Differences

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--- examples [2025/12/29 21:31] – [C++ program which uses MPI] dimitar
+++ examples [2025/12/30 23:27] (current) – [C++ program which uses GPU] dimitar
@@ Line 1: / Line 1: @@
-====MPI4PI====
+====Description====
-TOD
-----
+This page provides examples on how to use the cluster. There are language specific examples for **C/C++**, and **Python**, which showcase how you can compile and run applications which are written in those languages on the cluster. Additionally, there are examples for how to leverage the different resources of the cluster. These examples are written in **C++**, but the concepts apply to a program written in any language.
-==== PyTorch====
-Consider the following simple python test script( "pytorch_test.py"):
-<code python>
-import torch
-def test_pytorch():
-    print("PyTorch version:", torch.__version__)
-    print("CUDA available:", torch.cuda.is_available())
-    if torch.cuda.is_available():
-        print("CUDA device:", torch.cuda.get_device_name(0))
-        device = torch.device("cuda")
-    else:
-        device = torch.device("cpu")
-    # Simple tensor operation
-    x = torch.tensor([1.0, 2.0, 3.0], device=device)
-    y = torch.tensor([4.0, 5.0, 6.0], device=device)
-    z = x + y
-    print("Tensor operation result:", z)
-test_pytorch()
-</code>
-To test it on the unite cluster you can use the folling sbatch scrpit to run it:
-<code bash>
-#!/bin/bash
-#SBATCH --job-name=pytorch_test
-#SBATCH --output=pytorch_test.out
-#SBATCH --error=pytorch_test.err
-#SBATCH --time=00:10:00
-#SBATCH --partition=a40
-#SBATCH --gres=gpu:1
-#SBATCH --mem=4G
-#SBATCH --cpus-per-task=2
-# Load necessary modules (modify based on your system)
-module load python/pytorch-2.5.1-llvm-cuda-12.3-python-3.13.1-llvm
-# Activate your virtual environment if needed
-# source ~/your_env/bin/activate
-# Run the PyTorch script
-python3.13 pytorch_test.py
-</code>
 ----
-====Pandas====
-Consider the following simple python test script( “pandas_test.py”):
-<code python>
-import pandas as pd
-import numpy as np
-# Create a simple DataFrame
-data = {
-    'A': [1, 2, 3, 4],
-    'B': [5, 6, 7, 8],
-    'C': [9, 10, 11, 12]
-}
-df = pd.DataFrame(data)
-print("Original DataFrame:")
-print(df)
-# Test basic operations
-print("\nSum of each column:")
-print(df.sum())
-print("\nMean of each column:")
-print(df.mean())
-# Adding a new column
-df['D'] = df['A'] + df['B']
-print("\nDataFrame after adding new column D (A + B):")
-print(df)
-# Filtering rows
-filtered_df = df[df['A'] > 2]
-print("\nFiltered DataFrame (A > 2):")
-print(filtered_df)
-# Check if NaN values exist
-print("\nCheck for NaN values:")
-print(df.isna().sum())
-</code>
-You can use the following snatch script to run it:
-<code bash>
-#!/bin/bash
-#SBATCH --job-name=pytorch_test
-#SBATCH --output=pytorch_test.out
-#SBATCH --error=pytorch_test.err
-#SBATCH --time=00:10:00
-#SBATCH --partition=a40
-#SBATCH --gres=gpu:1
-#SBATCH --mem=4G
-#SBATCH --cpus-per-task=2
-# Load necessary modules (modify based on your system)
-module load python/3.13.1-llvm
-module load python/3.13/pandas/2.2.3
-# Activate your virtual environment if needed
-# source ~/your_env/bin/activate
-# Run the PyTorch script
-python3.13 pandas_test.py
-</code>
-----
 ====Simple C/C++ program====
 The following is a simple **C/C++** program which performs element-wise addition of 2 vectors. It does **not** use any dependent libraries:
@@ Line 875: / Line 768: @@
     CUDA_CHECK(cudaMalloc(&d_c, bytes));
-    double transfer_start = getTime();
     CUDA_CHECK(cudaMemcpy(d_a, h_a, bytes, cudaMemcpyHostToDevice));
     CUDA_CHECK(cudaMemcpy(d_b, h_b, bytes, cudaMemcpyHostToDevice));
@@ Line 899: / Line 791: @@
     CUDA_CHECK(cudaFree(d_b));
     CUDA_CHECK(cudaFree(d_c));
-    CUDA_CHECK(cudaEventDestroy(start));
-    CUDA_CHECK(cudaEventDestroy(stop));
     free(h_a);
     free(h_b);