Configuration Recipes

These recipes detail how to configure your system and set up Intel® VTune™ Profiler or its predecessor, Intel® VTune™ Amplifier, for performance analysis in particular code environments.

• Analyzing CPU and FPGA (Intel® Arria® 10 GX) Interaction

• Analyzing Hot Code Paths Using Flame Graphs (NEW)
  Follow this recipe to understand how you can use Flame Graphs to detect hotspots and hot code paths in Java workloads.
• Improving Hotspot Observability in a C++ Application Using Flame Graphs
  See how the Flame Graph feature of Intel® VTune™ Profiler can help in a scenario where the true hotspot is obscured behind template functions and long function names.
• Measuring Performance Impact of NUMA in Multi-Processor Systems (NEW)
  Use this recipe to measure the performance impact of non-uniform memory access (NUMA) in multi-processor systems. This recipe uses the Platform Profiler analysis type in Intel® VTune™ Profiler.
• Analyzing CPU and FPGA (Intel® Arria® 10 GX) Interaction
  This recipe instructs you how to configure your platform to analyze an interaction of your CPU and FPGA, using Intel® Arria 10 GX FPGA as an example.
• Profiling a .NET* Core Application
  This recipe uses Intel® VTune™ Profiler for .NET Core dynamic-code profiling to locate performance hotspots in the managed code and optimize the application turnaround.
• Profiling Applications in Amazon Web Services* (AWS) EC2 Instances
  This recipe helps you set up a VM instance in AWS for performance profiling with Intel® VTune™ Profiler.
• Enabling Performance Profiling in GitLab* CI
  This recipe helps you integrate Intel® VTune™ Profiler into your GitLab* CI pipeline to profile your builds on-the-fly.
• Configuring a Hyper-V* Virtual Machine for Hardware-Based Hotspots Analysis
  This recipe helps you set up a Virtual Machine instance in the Hyper-V environment for hardware performance profiling with Intel® VTune™ Profiler.
• Profiling an Application for Performance Anomalies (NEW)
  This recipe describes how you can use the Anomaly Detection analysis type in Intel® VTune™ Profiler to identify performance anomalies that could result from several factors. The recipe also includes some suggestions to help you fix these anomalies.
• Profiling an OpenMP* Offload Application running on a GPU (NEW)
  This recipe illustrates how you can build and compile an OpenMP* application offloaded onto an Intel GPU. The recipe also describes how to use Intel® VTune™ Profiler to run analyses with GPU capabilities (HPC Performance Characterization, GPU Offload, and GPU Compute/Media Hotspots) on the OpenMP application and examine results.
• Profiling a SYCL* Application running on a GPU
  This recipe illustrates how you can build and compile a SYCL application. The recipe also describes how to use Intel® VTune™ Profiler to run a GPU analysis on the SYCL application and examine results.
• Using the Command-Line Interface to Analyze the Performance of a SYCL* Application running on a GPU (NEW)
  This recipe illustrates how you use the command-line interface (CLI) in Intel® VTune™ Profiler to analyze the performance of a SYCL application offloaded on an Intel GPU. The recipe also describes how you can customize your report with collected data.
• Profiling an FPGA-driven SYCL* Application
  Use this recipe to profile an FPGA-driven SYCL application. The recipe features the AOCL Profiler integrated in the CPU/FPGA Interaction (preview) analysis type in Intel® VTune™ Profiler.
• Profiling Hardware Without Intel Sampling Drivers
  This collection of recipes helps you set up driverless Linux* Perf*-based performance profiling with Intel® VTune™ Profiler, understand benefits and workarounds for possible limitations.
• Profiling MPI Applications
  This recipe uses Intel® VTune™ Profiler to identify imbalances and communications issues in MPI enabled applications, allowing you to improve the application performance.
• Profiling JavaScript* Code in Node.js*
  This recipe provides configuration steps to rebuild Node.js* and enable Intel® VTune™ Amplifier performance analysis for your JavaScript code including mixed-mode call stacks containing JS frames and native frames (native code, for example, system libraries or native libraries called from the JavaScript code).
• Profiling Docker* Containers
  Intel® VTune™ Profiler allows you to profile applications running in Docker* containers, including profiling multiple containers simultaneously. This recipe guides you through the configuration of a Docker container and describes ways to use VTune Profiler to analyze one or multiple concurrently running containers. This recipe also utilizes the Java* Code Analysis capabilities of VTune Profiler.
• Profiling a Remote Target Through a Proxy Server (NEW)
  This recipe describes how to run Intel® VTune™ Profiler through a proxy server to profile remote targets.
• Using Intel® VTune™ Profiler Server with Visual Studio Code and Intel® DevCloud for oneAPI (NEW)
  This recipe demonstrates how you use Intel® VTune™ Profiler as a web server when you develop and tune performance on a remote development machine. As an example, we use a compute node at Intel® DevCloud for oneAPI for a remote machine.
• Using Intel® VTune™ Profiler Server in HPC Clusters
  This recipe demonstrates the usage of Intel® VTune™ Profiler server in High Performance Computing (HPC) clusters for interactive performance profiling or accessing performance data for scheduled jobs.
• Profiling in a Singularity* Container
  This recipe guides you through the configuration of a Singularity container for the Intel® VTune™ Profiler analysis to identify hotspots in an application running in the isolated container environment.
• Profiling Linux*, Android*, and QNX* System Boot Time
  This recipe illustrates how to integrate Intel® VTune™ Profiler performance analysis to the boot flow of Linux, Android, and QNX operating systems. This analysis helps to identify activities that execute unexpectedly long on CPU cores during the OS boot. This enables further boot order inspection.