Merge pull request #74 from mozhgan-kch/master

updated OpenACC,profiler

hpc/miniprofiler/English/C/jupyter_notebook/miniweather.ipynb

@@ -106,9 +106,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.2"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

hpc/miniprofiler/English/C/jupyter_notebook/profiling-c-lab1.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "In this lab, we will optimize the weather simulation application written in C++ (if you prefer to use Fortran, click [this link](../../Fortran/jupyter_notebook/profiling-fortran.ipynb)). \n",
     "\n",
-    "Let's execute the cell below to display information about the GPUs running on the server by running the pgaccelinfo command, which ships with the PGI compiler that we will be using. To do this, execute the cell block below by giving it focus (clicking on it with your mouse), and hitting Ctrl-Enter, or pressing the play button in the toolbar above. If all goes well, you should see some output returned below the grey cell."
+    "Let's execute the cell below to display information about the GPUs running on the server by running the nvaccelinfo command, which ships with the NVIDIA HPC compiler that we will be using. To do this, execute the cell block below by giving it focus (clicking on it with your mouse), and hitting Ctrl-Enter, or pressing the play button in the toolbar above. If all goes well, you should see some output returned below the grey cell."
    ]
   },
   {
@@ -15,7 +15,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pgaccelinfo"
+    "!nvaccelinfo"
    ]
   },
   {
@@ -27,7 +27,7 @@
     "###  Learning objectives\n",
     "Learn how to assess your serial application, compile, and profile with Nsight systems and find the hotspots. In this exercise you will:\n",
     "\n",
-    "- Learn how to compile your serial application with PGI compiler\n",
+    "- Learn how to compile your serial application with NVIDIA HPC compiler\n",
     "- Learn how to benchmark and profile the serial code using NVIDIA Nsight systems \n",
     "- Learn how to identify routines responsible for the bulk of the execution time via NVTX markers shown on the Nsight System’s timeline\n",
     "- Learn about scaling and Amdahl’s law\n",
@@ -42,7 +42,7 @@
     "\n",
     "Open the downloaded file for inspection.\n",
     "\n",
-    "**Compile** the code with PGI compiler by running `make`. You can get compiler feedback by adding the `-Minfo` flag. Some of the available options are:\n",
+    "**Compile** the code with NVIDIA HPC compiler by running `make`. You can get compiler feedback by adding the `-Minfo` flag. Some of the available options are:\n",
     "\n",
     "- `accel` – Print compiler operations related to the accelerator\n",
     "- `all` – Print all compiler output\n",
@@ -191,9 +191,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.2"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

hpc/miniprofiler/English/C/jupyter_notebook/profiling-c-lab2.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "In this lab, we will optimize the weather simulation application written in C++ (if you prefer to use Fortran, click [this link](../../Fortran/jupyter_notebook/profiling-fortran.ipynb)). \n",
     "\n",
-    "Let's execute the cell below to display information about the GPUs running on the server by running the pgaccelinfo command, which ships with the PGI compiler that we will be using. To do this, execute the cell block below by giving it focus (clicking on it with your mouse), and hitting Ctrl-Enter, or pressing the play button in the toolbar above. If all goes well, you should see some output returned below the grey cell."
+    "Let's execute the cell below to display information about the GPUs running on the server by running the nvaccelinfo command, which ships with the NVIDIA HPC compiler that we will be using. To do this, execute the cell block below by giving it focus (clicking on it with your mouse), and hitting Ctrl-Enter, or pressing the play button in the toolbar above. If all goes well, you should see some output returned below the grey cell."
    ]
   },
   {
@@ -15,7 +15,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pgaccelinfo"
+    "!nvaccelinfo"
    ]
   },
   {
@@ -28,9 +28,9 @@
     "Learn how to identify and parallelise the computationally expensive routines in your application using OpenACC compute constructs (A compute construct is a parallel, kernels, or serial construct.). In this exercise you will:\n",
     "\n",
     "- Implement OpenACC parallelism using parallel directives to parallelise the serial application\n",
-    "- Learn how to compile your parallel application with PGI compiler\n",
+    "- Learn how to compile your parallel application with NVIDIA HPC compiler\n",
     "- Benchmark and compare the parallel version of the application with the serial version\n",
-    "- Learn how to interpret PGI compiler feedback to ensure the applied optimization were successful"
+    "- Learn how to interpret NVIDIA HPC compiler feedback to ensure the applied optimization were successful"
    ]
   },
   {
@@ -39,7 +39,7 @@
    "source": [
     "Click on the <b>[miniWeather_openacc.cpp](../source_code/lab2/miniWeather_openacc.cpp)</b> and <b>[Makefile](../source_code/lab2/Makefile)</b> and inspect the code before running below cells. We have already added OpenACC compute directives (`#pragma acc parallel`) around the expensive routines (loops) in the code.\n",
     "\n",
-    "Once done, compile the code with `make`. View the PGI compiler feedback (enabled by adding `-Minfo=accel` flag) and investigate the compiler feedback for the OpenACC code. The compiler feedback provides useful information about applied optimizations."
+    "Once done, compile the code with `make`. View the NVIDIA HPC compiler feedback (enabled by adding `-Minfo=accel` flag) and investigate the compiler feedback for the OpenACC code. The compiler feedback provides useful information about applied optimizations."
    ]
   },
   {
@@ -176,9 +176,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

