Intel® VTune™ Profiler Performance Analysis Cookbook

Cookbook

  • 2021
  • 11/09/2021
  • Public Content
Contents

Poor Port Utilization

This recipe explores profiling a core-bound
matrix
 application using the Microarchitecture Exploration analysis (formerly, General Exploration) of the Intel® VTune™ Amplifier to understand the cause of the poor port utilization and Intel® Advisor to benefit from compiler vectorization.

Ingredients

This section lists the hardware and software tools used for the performance analysis scenario.
  • Application:
     matrix multiplication sample that multiplies 2 matrices of 2048x2048 size, matrix elements have the double type. The
    matrix_vtune_amp_axe.tgz
     sample package is available with the product in the
    <install-dir>/samples/en/C++
     directory and from the Intel Developer Zone at https://software.intel.com/en-us/product-code-samples.
  • Performance analysis tools:
  • Operating system:
     Linux*, Ubuntu* 16.04 64-bit
  • CPU:
     Intel® Core™ i7-6700K processor

Create Baseline

After optimizing the initial version of the
matrix
 code with a naïve multiplication algorithm (see the Frequent DRAM Accesses recipe), the execution time has reduced from 26 seconds to 1.3 seconds. This is a new performance baseline for further optimizations.

Run General Exploration Analysis

Run the General Exploration analysis for high-level understanding of potential performance bottlenecks for the sample application:
  1. Click the
    New Project
     button on the toolbar and specify a name for the new project, for example:
    matrix
    .
    The
    Configure Analysis
     window opens.
  2. On the
    WHERE
     pane, select the
    Local Host
     target system type.
  3. On the
    WHAT
     pane, select the
    Launch Application
     target type and specify an application for analysis.
  4. On the
    HOW
     pane, click the browse button and select
    Microarchitecture Analysis > Microarchitecture Exploration
    .
  5. Optionally, for such tiny workloads as this optimized
    matrix
     application, consider reducing the sampling interval to 0.1 seconds to get reliable metric values.
  6. Click
    Start
     to run the analysis.
    VTune
    Profiler
     launches the application, collects data, finalizes the data collection result resolving symbol information, which is required for successful source analysis.

Identify a Cause for Poor Port Utilization

Start with the
Summary
 view that shows high-level statistics for the application performance per hardware metrics:
You see that the dominant bottleneck has moved to
Core Bound
 >
Port Utilization
 with more than 3 execution ports utilized simultaneously for the most of the time. Note that the
Vector Capacity Usage
 metric value is also flagged as critical, which means that the code was either not vectorized or vectorized poorly. To confirm this, switch to the Assembly view of the kernel as follows:
  1. Click the
    Vector Capacity Usage (FPU)
     metric to switch to the Bottom-up view sorted by this metric.
  2. Double-click the hot
    multiply1
     function to open its Source view.
  3. Click the
    Assembly
     button on the toolbar to view the disassembly code:
You see that scalar instructions are used. The code is not vectorized.

Explore Options for Vectorization

Use the Vectorization Advisor tool from Intel® Advisor to understand what prevents the code from being vectorized:
Intel Advisor says that the loop was not vectorized due to assumed dependencies. For further details, mark the loop and run the Dependencies analysis from Intel Advisor:
According to the report, there are no actual dependencies found and Intel Advisor recommends to use
#pragma
 to make the compiler ignore the assumed dependencies:
With the
#pragma
 added, the
matrix
 code looks as follows: 
void multiply2_vec(inte msize, int tidx, int numt, TYPE a[][NUM],
    TYPE b[][NUM], TYPE c[][NUM], TYPE t[][NUM]
{
    int i,j,k;

    for(i=tidx; i<msize; i=i+numt) {
        for(k=0; k<msize; k++) {
#pragma ivdep
            for(j=0; j<msize; j++) {
                c[i][j] = c[i][j] + a[i][j] * b[i][j];
            }
        }
    }
}
Compiling and running the updated code results in 0.7 second speed-up in the execution time.

Compile with the Latest Instruction Set

Re-running the
VTune
Profiler
's Microarchitecture Exploration analysis on the latest code version provides the following result:
The
Vector Capacity Usage
 is improved but it still only 50% and flagged as performance-critical. Explore the Assembly view again for more insight:
The Assembly view helps you discover that the code uses SSE instructions while the CPU used for this use case supports the AVX2 instruction set. To apply it, re-compile the code with the
-xCORE-AVX2
 option and re-run the General Exploration analysis.
For the recompiled code, the execution time has dropped to 0.6 seconds. Re-run the Microarchitecture Exploration analysis to verify the optimization. The
Vector Capacity Usage
 metric value is now 100%:

Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.