From aaa80a7eb1a8968389cf81fbce1000f3e92e3b5e Mon Sep 17 00:00:00 2001
From: Jan Siwiec <jan.siwiec@vsb.cz>
Date: Tue, 17 Jan 2023 07:11:47 +0100
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+# NVIDIA CUDA
+
+## Introduction
+
+ROCm HIP allows developers to convert [CUDA code][a] to portable C++. The same source code can be compiled to run on NVIDIA or AMD GPUs.
+
+## This page documents the use of pre-built Singularity / apptainer image on karolina Accelerated nodes (acnXX)
+
+## Installed Versions of Singularity / apptainer
+
+For the current list of installed versions, use:
+
+```console
+module avail apptainer
+```
+
+Load the required module:
+
+```console
+module load apptainer/1.1.5
+```
+
+Run the container:
+```console
+singularity shell /home/vic0092/rocm/centos7-nvidia-rocm.sif
+```
+
+The above gives you Singularity / apptainer shell prompt:
+```console
+Singularity>
+```
+
+Verify that you have GPUs active and accessible on the given node:
+```console
+nvidia-smi
+```
+
+You should get output similar to:
+```console
++-----------------------------------------------------------------------------+
+| NVIDIA-SMI 515.65.07 Driver Version: 515.65.07 CUDA Version: 11.7 |
+|-------------------------------+----------------------+----------------------+
+| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
+| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
+| | | MIG M. |
+|===============================+======================+======================|
+| 0 NVIDIA A100-SXM... Off | 00000000:07:00.0 Off | 0 |
+| N/A 26C P0 50W / 400W | 0MiB / 40960MiB | 0% Default |
+| | | Disabled |
++-------------------------------+----------------------+----------------------+
+| 1 NVIDIA A100-SXM... Off | 00000000:0B:00.0 Off | 0 |
+| N/A 26C P0 51W / 400W | 0MiB / 40960MiB | 0% Default |
+| | | Disabled |
++-------------------------------+----------------------+----------------------+
+| 2 NVIDIA A100-SXM... Off | 00000000:48:00.0 Off | 0 |
+| N/A 22C P0 51W / 400W | 0MiB / 40960MiB | 0% Default |
+| | | Disabled |
++-------------------------------+----------------------+----------------------+
+| 3 NVIDIA A100-SXM... Off | 00000000:4C:00.0 Off | 0 |
+| N/A 25C P0 52W / 400W | 0MiB / 40960MiB | 0% Default |
+| | | Disabled |
++-------------------------------+----------------------+----------------------+
+| 4 NVIDIA A100-SXM... Off | 00000000:88:00.0 Off | 0 |
+| N/A 22C P0 51W / 400W | 0MiB / 40960MiB | 0% Default |
+| | | Disabled |
++-------------------------------+----------------------+----------------------+
+| 5 NVIDIA A100-SXM... Off | 00000000:8B:00.0 Off | 0 |
+| N/A 26C P0 54W / 400W | 0MiB / 40960MiB | 0% Default |
+| | | Disabled |
++-------------------------------+----------------------+----------------------+
+| 6 NVIDIA A100-SXM... Off | 00000000:C8:00.0 Off | 0 |
+| N/A 25C P0 52W / 400W | 0MiB / 40960MiB | 0% Default |
+| | | Disabled |
++-------------------------------+----------------------+----------------------+
+| 7 NVIDIA A100-SXM... Off | 00000000:CB:00.0 Off | 0 |
+| N/A 26C P0 51W / 400W | 0MiB / 40960MiB | 0% Default |
+| | | Disabled |
++-------------------------------+----------------------+----------------------+
+
++-----------------------------------------------------------------------------+
+| Processes: |
+| GPU GI CI PID Type Process name GPU Memory |
+| ID ID Usage |
+|=============================================================================|
+| No running processes found |
++-----------------------------------------------------------------------------+
+```
+
+
+### Code Example
+
+In this section, we show a basic code example. You can directly copy and paste the code to test it:
+
+```cpp
+// filename : /tmp/sample.cu
+
+#include <stdio.h>
+#include <cuda_runtime.h>
+
+#define CHECK(cmd) \
+{\
+ cudaError_t error = cmd;\
+ if (error != cudaSuccess) { \
+ fprintf(stderr, "error: '%s'(%d) at %s:%d\n", cudaGetErrorString(error), error,__FILE__, __LINE__); \
+ exit(EXIT_FAILURE);\
+ }\
+}
+
+
+/*
+ * Square each element in the array A and write to array C.