hpc/miniprofiler/English/C/jupyter_notebook/profiling-c-lab3.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "In this lab, we will optimize the weather simulation application written in C++ (if you prefer to use Fortran, click [this link](../../Fortran/jupyter_notebook/profiling-fortran.ipynb)). \n",
     "\n",
-    "Let's execute the cell below to display information about the GPUs running on the server by running the pgaccelinfo command, which ships with the PGI compiler that we will be using. To do this, execute the cell block below by giving it focus (clicking on it with your mouse), and hitting Ctrl-Enter, or pressing the play button in the toolbar above. If all goes well, you should see some output returned below the grey cell."
+    "Let's execute the cell below to display information about the GPUs running on the server by running the nvaccelinfo command, which ships with the NVIDIA HPC compiler that we will be using. To do this, execute the cell block below by giving it focus (clicking on it with your mouse), and hitting Ctrl-Enter, or pressing the play button in the toolbar above. If all goes well, you should see some output returned below the grey cell."
    ]
   },
   {
@@ -15,7 +15,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pgaccelinfo"
+    "!nvaccelinfo"
    ]
   },
   {
@@ -241,9 +241,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

hpc/miniprofiler/English/C/jupyter_notebook/profiling-c-lab4.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "In this lab, we will optimize the weather simulation application written in C++ (if you prefer to use Fortran, click [this link](../../Fortran/jupyter_notebook/profiling-fortran.ipynb)). \n",
     "\n",
-    "Let's execute the cell below to display information about the GPUs running on the server by running the pgaccelinfo command, which ships with the PGI compiler that we will be using. To do this, execute the cell block below by giving it focus (clicking on it with your mouse), and hitting Ctrl-Enter, or pressing the play button in the toolbar above. If all goes well, you should see some output returned below the grey cell."
+    "Let's execute the cell below to display information about the GPUs running on the server by running the nvaccelinfo command, which ships with the NVIDIA HPC compiler that we will be using. To do this, execute the cell block below by giving it focus (clicking on it with your mouse), and hitting Ctrl-Enter, or pressing the play button in the toolbar above. If all goes well, you should see some output returned below the grey cell."
    ]
   },
   {
@@ -15,7 +15,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pgaccelinfo"
+    "!nvaccelinfo"
    ]
   },
   {
@@ -28,10 +28,10 @@
     "Learn how to improve the performance of the application by managing data movement and reducing the unnecessary data transfers. In this exercise you will:\n",
     "\n",
     "- Learn about unified memory and how to automatically migrate data between CPU and GPU\n",
-    "- Learn how to use it via PGI compiler managed option, and profiling managed memory\n",
+    "- Learn how to use it via NVIDIA HPC compiler managed option, and profiling managed memory\n",
     "- Learn how to identify redundant memory copies via Nsight Systems\n",
     "- Learn how to improve efficiency by reducing extra data copies via OpenACC data directive\n",
-    "- Learn how to use PGI compiler feedback as a guidance on where to insert the OpenACC data directives\n",
+    "- Learn how to use NVIDIA HPC compiler feedback as a guidance on where to insert the OpenACC data directives\n",
     "- Apply data directives to the parallel application, benchmark and profile it\n",
     "\n",
     "Let's inspect the profiler report from previous exercise. From the \"timeline view\" on the top pane, double click on the \"CUDA\" from the function table on the left and expand it. Zoom in on the timeline and you can see a pattern similar to the screenshot below. The blue boxes are the compute kernels and each of these groupings of kernels is surrounded by purple and teal boxes (annotated with red color) representing data movements.\n",
@@ -179,9 +179,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

