3,108 research outputs found
cjw509/hello-world: hello-world
No full text<p>Release of hello-world repository to link with Zenodo.</p>
ConnorHahn/hello-world: First release of hello-world repo
No full text<p>Releasing hello-world repo to test out the Zenodo archiving.</p>
puzzlef/hello-cuda: A basic "Hello world" or "Hello CUDA" example to perform a number of operations on NVIDIA GPUs using CUDA
No full text<p>A basic "Hello world" or "Hello CUDA" example to perform a number of operations on NVIDIA GPUs using <a href="https://docs.nvidia.com/cuda/index.html">CUDA</a>.</p>
<blockquote>
<p>You can just copy <code>main.sh</code> to your system and run it. <br>
For the code, refer to <code>main.cu</code>.</p>
</blockquote>
<p><br></p>
<pre><code class="language-bash">$ bash main.sh
# Cloning into 'hello-cuda'...
# remote: Enumerating objects: 33, done.
# remote: Counting objects: 100% (12/12), done.
# remote: Compressing objects: 100% (11/11), done.
# remote: Total 33 (delta 2), reused 6 (delta 1), pack-reused 21
# Receiving objects: 100% (33/33), 24.58 KiB | 719.00 KiB/s, done.
# Resolving deltas: 100% (9/9), done.
# HELLO WORLD:
# GPU[B1.T0]: Hello CUDA
# GPU[B1.T1]: Hello CUDA
# GPU[B1.T2]: Hello CUDA
# GPU[B1.T3]: Hello CUDA
# GPU[B1.T4]: Hello CUDA
# GPU[B1.T5]: Hello CUDA
# GPU[B1.T6]: Hello CUDA
# GPU[B1.T7]: Hello CUDA
# GPU[B3.T0]: Hello CUDA
# GPU[B3.T1]: Hello CUDA
# GPU[B3.T2]: Hello CUDA
# GPU[B3.T3]: Hello CUDA
# GPU[B3.T4]: Hello CUDA
# GPU[B3.T5]: Hello CUDA
# GPU[B3.T6]: Hello CUDA
# GPU[B3.T7]: Hello CUDA
# GPU[B2.T0]: Hello CUDA
# GPU[B2.T1]: Hello CUDA
# GPU[B2.T2]: Hello CUDA
# GPU[B2.T3]: Hello CUDA
# GPU[B2.T4]: Hello CUDA
# GPU[B2.T5]: Hello CUDA
# GPU[B2.T6]: Hello CUDA
# GPU[B2.T7]: Hello CUDA
# GPU[B0.T0]: Hello CUDA
# GPU[B0.T1]: Hello CUDA
# GPU[B0.T2]: Hello CUDA
# GPU[B0.T3]: Hello CUDA
# GPU[B0.T4]: Hello CUDA
# GPU[B0.T5]: Hello CUDA
# GPU[B0.T6]: Hello CUDA
# GPU[B0.T7]: Hello CUDA
# CPU: Hello world!
# DEVICE PROPERTIES:
# COMPUTE DEVICE 0:
# Name: Tesla V100-PCIE-16GB
# Compute capability: 7.0
# Multiprocessors: 80
# Clock rate: 1380 MHz
# Global memory: 16151 MB
# Constant memory: 64 KB
# Shared memory per block: 48 KB
# Registers per block: 65536
# Threads per block: 1024 (max)
# Threads per warp: 32
# Block dimension: 1024x1024x64 (max)
# Grid dimension: 2147483647x65535x65535 (max)
# Device copy overlap: yes
# Kernel execution timeout: no
# CHOOSE DEVICE:
# Current CUDA device: 0
# CUDA device with atleast compute capability 1.3: 0
# Cards that have compute capability 1.3 or higher
# support double-precision floating-point math.
# MALLOC PERFORMANCE:
# Host malloc (1 GB): 0.00 ms
# CUDA malloc (1 GB): 1.35 ms
# Host free (1 GB): 0.00 ms
# CUDA free (1 GB): 1.51 ms
# MEMCPY PERFORMANCE:
# Host to host (1 GB): 412.59 ms
# Host to device (1 GB): 225.32 ms
# Device to host (1 GB): 246.87 ms
# Device to device (1 GB): 0.04 ms
# ADDITION:
# a = 1, b = 2
# a + b = 3 (GPU)
# VECTOR ADDITION:
# x = vector of size 1 GB
# y = vector of size 1 GB
# Vector addition on host (a = x + y): 438.02 ms
# Vector addition on device <<<32768, 32>>> (a = x + y): 4.33 ms
# Vector addition on device <<<16384, 64>>> (a = x + y): 3.98 ms
# Vector addition on device <<<8192, 128>>> (a = x + y): 4.01 ms
# Vector addition on device <<<4096, 256>>> (a = x + y): 3.97 ms
# Vector addition on device <<<2048, 512>>> (a = x + y): 4.00 ms
# Vector addition on device <<<1024, 1024>>> (a = x + y): 3.97 ms
# DOT PRODUCT:
# x = vector of size 1 GB
# y = vector of size 1 GB
# Dot product on host (a = x . y): 207.39 ms [2.154769e+05]
# Dot product on device (a = x . y): 2.69 ms [2.154769e+05] (memcpy approach)
# Dot product on device (a = x . y): 2.50 ms [2.154769e+05] (inplace approach)
# Dot product on device (a = x . y): 2.50 ms [2.154769e+05] (atomic-add approach)
# HISTOGRAM:
# buf = vector of size 1 GB
# Finding histogram of buf on host: 747.00 ms
# Finding histogram of buf on device (basic approach): 401.06 ms
# Finding histogram of buf on device (shared approach): 6.85 ms
# MATRIX MULTIPLICATION:
# x = matrix of size 16 MB
# y = matrix of size 16 MB
# Matrix multiplication on host (a = x * y): 33307.13 ms [3.287916e+00]
# Matrix multiplication on device (a = x * y): 18.93 ms (basic approach) [3.287916e+00]
# Matrix multiplication on device (a = x * y): 12.20 ms (tiled approach) [3.287916e+00]
</code></pre>
<p><br>
<br></p>
<h2>References</h2>
<ul>
<li><a href="https://nvlabs.github.io/cub/">CUB Documentation</a></li>
<li><a href="https://github.com/moderngpu/moderngpu">moderngpu/moderngpu: Patterns and behaviors for GPU computing</a></li>
<li><a href="https://developer.nvidia.com/blog/faster-parallel-reductions-kepler/">Faster Parallel Reductions on Kepler</a></li>
<li><a href="https://stackoverflow.com/a/37569519/1413259">CUDA atomicAdd for doubles definition error</a></li>
<li><a href="https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html">CUDA C++ Programming Guide</a></li>
<li><a href="https://docs.nvidia.com/cuda/index.html">CUDA Toolkit Documentation</a></li>
</ul>
<p><br>
<br></p>
<p><a href="https://www.youtube.com/watch?v=8sDg-lD1fZQ"></a><br>
<a href="https://puzzlef.github.io"></a></p>
smcl773/hello-world2: HelloWorld2
No full text<p>first release of hello world 2 for infosys 320</p>
mascotto91/hello-world: First Releases Test
No full text<p>First releases of hello-world test.</p>
puzzlef/hello-mpi: A basic "Hello world" example to output text to console from nodes over a network using MPI
No full text<p>A basic "Hello world" example to output text to console from nodes over a network using <a href="https://en.wikipedia.org/wiki/Message_Passing_Interface">MPI</a>.</p>
<p>A cluster at <a href="https://www.iiit.ac.in">IIIT</a> has four <a href="https://en.wikipedia.org/wiki/Slurm_Workload_Manager">SLURM</a> nodes. We want to run one process on each node, and run <code>32</code> threads using <a href="https://en.wikipedia.org/wiki/OpenMP">OpenMP</a>. In future, such a setup would allow us to run distributed algorithms that utilize each node's memory efficiently and minimize communication cost (within the same node). Output is saved in <a href="https://gist.github.com/wolfram77/41114570e75f5c0d0ffeb9fd73ec252b">gist</a>. Technical help from <a href="https://www.iiit.ac.in/people/faculty/Semparithi.Aravindan/">Semparithi Aravindan</a>.</p>