+ */
+template <typename T>
+__global__ void
+vector_square(T *C_d, T *A_d, size_t N)
+{
+ size_t offset = (blockIdx.x * blockDim.x + threadIdx.x);
+ size_t stride = blockDim.x * gridDim.x ;
+
+ for (size_t i=offset; i<N; i+=stride) {
+ C_d[i] = A_d[i] * A_d[i];
+ }
+}
+
+
+int main(int argc, char *argv[])
+{
+ float *A_d, *C_d;
+ float *A_h, *C_h;
+ size_t N = 1000000;
+ size_t Nbytes = N * sizeof(float);
+
+ cudaDeviceProp props;
+ CHECK(cudaGetDeviceProperties(&props, 0/*deviceID*/));
+ printf ("info: running on device %s\n", props.name);
+
+ printf ("info: allocate host mem (%6.2f MB)\n", 2*Nbytes/1024.0/1024.0);
+ A_h = (float*)malloc(Nbytes);
+ CHECK(A_h == 0 ? cudaErrorMemoryAllocation : cudaSuccess );
+ C_h = (float*)malloc(Nbytes);
+ CHECK(C_h == 0 ? cudaErrorMemoryAllocation : cudaSuccess );
+ // Fill with Phi + i
+ for (size_t i=0; i<N; i++)
+ {
+ A_h[i] = 1.618f + i;
+ }
+
+ printf ("info: allocate device mem (%6.2f MB)\n", 2*Nbytes/1024.0/1024.0);
+ CHECK(cudaMalloc(&A_d, Nbytes));
+ CHECK(cudaMalloc(&C_d, Nbytes));
+
+
+ printf ("info: copy Host2Device\n");
+ CHECK ( cudaMemcpy(A_d, A_h, Nbytes, cudaMemcpyHostToDevice));
+
+ const unsigned blocks = 512;
+ const unsigned threadsPerBlock = 256;
+
+ printf ("info: launch 'vector_square' kernel\n");
+ vector_square <<<blocks, threadsPerBlock>>> (C_d, A_d, N);
+
+ printf ("info: copy Device2Host\n");
+ CHECK ( cudaMemcpy(C_h, C_d, Nbytes, cudaMemcpyDeviceToHost));
+
+ printf ("info: check result\n");
+ for (size_t i=0; i<N; i++) {
+ if (C_h[i] != A_h[i] * A_h[i]) {
+ CHECK(cudaErrorUnknown);
+ }
+ }
+ printf ("PASSED!\n");
+}
+```
+
+First convert the CUDA sample code into HIP code:
+```console
+cd /tmp
+/opt/rocm/hip/bin/hipify-perl sample.cu > sample.cpp
+```
+
+This code can then be compiled using the following commands:
+```console
+cd /tmp
+export HIP_PLATFORM=$( /opt/rocm/hip/bin/hipconfig --platform )
+export HIPCC=/opt/rocm/hip/bin/hipcc
+$HIPCC sample.cpp -o sample
+```
+
+Running it, you should get the following output:
+```console
+Singularity> cd /tmp
+Singularity> ./sample
+info: running on device NVIDIA A100-SXM4-40GB
+info: allocate host mem ( 7.63 MB)
+info: allocate device mem ( 7.63 MB)
+info: copy Host2Device
+info: launch 'vector_square' kernel
+info: copy Device2Host
+info: check result
+PASSED!
+```
+
+That's all folks!
+
+[a]: nvidia-cuda.md
--
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