hpc/miniprofiler/English/C/jupyter_notebook/profiling-c-lab5.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "In this lab, we will optimize the weather simulation application written in C++ (if you prefer to use Fortran, click [this link](../../Fortran/jupyter_notebook/profiling-fortran.ipynb)). \n",
     "\n",
-    "Let's execute the cell below to display information about the GPUs running on the server by running the pgaccelinfo command, which ships with the PGI compiler that we will be using. To do this, execute the cell block below by giving it focus (clicking on it with your mouse), and hitting Ctrl-Enter, or pressing the play button in the toolbar above. If all goes well, you should see some output returned below the grey cell."
+    "Let's execute the cell below to display information about the GPUs running on the server by running the nvaccelinfo command, which ships with the NVIDIA HPC compiler that we will be using. To do this, execute the cell block below by giving it focus (clicking on it with your mouse), and hitting Ctrl-Enter, or pressing the play button in the toolbar above. If all goes well, you should see some output returned below the grey cell."
    ]
   },
   {
@@ -15,7 +15,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pgaccelinfo"
+    "!nvaccelinfo"
    ]
   },
   {
@@ -360,9 +360,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

hpc/miniprofiler/English/C/jupyter_notebook/profiling-c.ipynb

@@ -8,7 +8,7 @@
     "\n",
     "In this lab, we covers steps to optimize the weather simulation application written in C++ (if you prefer to use Fortran, click [this link](../../Fortran/jupyter_notebook/profiling-fortran.ipynb)). \n",
     "\n",
-    "Let's execute the cell below to display information about the GPUs running on the server by running the pgaccelinfo command, which ships with the PGI compiler that we will be using. To do this, execute the cell block below by giving it focus (clicking on it with your mouse), and hitting Ctrl-Enter, or pressing the play button in the toolbar above. If all goes well, you should see some output returned below the grey cell."
+    "Let's execute the cell below to display information about the GPUs running on the server by running the nvaccelinfo command, which ships with the NVIDIA HPC compiler that we will be using. To do this, execute the cell block below by giving it focus (clicking on it with your mouse), and hitting Ctrl-Enter, or pressing the play button in the toolbar above. If all goes well, you should see some output returned below the grey cell."
    ]
   },
   {
@@ -17,7 +17,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pgaccelinfo"
+    "!nvaccelinfo"
    ]
   },
   {
@@ -219,9 +219,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

hpc/miniprofiler/English/C/source_code/lab1/Makefile

@@ -1,6 +1,6 @@
 # Copyright (c) 2020 NVIDIA Corporation.  All rights reserved. 
 
-CC := pgc++
+CC := nvc++
 CFLAGS := -O3 -w -ldl
 ACCFLAGS := -Minfo=accel
 NVTXLIB := -I/opt/nvidia/hpc_sdk/Linux_x86_64/20.9/cuda/11.0/include

hpc/miniprofiler/English/C/source_code/lab2/Makefile

@@ -1,6 +1,6 @@
 # Copyright (c) 2020 NVIDIA Corporation.  All rights reserved. 
 
-CC := pgc++
+CC := nvc++
 CFLAGS := -O3 -w
 ACCFLAGS := -ta=tesla:managed -Minfo=accel
 

hpc/miniprofiler/English/C/source_code/lab3/Makefile

@@ -1,6 +1,6 @@
 # Copyright (c) 2020 NVIDIA Corporation.  All rights reserved. 
 
-CC := pgc++
+CC := nvc++
 CFLAGS := -O3 -w
 ACCFLAGS := -ta=tesla:managed -Minfo=accel
 

hpc/miniprofiler/English/C/source_code/lab4/Makefile

@@ -1,6 +1,6 @@
 # Copyright (c) 2020 NVIDIA Corporation.  All rights reserved. 
 
-CC := pgc++
+CC := nvc++
 CFLAGS := -O3 -w
 ACCFLAGS := -ta=tesla:managed -Minfo=accel
 

hpc/miniprofiler/English/C/source_code/lab5/Makefile

@@ -1,6 +1,6 @@
 # Copyright (c) 2020 NVIDIA Corporation.  All rights reserved. 
 
-CC := pgc++
+CC := nvc++
 CFLAGS := -O3 -w
 ACCFLAGS := -ta=tesla:managed -Minfo=accel
 

hpc/miniprofiler/English/C/source_code/solutions/Makefile

@@ -1,6 +1,6 @@
 # Copyright (c) 2020 NVIDIA Corporation.  All rights reserved. 
 
