Understanding AVX-512 & Validating Usage on AMD EPYC 

In the world of high-performance computing and artificial intelligence, the quest for greater throughput often leads to a single question: How can we do more with each clock cycle?  Advanced Vector Extensions 512 (AVX-512) was introduced as a powerful tool to answer this question; it improves throughput without touching clock speed. Available on the latest Amazon EC2 instances powered by 5th Gen AMD EPYC "Turin" processors.

What is AVX-512?

At the core of modern processors is Single Instruction, Multiple Data (SIMD) execution for compute-intensive workloads. This architecture allows a single CPU instruction to process multiple data elements simultaneously. The Advanced Vector Extensions (AVX) family of instruction sets was introduced to enable wider SIMD execution and improve throughput for these workloads.

Over time, Advanced Vector Extensions (AVX) evolved by increasing the width of these operations to drive higher and higher throughput in the same clock cycle. While earlier iterations like SSE utilized 128-bit vectors and AVX2 expanded to 256 bits, AVX-512 doubles the capacity again to 512 bits. This means a single AVX-512 instruction can process 16 single-precision (32-bit) floating-point numbers or 64 bytes of data in one cycle. For compute-bound applications, this translates into a massive increase in throughput without a proportional increase in clock frequency or power.

AVX instructions are used in applications that need dense numerical computation, such as linear algebra, signal processing, scientific simulations, data analytics, and machine learning preprocessing. Customers in financial services and life sciences are leveraging AVX-512 to accelerate Monte Carlo simulations and genomic sequencing, driving significant improvements in latency and time-to-discovery. Similarly, the media and cybersecurity industries utilize these 512-bit registers to slash 8K video transcoding times and enhance the performance of complex encryption algorithms like AES-XTS.

Older AVX2 CPUs executed 512-bit math by combining two 256-bit vector operations, increasing instruction pressure and reducing overall efficiency. On the latest cloud instances with 5th Gen AMD EPYC "Turin" processors (M8a, C8a, R8a, C4D, H4D, E6, Dasv7, Fasv7, Easv7) this capability is backed by a full 512-bit data path.

Just because your server supports AVX-512 doesn't mean your application is using it. Many legacy binaries or unoptimized libraries default to AVX2 or even SSE instructions, leaving significant potential compute performance "on the table". In practice, many applications fall back to narrower vector paths, mix instruction widths at runtime, or fail to trigger vectorized code paths altogether.

To fully benefit from these capabilities, it is essential to understand not only whether an application can use AVX-512, but whether it actually does under real execution conditions.

To address this, performance engineers need a reliable way to verify instruction-level activity. In this post, we will use Dgemm as a sample application that uses complex vector operation with matrix multiplication as a sample program. And use profiling utilities like Proceswatch, perf, and uprof to confirm that your application is truly utilizing AVX 512 and extracting every bit of performance from your AMD Turin hardware.

Verify CPU AVX-512 Capability

Before diving into profiling tools, the first step is to verify that your environment is configured to support AVX-512.

The most direct way to verify support is by querying the CPU flags.

grep -o 'avx512f' /proc/cpuinfo | head -n 1

AMD Turin also supports advanced subsets such as AVX512_BF16  and AVX512_FP16. You can run a broader check to see the full suite of supported 512-bit extensions.

grep -E 'avx512' /proc/cpuinfo | head -n 1 | tr ' ' '\n' | grep 'avx512'

If the avx512f flag is missing, your application will likely fall back to AVX2 (256-bit) or SSE (128-bit) code paths.

Now, let’s validate if your application is utilizing AVX512. It is important to note that you do not need to use all these tools. Each provides a different level of visibility, from high-level summaries to low-level hardware counter analysis. You can choose the one that best fits your technical needs or existing environment.

I have used DGEMM as my sample application and I have started it in another terminal.

./dgemm_avx512 16384 600

You can find my script and usage instructions here: https://github.com/nfairoza/cloud-samples/tree/main/avx512-check

Perf Tool

Perf is a standard Linux performance analysis tool that provides access to hardware performance counters exposed by the CPU and shows you how many instructions your workload retires, how many cycles it consumes, and what kinds of floating-point and vector instructions it runs.

The CPU maintains dedicated counters for different vector widths. By monitoring these, we can see exactly how many instructions were "retired" in 128-bit, 256-bit, and 512-bit lanes.

perf list --details this shows symbolic names for events

# Check if 512-bit operations are happening
perf stat -e fp_ops_retired_by_width.pack_512_uops_retired \
  -p $(pgrep -f dgemm_avx512_new) -- sleep 10

# See which vector width is being used

perf stat \

  -e fp_ops_retired_by_width.pack_128_uops_retired \

  -e fp_ops_retired_by_width.pack_256_uops_retired \

  -e fp_ops_retired_by_width.pack_512_uops_retired \

  -e fp_ops_retired_by_width.scalar_uops_retired \

  -p $(pgrep -f dgemm_avx512_new) -- sleep 10

Here you can see ~ 99.9% of the floating-point work is happening in 512-bit wide operations, which is exactly what you want for a well-optimized avx512.

Internally, perf programs performance monitoring counters using raw event encodings consisting of an event code and a unit mask (umask). On AMD EPYC Turin, these map as follows:

You can use either the symbolic event  names or the raw encodings. Raw encodings are especially useful when symbolic names aren’t available or consistent across kernels.

perf stat \

  -e r0408 \

  -e r0808 \

  -e r1008 \

  -e r2008 \

  -p $(pgrep -f dgemm_avx512_new) -- sleep 10

If more than 99% of floating-point operations occur in the 512-bit category, this confirms that the workload is fully utilizing AVX-512 execution units.

