How Intel® AVX Improves Performance on Server Applications

The latest Intel® Xeon® processor E7 v2 family includes a feature called Intel® Advanced Vector Extensions (Intel® AVX), which can potentially improve application performance. Here we will explain the context, and provide an example of how using Intel® AVX improved performance for a commonly known benchmark.

For existing vectorized code that uses floating point operations, you can gain a potential performance boost when running on newer platforms such as the Intel® Xeon® processor E7 v2 family, by doing one of the following:

1. Recompile your code, using the Intel® compiler with the proper AVX switch to convert existing SSE code. See the Intel® AVX State Transitions: Migrating SSE Code to AVX

2. Modify your code's function calls to leverage the Intel® Math Kernel Libraries (Intel® MKL) which are already optimized to use AVX where supported

3. Use the AVX intrinsic instructions. For high level language (such as C or C++) developers, you can use Intel® Intrinsic instructions to make the call and recompile code. See the Intel® Intrinsic Guide and Intel® 64 and IA-32 Architectures Optimization Reference Manual for more details

4. Code in assembly instructions directly. For low level language (such as assembly) developers, you can use those equivalent AVX instruction from their existing SSE code. See the Intel® 64 and IA-32 Architectures Optimization Reference Manual for more details

In this article, I will share a simple experiment using the Intel® Optimized LINPACK benchmark to demonstrate the performance gain of three different sized workloads (30K, 40K, and 75K) from Intel AVX running on Windows* and Linux* operating systems. I will also share the list of AVX instructions that were executed and the equivalent SSE instructions for developers who are interested in direct coding.

I used the following platform for the experiment:

Procedure for running LINPACK:

1. Download and install the following:
   1. Intel® Math Kernel Library – LINPACK Download
      https://software.intel.com/en-us/articles/intel-math-kernel-library-linpack-download
   2. Intel® Math Kernel Library (MKL)
      https://software.intel.com/en-us/intel-math-kernel-library-evaluation-options
2. Create three different input files for 30K, 40K, and 75K from the “...\linpack” directory
3. For AVX runs update files as follows:
   1. For Windows, update the runme_xeon64.bat file to take new input files you that have created runme_xeon64.bat file. For Linux, update the runme_xeon64 shell script file to take the new input files.
   2. The results will be in Glops similar to the Table 2
4. For Intel SSE runs, you will need to have an Intel AVX disabled processor and repeat the above steps.

What are the Intel AVX and the equivalent Intel SSE instructions that were executed?

Table 1 has a list of Intel AVX instructions that were executed during the Intel AVX runs. I have provided the equivalent Intel SSE instructions for those developers who are thinking of moving their existing Intel SSE code to Intel AVX.

Table 1 – Intel® AVX and Equivalent Intel SSE Instructions

The list in Table 1 is just a subset of the AVX instructions available. The full list can be obtained from the Intel® 64 and IA-32 Architectures Optimization Reference Manual.

What is the performance gain for running the LINPACK benchmark with Intel AVX vs. Intel SSE enabled on the Intel Xeon E7 4890 v2 server?

Table 2 shows the results from the three different workloads running on Windows* and Linux*. In the Ratio column, the numbers show that the LINPACK benchmark produces ~1.6x-1.7x better performance when running with the combination of an Intel AVX optimized LINPACK and an Intel AVX capable processor. This is just an example of the potential performance boost for LINPACK. For other applications, the performance gain will vary depending on the optimized code and the hardware environment.

Table 2 – Results and Performance Gain from the LINPACK benchmark

Conclusion

From our LINPACK experiment, we see compelling performance benefits when going to an AVX-enabled Intel Xeon processor; in this specific case, we saw a performance increase of ~1.6x-1.7x in our test environment, which is a strong case for developers who have SSE-enabled code and are weighing the benefit of moving to a newer Intel® Xeon® processor-based system with AVX. The reference materials below can help developers learn how to migrate existing SSE code to Intel AVX code.

References:

• AVX on Intel Developer Zone
• Introduction to Intel Advanced Vector Extensions
• Which applications are most likely to benefit from recompilation for Intel Advanced Vector Extensions (Intel AVX)?
• How to compile for Intel AVX
• Intel® Advanced Vector Extensions Programming Reference
• Intel® AVX State Transitions: Migrating SSE Code to AVX