-CC := pgc++
+CC := nvc++
 CFLAGS := -O3 -w
 ACCFLAGS := -ta=tesla:managed -Minfo=accel
 

hpc/miniprofiler/English/Fortran/jupyter_notebook/profiling-fortran-lab1.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "In this lab, we will optimize the weather simulation application written in Fortran (if you prefer to use C++, click [this link](../../C/jupyter_notebook/profiling-c.ipynb)). \n",
     "\n",
-    "Let's execute the cell below to display information about the GPUs running on the server by running the pgaccelinfo command, which ships with the PGI compiler that we will be using. To do this, execute the cell block below by giving it focus (clicking on it with your mouse), and hitting Ctrl-Enter, or pressing the play button in the toolbar above. If all goes well, you should see some output returned below the grey cell."
+    "Let's execute the cell below to display information about the GPUs running on the server by running the nvaccelinfo command, which ships with the NVIDIA HPC compiler that we will be using. To do this, execute the cell block below by giving it focus (clicking on it with your mouse), and hitting Ctrl-Enter, or pressing the play button in the toolbar above. If all goes well, you should see some output returned below the grey cell."
    ]
   },
   {
@@ -15,7 +15,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pgaccelinfo"
+    "!nvaccelinfo"
    ]
   },
   {
@@ -27,7 +27,7 @@
     "###  Learning objectives\n",
     "Learn how to assess your serial application, compile, and profile with Nsight systems and find the hotspots. In this exercise you will:\n",
     "\n",
-    "- Learn how to compile your serial application with PGI compiler\n",
+    "- Learn how to compile your serial application with NVIDIA HPC compiler\n",
     "- Learn how to benchmark and profile the serial code using NVIDIA Nsight systems \n",
     "- Learn how to identify routines responsible for the bulk of the execution time via NVTX markers shown on the Nsight System’s timeline\n",
     "- Learn about scaling and Amdahl’s law\n",
@@ -42,7 +42,7 @@
     "\n",
     "Open the downloaded file for inspection.\n",
     "\n",
-    "**Compile** the code with PGI compiler by running `make`. You can get compiler feedback by adding the `-Minfo` flag. Some of the available options are:\n",
+    "**Compile** the code with NVIDIA HPC compiler by running `make`. You can get compiler feedback by adding the `-Minfo` flag. Some of the available options are:\n",
     "\n",
     "- `accel` – Print compiler operations related to the accelerator\n",
     "- `all` – Print all compiler output\n",
@@ -184,9 +184,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.2"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

hpc/miniprofiler/English/Fortran/jupyter_notebook/profiling-fortran-lab2.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "In this lab, we will optimize the weather simulation application written in Fortran (if you prefer to use C++, click [this link](../../C/jupyter_notebook/profiling-c.ipynb)). \n",
     "\n",
-    "Let's execute the cell below to display information about the GPUs running on the server by running the pgaccelinfo command, which ships with the PGI compiler that we will be using. To do this, execute the cell block below by giving it focus (clicking on it with your mouse), and hitting Ctrl-Enter, or pressing the play button in the toolbar above. If all goes well, you should see some output returned below the grey cell."
+    "Let's execute the cell below to display information about the GPUs running on the server by running the nvaccelinfo command, which ships with the NVIDIA HPC compiler that we will be using. To do this, execute the cell block below by giving it focus (clicking on it with your mouse), and hitting Ctrl-Enter, or pressing the play button in the toolbar above. If all goes well, you should see some output returned below the grey cell."
    ]
   },
   {
@@ -15,7 +15,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pgaccelinfo"
+    "!nvaccelinfo"
    ]
   },
   {
@@ -28,9 +28,9 @@
     "Learn how to identify and parallelise the computationally expensive routines in your application using OpenACC compute constructs (A compute construct is a parallel, kernels, or serial construct.). In this exercise you will:\n",
     "\n",
     "- Implement OpenACC parallelism using parallel directives to parallelise the serial application\n",
-    "- Learn how to compile your parallel application with PGI compiler\n",
+    "- Learn how to compile your parallel application with NVIDIA HPC compiler\n",
     "- Benchmark and compare the parallel version of the application with the serial version\n",
-    "- Learn how to interpret PGI compiler feedback to ensure the applied optimization were successful\n"
+    "- Learn how to interpret NVIDIA HPC compiler feedback to ensure the applied optimization were successful\n"
    ]
   },
   {
@@ -39,7 +39,7 @@
    "source": [
     "Click on the <b>[miniWeather_openacc.f90](../source_code/lab2/miniWeather_openacc.f90)</b> and <b>[Makefile](../source_code/lab2/Makefile)</b> and inspect the code before running below cells. We have already added OpenACC compute directives (`!$acc parallel loop`) around the expensive routines (loops) in the code.\n",
     "\n",
-    "Once done, compile the code with `make`. View the PGI compiler feedback (enabled by adding `-Minfo=accel` flag) and investigate the compiler feedback for the OpenACC code. The compiler feedback provides useful information about applied optimizations."
+    "Once done, compile the code with `make`. View the NVIDIA HPC compiler feedback (enabled by adding `-Minfo=accel` flag) and investigate the compiler feedback for the OpenACC code. The compiler feedback provides useful information about applied optimizations."
    ]
   },
   {
@@ -193,9 +193,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

