vector-add

Base: Vector Add Sample

The Base: Vector Add is the equivalent of a Hello, World! sample for data parallel programs, so the sample code demonstrates some core features of SYCL*. Additionally, building and running this sample verifies that your development environment is configured correctly for Intel® oneAPI Toolkits.

Area	Description
What you will learn	How to begin using SYCL* to offload computations to CPUs and accelerators
Time to complete	~15 minutes
Category	Getting Started

Purpose

The Base: Vector Add is a relatively simple program that adds two large vectors of integers and verifies the results. This program uses C++ and SYCL* for Intel® CPU and accelerators. By reviewing the code in this sample, you can learn how to use C++ code to offload computations to a GPU. This includes using Unified Shared Memory (USM) and buffers.

• USM requires an explicit wait for the asynchronous computation on the kernel to complete.

• When they go out of scope, buffers synchronize main memory with device memory implicitly; an explicit wait on the event is not required.

This sample provides example implementations of both Unified Shared Memory (USM) and buffers for side-by-side comparison.

Note: See the Simple Add sample to examine another getting started sample you can use to learn more about using the Intel® oneAPI Toolkits to develop SYCL-compliant applications for CPU, GPU, and FPGA devices.

Prerequisites

Optimized for	Description
OS	Ubuntu* 18.04 Windows* 10
Hardware	GEN9 or newer Intel® Programmable Acceleration Card with Intel® Arria® 10 GX FPGA (Intel® PAC with Intel® Arria® 10 GX FPGA)
Software	Intel® oneAPI DPC++/C++ Compiler

Key Implementation Details

The basic SYCL implementation explained in the code includes device selector, USM, buffer, accessor, kernel, and command groups.

The code attempts to execute on an available GPU and fallback to the system CPU if a compatible GPU is not detected. If successful, the name of the offload device and a success message is displayed, which indicates your development environment is set up correctly.

In addition, you can target an FPGA device using the instructions provided below. If you do not have FPGA hardware, the sample will run in emulation mode, which includes static optimization reports for design analysis.

Note: For comprehensive information about oneAPI programming, see the Intel® oneAPI Programming Guide. (Use search or the table of contents to find relevant information quickly.)

Set Environment Variables

When working with the command-line interface (CLI), you should configure the oneAPI toolkits using environment variables. Set up your CLI environment by sourcing the setvars script every time you open a new terminal window. This practice ensures that your compiler, libraries, and tools are ready for development.

Build the Base: Vector Add Sample

Note: If you have not already done so, set up your CLI environment by sourcing the setvars script in the root of your oneAPI installation.

Linux*:

• For system wide installations: . /opt/intel/oneapi/setvars.sh
• For private installations: . ~/intel/oneapi/setvars.sh
• For non-POSIX shells, like csh, use the following command: bash -c 'source <install-dir>/setvars.sh ; exec csh'

Windows*:

• C:\Program Files(x86)\Intel\oneAPI\setvars.bat
• Windows PowerShell*, use the following command: cmd.exe "/K" '"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" && powershell'

For more information on configuring environment variables, see Use the setvars Script with Linux* or macOS* or Use the setvars Script with Windows*.

Using Visual Studio Code* (VS Code) (Optional)

You can use Visual Studio Code* (VS Code) extensions to set your environment, create launch configurations, and browse and download samples.

The basic steps to build and run a sample using VS Code include:

1. Configure the oneAPI environment with the extension Environment Configurator for Intel® oneAPI Toolkits.
2. Download a sample using the extension Code Sample Browser for Intel® oneAPI Toolkits.
3. Open a terminal in VS Code (Terminal > New Terminal).
4. Run the sample in the VS Code terminal using the instructions below.

To learn more about the extensions and how to configure the oneAPI environment, see the Using Visual Studio Code with Intel® oneAPI Toolkits User Guide.

On Linux*

Configure the build system

1. Change to the sample directory.

2. Configure the project to use the buffer-based implementation.

   mkdir build
   cd build
   cmake ..
   

   or

   Configure the project to use the Unified Shared Memory (USM) based implementation.

   mkdir build
   cd build
   cmake .. -DUSM=1
   

Build for CPU and GPU

1. Build the program.

   make cpu-gpu
   

2. Clean the program. (Optional)

   make clean
   

Build for FPGA

1. Compile for FPGA emulation.

   make fpga_emu
   

2. Generate HTML performance reports.

   make report
   

   The reports reside at simple-add_report.prj/reports/report.html.