<blockquote>
<p><strong>Note</strong>
You can just copy <code>main.sh</code> to your system and run it. <br>
For the code, refer to <code>main.cu</code>.</p>
</blockquote>
<p><br></p>
<pre><code class="language-bash"> sbatch main.sh
# ==========================================
# SLURM_JOB_ID = 3373
# SLURM_NODELIST = node[01-04]
# SLURM_JOB_GPUS =
# ==========================================
# Cloning into 'hello-mpi'...
# [node01.local:2180262] MCW rank 0 is not bound (or bound to all available processors)
# [node02.local:3790641] MCW rank 1 is not bound (or bound to all available processors)
# [node04.local:3758212] MCW rank 3 is not bound (or bound to all available processors)
# [node03.local:3287974] MCW rank 2 is not bound (or bound to all available processors)
# P00: NAME=node01.local
# P00: OMP_NUM_THREADS=32
# P02: NAME=node03.local
# P02: OMP_NUM_THREADS=32
# P03: NAME=node04.local
# P03: OMP_NUM_THREADS=32
# P01: NAME=node02.local
# P01: OMP_NUM_THREADS=32
# P00.T00: Hello MPI
# P00.T24: Hello MPI
# P00.T16: Hello MPI
# P00.T26: Hello MPI
# P00.T05: Hello MPI
# P00.T29: Hello MPI
# P00.T22: Hello MPI
# P00.T06: Hello MPI
# P00.T17: Hello MPI
# P00.T23: Hello MPI
# P00.T25: Hello MPI
# P00.T13: Hello MPI
# P00.T01: Hello MPI
# P00.T09: Hello MPI
# P00.T03: Hello MPI
# P00.T02: Hello MPI
# P00.T31: Hello MPI
# P03.T00: Hello MPI
# P03.T24: Hello MPI
# P03.T05: Hello MPI
# P03.T21: Hello MPI
# P03.T04: Hello MPI
# ...
</code></pre>
<p><br>
<br></p>
<h2>References</h2>
<ul>
<li><a href="https://www.youtube.com/watch?v=c0C9mQaxsD4">MPI Basics : Tom Nurkkala</a></li>
<li><a href="https://www.youtube.com/watch?v=wTlu971fXkE">OpenMPI tutorial coding in Fortran 90 - 01 Hello World! : yinjianz</a></li>
<li><a href="https://www.youtube.com/watch?v=mzfVimVbguQ">Mod-09 Lec-40 MPI programming : Prof. Matthew Jacob</a></li>
<li><a href="https://www.youtube.com/watch?v=TiQRPMBBmDs">MPI/OpenMP Hybrid Programming : Neil Stringfellow</a></li>
<li><a href="https://www.youtube.com/watch?v=LBgx_S5ougk">Introduction to MPI Programming, part 1 : Hristo Iliev</a></li>
<li><a href="https://www.youtube.com/watch?v=1Inj6hdSnG0">Hybrid MPI+OpenMP programming : Dr. Jussi Enkovaara</a></li>
<li><a href="https://mpitutorial.com/tutorials/running-an-mpi-cluster-within-a-lan/">Running an MPI Cluster within a LAN : Dwaraka Nath</a></li>
<li><a href="https://riptutorial.com/mpi/example/16808/return-values-of-mpi-calls">Return values of MPI calls : RIP Tutorial</a></li>
<li><a href="https://www.dartmouth.edu/~rc/classes/intro_mpi/mpi_error_functions.html">MPI Error Handling : Dartmouth College</a></li>
<li><a href="https://stackoverflow.com/a/49873583/1413259">Does storing mpi rank enhance the performance : Cosmin Ioniță</a></li>
<li><a href="https://stackoverflow.com/a/20067763/1413259">MPI error handler not getting called when exception occurs : Hristo Iliev</a></li>
<li><a href="https://stackoverflow.com/a/50519696/1413259">Assert function for MPI Programs : Gilles Gouaillardet</a></li>
<li><a href="https://stackoverflow.com/a/39609017/1413259">In MPI, how to make the following program Wait till all calculations are completed : Gilles</a></li>
<li><a href="https://stackoverflow.com/a/54844676/1413259">MPI_Abort() vs exit() : R.. GitHub STOP HELPING ICE</a></li>
<li><a href="https://rookiehpc.org/mpi/docs/mpi_datatype/index.html">MPI_Datatype : RookieHPC</a></li>
<li><a href="https://mpi.deino.net/mpi_functions/MPI_Error_string.html">MPI_Error_string : DeinoMPI</a></li>
<li><a href="https://www.mpich.org/static/docs/v3.3/www3/MPI_Error_string.html">MPI_Error_string : MPICH</a></li>
<li><a href="https://www.mpich.org/static/docs/v3.3/www3/MPI_Comm_size.html">MPI_Comm_size : MPICH</a></li>
<li><a href="https://www.mpich.org/static/docs/v3.3/www3/MPI_Comm_rank.html">MPI_Comm_rank : MPICH</a></li>
<li><a href="https://www.mpich.org/static/docs/v3.2/www3/MPI_Get_processor_name.html">MPI_Get_processor_name : MPICH</a></li>
</ul>
<p><br>
<br></p>
<p><a href="https://www.youtube.com/watch?v=c0C9mQaxsD4"></a><br>
<a href="https://puzzlef.github.io"></a></p>
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