hpc/miniprofiler/English/Fortran/jupyter_notebook/profiling-fortran-lab3.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "In this lab, we will optimize the weather simulation application written in Fortran (if you prefer to use C++, click [this link](../../C/jupyter_notebook/profiling-c.ipynb)). \n",
     "\n",
-    "Let's execute the cell below to display information about the GPUs running on the server by running the pgaccelinfo command, which ships with the PGI compiler that we will be using. To do this, execute the cell block below by giving it focus (clicking on it with your mouse), and hitting Ctrl-Enter, or pressing the play button in the toolbar above. If all goes well, you should see some output returned below the grey cell."
+    "Let's execute the cell below to display information about the GPUs running on the server by running the nvaccelinfo command, which ships with the NVIDIA HPC compiler that we will be using. To do this, execute the cell block below by giving it focus (clicking on it with your mouse), and hitting Ctrl-Enter, or pressing the play button in the toolbar above. If all goes well, you should see some output returned below the grey cell."
    ]
   },
   {
@@ -15,7 +15,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pgaccelinfo"
+    "!nvaccelinfo"
    ]
   },
   {
@@ -229,9 +229,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

hpc/miniprofiler/English/Fortran/jupyter_notebook/profiling-fortran-lab4.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "In this lab, we will optimize the weather simulation application written in Fortran (if you prefer to use C++, click [this link](../../C/jupyter_notebook/profiling-c.ipynb)). \n",
     "\n",
-    "Let's execute the cell below to display information about the GPUs running on the server by running the pgaccelinfo command, which ships with the PGI compiler that we will be using. To do this, execute the cell block below by giving it focus (clicking on it with your mouse), and hitting Ctrl-Enter, or pressing the play button in the toolbar above. If all goes well, you should see some output returned below the grey cell."
+    "Let's execute the cell below to display information about the GPUs running on the server by running the nvaccelinfo command, which ships with the NVIDIA HPC compiler that we will be using. To do this, execute the cell block below by giving it focus (clicking on it with your mouse), and hitting Ctrl-Enter, or pressing the play button in the toolbar above. If all goes well, you should see some output returned below the grey cell."
    ]
   },
   {
@@ -15,7 +15,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pgaccelinfo"
+    "!nvaccelinfo"
    ]
   },
   {
@@ -28,10 +28,10 @@
     "Learn how to improve the performance of the application by managing data movement and reducing the unnecessary data transfers. In this exercise you will:\n",
     "\n",
     "- Learn about unified memory and how to automatically migrate data between CPU and GPU\n",
-    "- Learn how to use it via PGI compiler managed option, and profiling managed memory\n",
+    "- Learn how to use it via NVIDIA HPC compiler managed option, and profiling managed memory\n",
     "- Learn how to identify redundant memory copies via Nsight Systems\n",
     "- Learn how to improve efficiency by reducing extra data copies via OpenACC data directive\n",
-    "- Learn how to use PGI compiler feedback as a guidance on where to insert the OpenACC data directives\n",
+    "- Learn how to use NVIDIA HPC compiler feedback as a guidance on where to insert the OpenACC data directives\n",
     "- Apply data directives to the parallel application, benchmark and profile it\n",
     "\n",
     "Let's inspect the profiler report from previous exercise. From the \"timeline view\" on the top pane, double click on the \"CUDA\" from the function table on the left and expand it. Zoom in on the timeline and you can see a pattern similar to the screenshot below. The blue boxes are the compute kernels and each of these groupings of kernels is surrounded by purple and teal boxes (annotated with red color) representing data movements.\n",
@@ -180,9 +180,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

hpc/miniprofiler/English/Fortran/jupyter_notebook/profiling-fortran-lab5.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "In this lab, we will optimize the weather simulation application written in Fortran (if you prefer to use C++, click [this link](../../C/jupyter_notebook/profiling-c.ipynb)). \n",
     "\n",
-    "Let's execute the cell below to display information about the GPUs running on the server by running the pgaccelinfo command, which ships with the PGI compiler that we will be using. To do this, execute the cell block below by giving it focus (clicking on it with your mouse), and hitting Ctrl-Enter, or pressing the play button in the toolbar above. If all goes well, you should see some output returned below the grey cell."
+    "Let's execute the cell below to display information about the GPUs running on the server by running the nvaccelinfo command, which ships with the NVIDIA HPC compiler that we will be using. To do this, execute the cell block below by giving it focus (clicking on it with your mouse), and hitting Ctrl-Enter, or pressing the play button in the toolbar above. If all goes well, you should see some output returned below the grey cell."
    ]
   },
   {
@@ -15,7 +15,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pgaccelinfo"
+    "!nvaccelinfo"
    ]
   },
   {
@@ -360,9 +360,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