3. Compile the program for FPGA hardware. (Compiling for hardware can take a long time.)

   make fpga
   

4. Clean the program. (Optional)

   make clean
   

On Windows*

Configure the build system

1. Change to the sample directory.

2. Configure the project to use the buffer-based implementation.

   mkdir build
   cd build
   cmake -G "NMake Makefiles" ..
   

   or

   Configure the project to use the Unified Shared Memory (USM) based implementation.

   mkdir build
   cd build
   cmake -G "NMake Makefiles" .. -DUSM=1
   

Build for CPU and GPU

1. Build the program.

   nmake cpu-gpu
   

2. Clean the program. (Optional)

   nmake clean
   

Build for FPGA

Note: Compiling to FPGA hardware on Windows* requires a third-party or custom Board Support Package (BSP) with Windows* support.

1. Compile for FPGA emulation.

   nmake fpga_emu
   

2. Generate HTML performance reports.

   nmake report
   

The reports reside at simple-add_report.prj/reports/report.html.

3. Compile the program for FPGA hardware. (Compiling for hardware can take a long time.)

   nmake fpga
   

4. Clean the program. (Optional)

   nmake clean
   

Troubleshooting

If an error occurs, you can get more details by running make with the VERBOSE=1 argument:

make VERBOSE=1

If you receive an error message, troubleshoot the problem using the Diagnostics Utility for Intel® oneAPI Toolkits. The diagnostic utility provides configuration and system checks to help find missing dependencies, permissions errors, and other issues. See the Diagnostics Utility for Intel® oneAPI Toolkits User Guide for more information on using the utility.

Run the Base: Vector Add Program

Configurable Parameters

The source files (vector-add-buffers.cpp and vector-add-usm.cpp) specify the default vector size of 10000. You can change the vector size in one or both files if necessary.

On Linux

Run for CPU and GPU

1. Change to the output directory.

2. Run the program for Unified Shared Memory (USM) and buffers.

   ./vector-add-buffers
   ./vector-add-usm
   

Run for FPGA

1. Change to the output directory.

2. Run for FPGA emulation.

   ./vector-add-buffers.fpga_emu
   ./vector-add-usm.fpga_emu
   

3. Run on FPGA hardware.

   ./vector-add-buffers.fpga
   ./vector-add-usm.fpga
   

On Windows

Run for CPU and GPU

1. Change to the output directory.

2. Run the program for Unified Shared Memory (USM) and buffers.

   vector-add-usm.exe
   vector-add-buffers.exe
   

Run for FPGA

1. Change to the output directory.

2. Run for FPGA emulation.

   vector-add-buffers.fpga_emu.exe
   vector-add-usm.fpga_emu.exe
   

3. Run on FPGA hardware.

   vector-add-buffers.fpga.exe
   vector-add-usm.fpga.exe
   

Build and Run the Base: Vector Add Sample in Intel® DevCloud (Optional)

When running a sample in the Intel® DevCloud, you must specify the compute node (CPU, GPU, FPGA) and whether to run in batch or interactive mode.

Note: Since Intel® DevCloud for oneAPI includes the appropriate development environment already configured, you do not need to set environment variables.

Use the Linux instructions to build and run the program.

You can specify a GPU node using a single line script.

qsub  -I  -l nodes=1:gpu:ppn=2 -d .

• -I (upper case I) requests an interactive session.

• -l nodes=1:gpu:ppn=2 (lower case L) assigns one full GPU node.

• -d . makes the current folder as the working directory for the task.

  Available Nodes	Command Options
  GPU	qsub -l nodes=1:gpu:ppn=2 -d .
  CPU	qsub -l nodes=1:xeon:ppn=2 -d .
  FPGA Compile Time	qsub -l nodes=1:fpga_compile:ppn=2 -d .
  FPGA Runtime (Arria 10)	qsub -l nodes=1:fpga_runtime:arria10:ppn=2 -d .

Note: For more information on how to specify compute nodes, read Launch and manage jobs in the Intel® DevCloud for oneAPI Documentation.

Only fpga_compile nodes support compiling to FPGA. When compiling for FPGA hardware, increase the job timeout to 24 hours.

Executing programs on FPGA hardware is only supported on fpga_runtime nodes of the appropriate type, such as fpga_runtime:arria10.

Neither compiling nor executing programs on FPGA hardware are supported on the login nodes. For more information, see the Intel® DevCloud for oneAPI Intel® oneAPI Base Toolkit Get Started page.

Example Output

Running on device:        Intel(R) Gen(R) HD Graphics NEO
Vector size: 10000
[0]: 0 + 0 = 0
[1]: 1 + 1 = 2
[2]: 2 + 2 = 4
...
[9999]: 9999 + 9999 = 19998
Vector add successfully completed on device.

License

Code samples are licensed under the MIT license. See License.txt for details.

Third-party program Licenses can be found here: third-party-programs.txt.