When you need a rough estimate of your Linux system's floating-point performance, full-scale benchmarks like HPL can feel like overkill. Compilation issues, dependency hell, and configuration complexity often outweigh the benefits when you just need a ballpark figure.

Surprisingly, a simple C program can give you a reasonable approximation. While not as precise as professional benchmarks, this method provides instant results without any setup hassles. Here's why it works:

• Focuses on core floating-point operations
• Eliminates memory bandwidth bottlenecks
• Provides repeatable measurements
• Works on any Linux system with gcc

Here's a basic FLOPS estimator that measures single-precision performance:

#include 
#include 

#define ITERATIONS 1000000000

int main() {
    clock_t start, end;
    float a = 3.14159f, b = 2.71828f, c = 0.0f;
    
    start = clock();
    for (long i = 0; i < ITERATIONS; i++) {
        c = a * b; // Core FP operation
        // Prevent compiler optimization
        asm volatile("" : "+r"(c));
    }
    end = clock();
    
    double time_used = ((double)(end - start)) / CLOCKS_PER_SEC;
    double flops = ITERATIONS / time_used;
    
    printf("Estimated FLOPS: %.2f GFLOP/s\n", flops / 1e9);
    return 0;
}

Compile and execute with:

gcc -O3 flops_estimate.c -o flops_estimate
./flops_estimate

This test measures peak theoretical performance by:

• Using tight loops to maximize IPC
• Bypassing memory bottlenecks with registers
• Focusing on the multiply operation (common in HPC)

For those wanting slightly more robust solutions:

• LINPACK: Lightweight version of HPL (netlib.org/linpack)
• GFLOP: Simple Python script (github.com/gflops/gflops)
• Stress-ng: Includes basic FP tests (kernel.ubuntu.com/~cking/stress-ng)

While quick tests are convenient, remember:

• Modern CPUs have different FPU pipelines
• SIMD instructions aren't tested in basic programs
• Thermal throttling affects sustained performance
• For production use, proper benchmarks are recommended

When evaluating system performance, FLOPS (Floating Point Operations Per Second) serves as a crucial metric, especially for scientific computing and machine learning workloads. While comprehensive benchmarks like HPL (High Performance Linpack) exist, they often require complex setup and dependencies that can be time-consuming for a quick assessment.

For a ballpark estimate, a simple C program can provide meaningful results. The key is to:

1. Focus on a specific floating-point operation
2. Minimize memory access overhead
3. Ensure compiler optimizations don't eliminate the computation

Here's a basic FLOPS estimator that measures single-precision multiplication:

#include 
#include 
#include 

#define ITERATIONS 1000000000

int main() {
    float a = 3.14159f;
    float b = 2.71828f;
    float c = 0.0f;
    
    clock_t start = clock();
    for (long i = 0; i < ITERATIONS; i++) {
        c += a * b;  // Core FLOP operation
        a = b;       // Prevent optimization
        b = c;
    }
    clock_t end = clock();
    
    double elapsed = (double)(end - start) / CLOCKS_PER_SEC;
    double flops = (ITERATIONS * 2) / elapsed; // 2 operations per iteration
    
    printf("Estimated FLOPS: %.2f GFLOPS\n", flops / 1e9);
    return 0;
}

While this gives a rough estimate, be aware of several factors:

• Compiler Flags: Use -O2 optimization but avoid -O3 which might remove computations
• CPU Throttling: Ensure your system runs at full clock speed (check with cpupower frequency-info)
• Thermal Constraints: Sustained performance may differ from short bursts

If you prefer pre-built solutions:

# Using sysbench (requires installation)
sysbench cpu --cpu-max-prime=20000 --threads=1 run | grep "events per second"

# Using likwid-perfctr (advanced users)
likwid-perfctr -C 0 -g FLOPS_DP ./your_benchmark

Compare your measurements against:

Remember that sustained performance typically reaches 60-80% of theoretical peak in optimized workloads.