hpc/miniprofiler/English/Fortran/jupyter_notebook/profiling-fortran.ipynb

@@ -8,7 +8,7 @@
     " \n",
     "In this lab, we will optimize the weather simulation application written in Fortran (if you prefer to use C++, click [this link](../../C/jupyter_notebook/profiling-c.ipynb)). \n",
     "\n",
-    "Let's execute the cell below to display information about the GPUs running on the server by running the pgaccelinfo command, which ships with the PGI compiler that we will be using. To do this, execute the cell block below by giving it focus (clicking on it with your mouse), and hitting Ctrl-Enter, or pressing the play button in the toolbar above. If all goes well, you should see some output returned below the grey cell."
+    "Let's execute the cell below to display information about the GPUs running on the server by running the nvaccelinfo command, which ships with the NVIDIA HPC compiler that we will be using. To do this, execute the cell block below by giving it focus (clicking on it with your mouse), and hitting Ctrl-Enter, or pressing the play button in the toolbar above. If all goes well, you should see some output returned below the grey cell."
    ]
   },
   {
@@ -17,7 +17,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pgaccelinfo"
+    "!nvaccelinfo"
    ]
   },
   {
@@ -230,9 +230,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

hpc/miniprofiler/English/Fortran/source_code/lab1/Makefile

@@ -1,6 +1,6 @@
 # Copyright (c) 2020 NVIDIA Corporation.  All rights reserved. 
-FC := pgf90
-FCC := pgfortran
+FC := nvfortran
+FCC := nvfortran
 FFLAGS := -fast
 LDFLAGS := -lnvToolsExt 
 ACCFLAGS := -Minfo=accel

hpc/miniprofiler/English/Fortran/source_code/lab2/Makefile

@@ -1,6 +1,6 @@
 # Copyright (c) 2020 NVIDIA Corporation.  All rights reserved. 
-FC := pgf90
-FCC := pgfortran
+FC := nvfortran
+FCC := nvfortran
 FFLAGS := -fast
 LDFLAGS := -lnvToolsExt 
 ACCFLAGS := -ta=tesla:managed -Minfo=accel

hpc/miniprofiler/English/Fortran/source_code/lab3/Makefile

@@ -1,6 +1,6 @@
 # Copyright (c) 2020 NVIDIA Corporation.  All rights reserved. 
-FC := pgf90
-FCC := pgfortran
+FC := nvfortran
+FCC := nvfortran
 FFLAGS := -fast
 LDFLAGS := -lnvToolsExt 
 ACCFLAGS := -ta=tesla:managed -Minfo=accel

hpc/miniprofiler/English/Fortran/source_code/lab4/Makefile

@@ -1,6 +1,6 @@
 # Copyright (c) 2020 NVIDIA Corporation.  All rights reserved. 
-FC := pgf90
-FCC := pgfortran
+FC := nvfortran
+FCC := nvfortran
 FFLAGS := -fast
 LDFLAGS := -lnvToolsExt 
 ACCFLAGS := -ta=tesla:managed -Minfo=accel

hpc/miniprofiler/English/Fortran/source_code/lab5/Makefile

@@ -1,5 +1,5 @@
 # Copyright (c) 2020 NVIDIA Corporation.  All rights reserved. 
-FC := pgf90
+FC := nvfortran
 FFLAGS := -fast
 LDFLAGS := -lnvToolsExt 
 ACCFLAGS := -ta=tesla:managed -Minfo=accel

hpc/miniprofiler/English/Fortran/source_code/solutions/Makefile

@@ -1,6 +1,6 @@
 # Copyright (c) 2020 NVIDIA Corporation.  All rights reserved. 
-FC := pgf90
-FCC := pgfortran
+FC := nvfortran
+FCC := nvfortran
 FFLAGS := -fast
 LDFLAGS := -lnvToolsExt 
 ACCFLAGS := -ta=tesla:managed -Minfo=accel

