ray12131
Custom Script Developer
2
MONTHS
2 2 MONTHS OF SERVICE
LEVEL 1
200 XP
[Hopper, Ampere, GeForce and Tesla]
Lecture 4 How to know the Architecture and Generation
Lecture 5 The difference between the GPU and the GPU Chip
Lecture 6 The architectures and the corresponding chips
Lecture 7 Nvidia GPU architectures From Fermi to hopper
Lecture 8 Parameters required to compare between different Architectures
Lecture 9 Half, single and double precision operations
Lecture 10 Compute capability and utilizations of the GPUs
Lecture 11 Before reading any whitepapers !! look at this
Lecture 12 Volta+Ampere+Pascal+SIMD (Don't skip)
Section 2: Installing Cuda and other programs
Lecture 13 What features installed with the CUDA toolkit?
Lecture 14 Installing CUDA on Windows
Lecture 15 Installing WSL to use Linux on windows OS.
Lecture 16 Installing Cuda toolkits on Linux
Section 3: Introduction to CUDA programming
Lecture 17 Mapping SW from CUDA to HW + introducing CUDA.
Lecture 18 001 Hello World program (threads - Blocks)
Lecture 19 Compiling Cuda on Linux
Lecture 20 002 Hello World program ( Warp_IDs)
Lecture 21 003 : Vector addition + the Steps for any CUDA project
Lecture 22 004 : Vector addition + blocks and thread indexing + GPU performance
Lecture 23 005 levels of parallelization - Vector addition with Extra-large vectors
Section 4: Profiling
Lecture 24 Query the device properties using the Runtime APIs
Lecture 25 Nvidia-smi and its configurations (Linux User)
Lecture 26 The GPU's Occupancy and Latency hiding
Lecture 27 Allocated active blocks per SM (important)
Lecture 28 Starting with the nsight compute (first issue)
Lecture 29 All profiling tools from NVidia (Nsight systems - compute - nvprof .)
Lecture 30 Error checking APIs (look at chat GPU there is an example)
Lecture 31 Nsight Compute performance using command line analysis
Lecture 32 Graphical Nsight Compute (windows and linux)
Section 5: Performance analysis for the previous applications
Lecture 33 Performance analysis
Lecture 34 Vector addition with a size not power of 2 !!! important
Section 6: 2D Indexing
Lecture 35 Matrices addition using 2D of blocks and threads
Lecture 36 Why L1 Hit-rate is zero ?
Section 7: Shared Memory + Warp Divergence + Shuffle Operations
Lecture 37 The shared memory
Lecture 38 Warp Divergence
Section 8: Debugging tools
Lecture 39 Debugging using visual studio (important) 1
For any one interested in GPU and CUDA like engineering students, researchers and any other one
FileAxa
Code:
RapidGator
Code:
TurboBit
Code:
Lecture 4 How to know the Architecture and Generation
Lecture 5 The difference between the GPU and the GPU Chip
Lecture 6 The architectures and the corresponding chips
Lecture 7 Nvidia GPU architectures From Fermi to hopper
Lecture 8 Parameters required to compare between different Architectures
Lecture 9 Half, single and double precision operations
Lecture 10 Compute capability and utilizations of the GPUs
Lecture 11 Before reading any whitepapers !! look at this
Lecture 12 Volta+Ampere+Pascal+SIMD (Don't skip)
Section 2: Installing Cuda and other programs
Lecture 13 What features installed with the CUDA toolkit?
Lecture 14 Installing CUDA on Windows
Lecture 15 Installing WSL to use Linux on windows OS.
Lecture 16 Installing Cuda toolkits on Linux
Section 3: Introduction to CUDA programming
Lecture 17 Mapping SW from CUDA to HW + introducing CUDA.
Lecture 18 001 Hello World program (threads - Blocks)
Lecture 19 Compiling Cuda on Linux
Lecture 20 002 Hello World program ( Warp_IDs)
Lecture 21 003 : Vector addition + the Steps for any CUDA project
Lecture 22 004 : Vector addition + blocks and thread indexing + GPU performance
Lecture 23 005 levels of parallelization - Vector addition with Extra-large vectors
Section 4: Profiling
Lecture 24 Query the device properties using the Runtime APIs
Lecture 25 Nvidia-smi and its configurations (Linux User)
Lecture 26 The GPU's Occupancy and Latency hiding
Lecture 27 Allocated active blocks per SM (important)
Lecture 28 Starting with the nsight compute (first issue)
Lecture 29 All profiling tools from NVidia (Nsight systems - compute - nvprof .)
