140,979 research outputs found
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>
Hello message scheme enhancement in CRMANET
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trabajadores, los principales problemas contra los que lucha la empresa, y
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No full text[ES] Proyecto aplicado que se fundamenta en la creación de un busto animatrónico femenino, que mediante la gesticulación de diferentes partes de su cara lanza un mensaje en código binario, planteando dudas respecto a la correcta comunicación entre el hombre y la máquina.[EN] Implemented Project based on the creation of a female animatronic bust, which sends a binari message through several parts of its face. It suggests doubts about correct communication between human beings and machines.Pruñonosa Asensio, A. (2013). Hello World! Una paradoja comunicativa entre humanos y máquinas. Universitat Politècnica de València. https://riunet.upv.es/handle/10251/38013Archivo delegad
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THE INFLUENCE OF MARKETING MIX ON CUSTOMER DECISIONS AT HELLO COFFEE BANJARMASIN
No full textThe research aims to; 1) examine and analyze the influence of product on customer buying decision at Hello Coffe Banjarmasin; 2) examine and analyze the influence of price on customer buying decision at Hello Coffe Banjarmasin; 3) examine and analyze the influence of place on customer buying decision at Hello Coffe Banjarmasin; 4) examine and analyze the influence of promotion on customer buying decision at Hello Coffe Banjarmasin. The type of research is quantitative with causal approach to determine the relationship between variables or the influence of independent variables on dependent variable. The population of this research is all customer at Hello Coffe. The sampling technique used in this research is quota sampling. The respondent of this research amounts to 35 customer at Hello Coffer Banjarmasin. the data collection technique used in this research is a questionnaire. The data analysis technique used in this research is multiple linear regression with the application of the SPSS Statistics 26 program. The result of the t-test in this research showed that there is a positive and significant influence of product on customer buying decision, a positive and significant influence of price on customer buying decision, a positive and significant influence of place on customer buying decision and there is a positive and significant influence of promotion on customer buying decision at Hello Coffe Banjarmasin. the determination coefficient (R2) test result showed that Customer Buying Decision (Y) variable can be affected by Product (X1), Price (X2), Place (X3) and Promotion (X4) by 40,2% while the remaining 59,8% can be explained by other factors or variables that are not examined in this research
Spatially-localized time dependent solutions including turbulence and their interactions in 2D Kolmogorov flow
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