hpc/miniprofiler/English/profiling_start.ipynb

@@ -28,13 +28,13 @@
     "    - Overview of [Mini Weather application](C/jupyter_notebook/miniweather.ipynb)\n",
     "    - Optimization Steps to parallel programming with OpneACC\n",
     "- Lab 1 ([C](C/jupyter_notebook/profiling-c-lab1.ipynb) , [Fortran](Fortran/jupyter_notebook/profiling-fortran-lab1.ipynb))\n",
-    "    - How to compile a serial application with PGI compiler\n",
+    "    - How to compile a serial application with NVIDIA HPC compiler\n",
     "    - How to profile a serial application with Nsight Systems and NVTX APIs\n",
     "    - How to use profiler's report to find hotspots\n",
     "    - Scaling and Amdahl's law and why it matters\n",
     "- Lab 2 ([C](C/jupyter_notebook/profiling-c-lab2.ipynb) , [Fortran](Fortran/jupyter_notebook/profiling-fortran-lab2.ipynb))\n",
     "    - Parallelise the serial application using OpenACC compute directives\n",
-    "    - How to compile a parallel application with PGI compiler\n",
+    "    - How to compile a parallel application with NVIDIA HPC compiler\n",
     "    - What does the compiler feedback tell us\n",
     "    - Profile with Nsight Systems\n",
     "    - Finding bottlenecks from Nsight Systems report\n",
@@ -93,9 +93,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

hpc/openacc/English/C/jupyter_notebook/openacc_c_lab3.ipynb

@@ -35,7 +35,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pgaccelinfo"
+    "!nvaccelinfo"
    ]
   },
   {
@@ -408,9 +408,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

hpc/openacc/English/C/source_code/lab1/Makefile

@@ -1,5 +1,5 @@
 # Copyright (c) 2020 NVIDIA Corporation.  All rights reserved. 
-CC := pgcc
+CC := nvc
 ACCFLAGS_1 := -fast
 ACCFLAGS_2 := -fast -ta=multicore -Minfo=accel
 ACCFLAGS_3 := -fast -ta=tesla:managed -Minfo=accel

hpc/openacc/English/C/source_code/lab1/solutions/Makefile

@@ -1,5 +1,5 @@
 # Copyright (c) 2020 NVIDIA Corporation.  All rights reserved. 
-CC := pgcc
+CC := nvc
 ACCFLAGS_1 := -fast
 ACCFLAGS_2 := -fast -ta=multicore -Minfo=accel
 ACCFLAGS_3 := -fast -ta=tesla:managed -Minfo=accel

hpc/openacc/English/C/source_code/lab2/Makefile

@@ -1,5 +1,5 @@
 # Copyright (c) 2020 NVIDIA Corporation.  All rights reserved. 
-CC := pgcc
+CC := nvc
 ACCFLAGS_1 := -fast -ta=tesla -Minfo=accel
 ACCFLAGS_2 := -fast -ta=tesla:managed -Minfo=accel
 

hpc/openacc/English/C/source_code/lab2/solutions/Makefile

@@ -1,5 +1,5 @@
 # Copyright (c) 2020 NVIDIA Corporation.  All rights reserved. 
-CC := pgcc
+CC := nvc
 ACCFLAGS_1 := -fast -ta=tesla -Minfo=accel
 ACCFLAGS_2 := -fast -ta=tesla:managed -Minfo=accel
 

hpc/openacc/English/C/source_code/lab2/update/Makefile

@@ -1,5 +1,5 @@
 # Copyright (c) 2020 NVIDIA Corporation.  All rights reserved. 
-CC := pgcc
+CC := nvc
 ACCFLAGS_1 := -fast -ta=tesla -Minfo=accel
 ACCFLAGS_2 := -fast -ta=tesla:managed -Minfo=accel
 

hpc/openacc/English/C/source_code/lab2/update/solution/Makefile

@@ -1,5 +1,5 @@
 # Copyright (c) 2020 NVIDIA Corporation.  All rights reserved. 
-CC := pgcc
+CC := nvc
 ACCFLAGS_1 := -fast -ta=tesla -Minfo=accel
 ACCFLAGS_2 := -fast -ta=tesla:managed -Minfo=accel
 

hpc/openacc/English/C/source_code/lab3/Makefile

@@ -1,5 +1,5 @@
 # Copyright (c) 2020 NVIDIA Corporation.  All rights reserved. 
-CC := pgcc
+CC := nvc
 ACCFLAGS:= -fast -ta=tesla -Minfo=accel
 
 laplace: jacobi.c laplace2d.c

hpc/openacc/English/C/source_code/lab3/solutions/collapse/Makefile

@@ -1,5 +1,5 @@
 # Copyright (c) 2020 NVIDIA Corporation.  All rights reserved. 
-CC := pgcc
+CC := nvc
 ACCFLAGS:= -fast -ta=tesla -Minfo=accel
 