Lecture 30 Error checking APIs (look at chat GPU there is an example)
Lecture 31 Nsight Compute performance using command line analysis
Lecture 32 Graphical Nsight Compute (windows and linux)
Section 5: Performance analysis for the previous applications
Lecture 33 Performance analysis
Lecture 34 Vector addition with a size not power of 2 !!! important
Section 6: 2D Indexing
Lecture 35 Matrices addition using 2D of blocks and threads
Lecture 36 Why L1 Hit-rate is zero ?
Section 7: Shared Memory + Warp Divergence + Shuffle Operations
Lecture 37 The shared memory
Lecture 38 Warp Divergence
Section 8: Debugging tools
Lecture 39 Debugging using visual studio (important) 1
For any one interested in GPU and CUDA like engineering students, researchers and any other one
Code:
https://fikper.com/WPBcy6QnPx/CUDA.Parallel.Programming.on.NVIDIA.GPUs.HW.and.SW.part1.rar.html
https://fikper.com/3xNFfkdFH2/CUDA.Parallel.Programming.on.NVIDIA.GPUs.HW.and.SW.part2.rar.html
https://fikper.com/X6SBrp9B0m/CUDA.Parallel.Programming.on.NVIDIA.GPUs.HW.and.SW.part3.rar.html
https://fikper.com/xp3sCRVOeQ/CUDA.Parallel.Programming.on.NVIDIA.GPUs.HW.and.SW.part4.rar.html
https://fikper.com/N1Fv77l2ac/CUDA.Parallel.Programming.on.NVIDIA.GPUs.HW.and.SW.part5.rar.html
https://fikper.com/I2gAgtkPXT/CUDA.Parallel.Programming.on.NVIDIA.GPUs.HW.and.SW.part6.rar.htmlCode:
Code:
https://fileaxa.com/9m3th9l29rpo/CUDA.Parallel.Programming.on.NVIDIA.GPUs.HW.and.SW.part1.rar
https://fileaxa.com/xxda3u34fpa1/CUDA.Parallel.Programming.on.NVIDIA.GPUs.HW.and.SW.part2.rar
https://fileaxa.com/m0wizrqhc5yo/CUDA.Parallel.Programming.on.NVIDIA.GPUs.HW.and.SW.part3.rar
https://fileaxa.com/3b7aswsvlguj/CUDA.Parallel.Programming.on.NVIDIA.GPUs.HW.and.SW.part4.rar
https://fileaxa.com/o3qc3a2rz9io/CUDA.Parallel.Programming.on.NVIDIA.GPUs.HW.and.SW.part5.rar
https://fileaxa.com/18cjow7qle5u/CUDA.Parallel.Programming.on.NVIDIA.GPUs.HW.and.SW.part6.rarCode:
Code:
[hide]https://rapidgator.net/file/83419619b5a1deb15134c2626e8da9f3/CUDA.Parallel.Programming.on.NVIDIA.GPUs.HW.and.SW.part1.rar[/hide]
[hide]https://rapidgator.net/file/6f6a84dd5b66389503f7f4e97e1d718e/CUDA.Parallel.Programming.on.NVIDIA.GPUs.HW.and.SW.part2.rar[/hide]
[hide]https://rapidgator.net/file/1367fe246a1c87f9610079ef3e3f280f/CUDA.Parallel.Programming.on.NVIDIA.GPUs.HW.and.SW.part3.rar[/hide]
[hide]https://rapidgator.net/file/0bf5e5083000521a6ac84c395fca5e1f/CUDA.Parallel.Programming.on.NVIDIA.GPUs.HW.and.SW.part4.rar[/hide]
[hide]https://rapidgator.net/file/7df876ebbe46624dc8731d5c7848b550/CUDA.Parallel.Programming.on.NVIDIA.GPUs.HW.and.SW.part5.rar[/hide]
[hide]https://rapidgator.net/file/e24d8482477403e6afdb7816a504d391/CUDA.Parallel.Programming.on.NVIDIA.GPUs.HW.and.SW.part6.rar[/hide]Code:
Code:
https://turbobit.net/ay4f0bs7u8d9/CUDA.Parallel.Programming.on.NVIDIA.GPUs.HW.and.SW.part1.rar.html
https://turbobit.net/7y1hfvuxhl2h/CUDA.Parallel.Programming.on.NVIDIA.GPUs.HW.and.SW.part2.rar.html
https://turbobit.net/foffa1qkghzd/CUDA.Parallel.Programming.on.NVIDIA.GPUs.HW.and.SW.part3.rar.html
https://turbobit.net/i0uen7wb5p5u/CUDA.Parallel.Programming.on.NVIDIA.GPUs.HW.and.SW.part4.rar.html
https://turbobit.net/5oe4mayvlsl9/CUDA.Parallel.Programming.on.NVIDIA.GPUs.HW.and.SW.part5.rar.html
https://turbobit.net/u52vjxf9vous/CUDA.Parallel.Programming.on.NVIDIA.GPUs.HW.and.SW.part6.rar.html