Jetson TX1与Jetson NANO性能基准测试异常问题咨询

Great question—let’s dive into why your performance results aren’t matching the theoretical 2:1 CUDA core ratio between the Jetson TX1 (256 cores) and Nano (128 cores). Here are the key factors to consider and actionable steps to validate:

1. Your Kernel is Memory-Bound, Not Compute-Bound #

The kernel you wrote is extremely lightweight: it only performs a single float multiplication per thread. For operations this simple, memory bandwidth becomes the bottleneck, not raw compute power.

Jetson TX1 and Nano have nearly identical memory bandwidth (TX1: ~12.8 GB/s, Nano: ~12.4 GB/s). When your kernel spends most of its time reading/writing memory instead of doing calculations, the extra CUDA cores on the TX1 can’t be utilized to their full potential—hence the small performance gap you’re observing.

2. Ensure Both Devices are Running at Maximum Performance Mode #

Both boards often default to power-saving modes that throttle GPU clock speeds. If either device isn’t operating at full capacity, you won’t see the expected performance difference.

Run these commands on both Jetsons to lock in maximum performance:

sudo nvpmodel -m 0  # Set to highest power mode
sudo jetson_clocks  # Force GPU/CPU to maximum clock speeds

3. Test with a Compute-Dense Kernel #

To make the CUDA core count matter, modify your kernel to be compute-heavy. This shifts the bottleneck from memory to raw compute, letting the TX1’s extra cores shine. For example:

__global__ void matrixMultiplyComputeDense(float* mat1, float* mat2, int nx, int ny, int iterations) {
    unsigned int ix = threadIdx.x + blockDim.x * blockIdx.x;
    unsigned int iy = threadIdx.y + blockDim.y * blockIdx.y;
    int idx = iy * nx + ix;
    
    // Add boundary check to avoid out-of-bounds access
    if (ix < nx && iy < ny) {
        float result = mat1[idx] * mat2[idx];
        // Repeat computations to increase work per thread
        for (int i = 0; i < iterations; i++) {
            result = result * mat2[idx] + mat1[idx];
        }
        mat1[idx] = result;
    }
}

Try running this with iterations=20 or higher. You should see the TX1’s runtime drop to roughly half of the Nano’s, matching the theoretical core ratio.

4. Validate Memory Access Efficiency #

While your (256,1) thread configuration has good occupancy, double-check that memory accesses are coalesced (Maxwell architectures rely heavily on this for bandwidth efficiency). Since you’re accessing contiguous memory per warp, your pattern should already be optimal—but use nvprof to confirm:

nvprof --metrics gld_efficiency,gst_efficiency ./your_test_program

Look for near-100% global load/store efficiency. If you see lower numbers, try adjusting your block layout (e.g., 2D blocks like (128,2)) to improve coalescing—though this won’t fix the memory bottleneck for your original kernel.

5. Check for ECC Memory Overhead #

If your TX1 has ECC memory enabled, it reduces effective memory bandwidth by ~10% due to parity checks. You can disable ECC (if not required) via the Jetson configuration tools to narrow the performance gap.

In short: Your initial test is limited by memory bandwidth, not compute power. Switching to a compute-dense workload and ensuring both devices run at full speed will reveal the expected 2:1 performance ratio between the TX1 and Nano.

内容的提问来源于stack exchange，提问作者roody