 laplace: jacobi.c laplace2d.c

hpc/openacc/English/C/source_code/lab3/solutions/tile/Makefile

@@ -1,5 +1,5 @@
 # Copyright (c) 2020 NVIDIA Corporation.  All rights reserved. 
-CC := pgcc
+CC := nvc
 ACCFLAGS:= -fast -ta=tesla -Minfo=accel
 
 laplace: jacobi.c laplace2d.c

hpc/openacc/English/Fortran/jupyter_notebook/openacc_fortran_lab3.ipynb

@@ -35,7 +35,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pgaccelinfo"
+    "!nvaccelinfo"
    ]
   },
   {
@@ -392,9 +392,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

hpc/openacc/English/Fortran/source_code/lab1/Makefile

@@ -1,5 +1,5 @@
 # Copyright (c) 2020 NVIDIA Corporation.  All rights reserved. 
-FC := pgfortran
+FC := nvfortran
 ACCFLAGS_1 := -fast
 ACCFLAGS_2 := -fast -ta=multicore -Minfo=accel
 ACCFLAGS_3 := -fast -ta=tesla:managed -Minfo=accel

hpc/openacc/English/Fortran/source_code/lab1/solutions/Makefile

@@ -1,5 +1,5 @@
 # Copyright (c) 2020 NVIDIA Corporation.  All rights reserved. 
-FC := pgfortran
+FC := nvfortran
 ACCFLAGS_1 := -fast
 ACCFLAGS_2 := -fast -ta=multicore -Minfo=accel
 ACCFLAGS_3 := -fast -ta=tesla:managed -Minfo=accel

hpc/openacc/English/Fortran/source_code/lab2/Makefile

@@ -1,5 +1,5 @@
 # Copyright (c) 2020 NVIDIA Corporation.  All rights reserved. 
-FC := pgfortran
+FC := nvfortran
 ACCFLAGS_1 := -fast -ta=tesla -Minfo=accel
 ACCFLAGS_2 := -fast -ta=tesla:managed -Minfo=accel
 

hpc/openacc/English/Fortran/source_code/lab2/solutions/Makefile

@@ -1,5 +1,5 @@
 # Copyright (c) 2020 NVIDIA Corporation.  All rights reserved. 
-FC := pgfortran
+FC := nvfortran
 ACCFLAGS_1 := -fast -ta=tesla -Minfo=accel
 ACCFLAGS_2 := -fast -ta=tesla:managed -Minfo=accel
 

hpc/openacc/English/Fortran/source_code/lab2/update/Makefile

@@ -1,5 +1,5 @@
 # Copyright (c) 2020 NVIDIA Corporation.  All rights reserved. 
-FC := pgfortran
+FC := nvfortran
 ACCFLAGS_1 := -fast -ta=tesla -Minfo=accel
 ACCFLAGS_2 := -fast -ta=tesla:managed -Minfo=accel
 

hpc/openacc/English/Fortran/source_code/lab2/update/solution/Makefile

@@ -1,5 +1,5 @@
 # Copyright (c) 2020 NVIDIA Corporation.  All rights reserved. 
-FC := pgfortran
+FC := nvfortran
 ACCFLAGS_1 := -fast -ta=tesla -Minfo=accel
 ACCFLAGS_2 := -fast -ta=tesla:managed -Minfo=accel
 

hpc/openacc/English/Fortran/source_code/lab3/Makefile

@@ -1,5 +1,5 @@
 # Copyright (c) 2020 NVIDIA Corporation.  All rights reserved. 
-FC := pgfortran
+FC := nvfortran
 ACCFLAGS:= -fast -ta=tesla -Minfo=accel
 
 laplace: laplace2d.f90 jacobi.f90

hpc/openacc/English/Fortran/source_code/lab3/solutions/collapse/Makefile

@@ -1,5 +1,5 @@
 # Copyright (c) 2020 NVIDIA Corporation.  All rights reserved. 
-FC := pgfortran
+FC := nvfortran
 ACCFLAGS:= -fast -ta=tesla -Minfo=accel
 
 laplace: laplace2d.f90 jacobi.f90

hpc/openacc/English/Fortran/source_code/lab3/solutions/tile/Makefile

@@ -1,5 +1,5 @@
 # Copyright (c) 2020 NVIDIA Corporation.  All rights reserved. 
-FC := pgfortran
+FC := nvfortran
 ACCFLAGS:= -fast -ta=tesla -Minfo=accel
 
 laplace: laplace2d.f90 jacobi.f90

hpc/openacc/English/Lab2.ipynb

@@ -29,9 +29,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

hpc/openacc/English/Lab3.ipynb

@@ -29,9 +29,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

hpc/openacc/English/openacc_start.ipynb

@@ -62,9 +62,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }