example
  • 31 - Nsight Compute performance using command line analysis
  • 31 - S4-08-nsight-compute-CLI.pptx
  • 31 - The Documentation.txt
  • 31 - The Second Documentation.txt
  • 32 - Graphical Nsight Compute windows and linux
  • 32 - Graphic kernel profiling.txt

  • 5 - Performance analysis for the previous applications
  • 33 - Performance analysis
  • 33 - S5-001-number-of-waves-and-performance-analysis.pptx
  • 33 - graph-1.zip
  • 34 - Vector addition with a size not power of 2 important
  • 34 - sec5-002.zip

  • 6 - 2D Indexing
  • 35 - Matrices addition using 2D of blocks and threads
  • 35 - S5-001-number-of-waves-and-performance-analysis.pdf
  • 36 - Why L1 Hitrate is zero

  • 7 - Shared Memory Warp Divergence Shuffle Operations
  • 2 - Quiz 1.html
  • 37 - NVidia GTC Lecture and powerpoint.txt
  • 37 - Shared-Memory.pdf
  • 37 - The shared memory
  • 38 - Good detailed lecture about the warp divergence.txt
  • 38 - Warp Divergence

  • 8 - Debugging tools
  • 39 - Debugging using visual studio important 1
  • 39 - Getting Started with the CUDA Debugger.txt
  • 39 - NVIDIA Developer Tools.txt
  • 39 - NVIDIA Nsight Integration.txt
  • 39 - NVIDIA Nsight Visual Studio Code Edition.txt
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    تاریخ انتشار: ۲۰ اردیبهشت ۱۴۰۴
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