ProcessWatch Tool

ProcessWatch is a lightweight Linux utility designed specifically to identify the instruction mix of a running process in real-time. Rather than exposing raw hardware events, ProcessWatch presents a simple, readable view of the instruction mix, how much of the execution comes from SSE, AVX, AVX2 and AVX-512 during each sampling interval.

To use ProcessWatch, you first start your application or benchmark. Once the process is running, you attach ProcessWatch to it and filter for the instruction sets you care about. In this example, we attach it to our DGEMM workload to monitor the transition between different vector widths.

sudo ./processwatch -p $(pgrep -n dgemm_avx512) \

  -f SSE -f AVX -f AVX2 -f AVX512 -f AMX_TILE 2>/dev/null

In above results, the AVX512 column consistently shows a value of 0.13 or 0.14. While this might look low at first glance, it is actually a sign of high-efficiency execution. A value of 0.14 represents 14% of the total instructions retired by the CPU during that sampling period. One AVX-512 instruction can process 8 double-precision numbers at once. To do the same work with standard instructions, the CPU would need many more individual operations. Therefore, even if 100% of your math is AVX-512, it will only represent a fraction of the total instruction count. You may notice, SSE, AVX, and AVX2 all remain at zero, the application avoids older vector paths entirely and drives execution through the 512-bit lanes.

uProf Tool

uProf is a high-performance analysis suite that provides both a command-line interface (AMDuProfCLI) and a robust GUI. It allows you to generate visual reports, charts, and even perform source-level attribution identifying exactly which lines of code are triggering AVX-512 instructions.

You download uProf directly from AMD. During the download process, AMD requires you to review and accept the End User License Agreement (EULA). Once you accept the terms, you receive the uProf package as a compressed archive, which you then transfer to the system you want to profile. uProf does not install itself into the system path by default, as, it ships as a standalone directory that contains the CLI and GUI tools. For this reason, it is typically invoked using a relative path (like ./AMDuProfCLI) from the bin/ directory, or by adding the uProf bin/ directory to $PATH for convenience.

export PATH=$PATH:/path/to/AMDuProf_Linux_x64/bin

# OR

sudo ln -sf $HOME/noorwork/AMDuProf_Nda_Linux_x64_5.0.1498/bin/AMDuProfCLI /usr/local/bin/AMDuProfCLI

AMDuProfCLI --version

On AMD Turin (Zen 5), we use the hardware event code 0x08 (PMCx008). To isolate the vector widths, we apply specific Unit Masks directly in the collection command. The following command attaches to our running DGEMM process and collects data for 30 seconds.

sudo AMDuProfCLI collect \

  -p $(pgrep -f dgemm_avx512_new) \

  -d 30 \

  --output-dir uprof_avx512_validation \

  -e event=PMCx008,umask=0x01,interval=250000 \ # Scalar

  -e event=PMCx008,umask=0x02,interval=250000 \ # 128-bit

  -e event=PMCx008,umask=0x04,interval=250000 \ # 256-bit

  -e event=PMCx008,umask=0x08,interval=250000   # 512-bit

The uProf profiling used sampling-based collection with an interval of 250,000, meaning every time the hardware counter reached 250,000 operations, uProf recorded a sample. This is fundamentally different from counting every single operation like perf.

If your application executes 10 trillion AVX-512 operations, perf will show you that massive total. In contrast, uProf might show 11,616 samples. This statistical approach provides a precise picture of where the CPU is spending its time without the massive overhead of counting every individual cycle.

Once the collection is complete, you can generate a CSV or HTML report to view the statistical distribution.

AMDuProfCLI report -i uprof_avx512_validation/AMDuProf-Data/

cat uprof_avx512_validation/AMDuProf-Data/report.csv

In my report .csv, looking at the hottest process data, we see the application accumulated exactly zero scalar operations, 120 samples of 128-bit operations, 15 samples of 256-bit operations, and 11,616 samples of 512-bit operations.

We see dominance of 512-bit samples, they represent 98.85% of all floating-point work. This validated my sample application is using AVX512 effectively.

Conclusion

Optimizing for the latest cloud hardware is more than a matter of selecting the right instance type. It is also about validating your software actually engages the hardware’s most powerful features. In this post, we have demonstrated using the DGEMM workload, the native 512 bit data path in 5th Gen AMD EPYC "Turin" processors offers a massive leap in throughput, but this potential is only realized when the application consistently utilizes 512 bit instructions.

If you’re running compute-intensive workloads and want to unlock the full benefit of AVX-512, don’t stop at validation. Use what you’ve measured to make better platform and optimization decisions. 

Don’t Go It Alone

If you’re running compute-intensive workloads and want to unlock the full benefit of AVX-512, don’t stop at validation. Use what you’ve measured to make better platform and optimization decisions.

Switching CPU architecture doesn’t require a full rewrite or a long migration cycle. In many cases, you can test AVX-512–enabled instances by changing an instance type and redeploy using the same tools and workflows you are already using. For more information Look forward to AMD switching guide.

Whether you’re modernizing existing workloads, downsizing oversized environments, or simply looking for better performance with cost efficiency, AMD offers tools and expertise to help guide those decisions. If you want help interpreting results, tuning your application, or deciding whether AVX-512–capable instances make sense for your environment, reach out to the AMD team. AMD works directly with customers across finance, AI, analytics, and scientific computing to validate vectorization, analyze instruction behavior, and map workloads to the right EPYC platforms.