Qwen2 72b-instruct on NVIDIA 48G RTX-A6000 or Apple M4 Max 40 core 48G compared to OpenAI o1 pro

[obriensystems]

https://ollama.com/library/qwen2:72b-instruct

see
https://huggingface.co/datasets/princeton-nlp/SWE-bench_Verified
uses yarn
https://arxiv.org/pdf/2309.00071

prompt
Can we go over creating a mandelbrot image using cuda c. I would like to fully utilize all the cores of the GPU. I am using specific GPUs from NVidia including the RTX-3500 Ada mobile with 5120 cores and the RTX-4090 with 16384 cores.

Observation

• Qwen2 seems confused over the RTX-3500 Ada mobile - confusing it with the Amper generation RTX-3050, OpenAI O1 pro and deepseek-r1 are ok with the 3500

[obriensystems]

Dual RTX-4090 24+24=48G VRAM - qwen2-72b-instruct

PS C:\Users\michael> ollama run qwen2:72b-instruct --verbose
pulling manifest
pulling manifest
pulling manifest
pulling manifest
pulling manifest
pulling manifest
pulling f6ac28d6f58a... 100% ▕██████████████████████████████████████████████████▏  41 GB
pulling 77c91b422cc9... 100% ▕██████████████████████████████████████████████████▏ 1.4 KB
pulling 6e521a32ba66... 100% ▕██████████████████████████████████████████████████▏ 6.9 KB
pulling f02dd72bb242... 100% ▕██████████████████████████████████████████████████▏   59 B
pulling 75357d685f23... 100% ▕██████████████████████████████████████████████████▏   28 B
pulling 4bdbd69f323f... 100% ▕██████████████████████████████████████████████████▏  486 B
verifying sha256 digest
writing manifest
success
>>> Can we go over creating a mandelbrot image using cuda c. I would like to fully utilize all the cores of the GP
... U. I am using specific GPUs from NVidia including the RTX-3500 Ada mobile with 5120 cores and the RTX-4090 wit
... h 16384 cores.
Creating a Mandelbrot image using CUDA C can significantly speed up rendering times compared to doing it on
CPU, especially when utilizing high-end NVIDIA GPUs like the RTX 3050 Ada mobile (I believe you meant RTX
3050 or RTX 40 series since there's no RTX 3500) and RTX 4090. Here's a step-by-step guide on how to create
a Mandelbrot set image using CUDA C:

1. **Set up the project:**
   Create a new project in your preferred IDE (e.g., Visual Studio, CLion). Make sure you have CUDA Toolkit
installed and properly configured for your development environment.

2. **Understanding the Mandelbrot Set Algorithm:**
   The Mandelbrot set is defined by iterating the function `Z = Z^2 + C` starting with `Z = 0`, where `C` is
a complex number. If the magnitude of `Z` stays bounded (less than or equal to 2) after a certain maximum
number of iterations, then `C` is considered part of the set.

3. **CUDA Kernel Function:**
   Write a CUDA kernel function that performs the Mandelbrot calculation for each pixel in parallel. Each
thread will calculate whether a given complex number (representing a pixel's position on the complex plane)
belongs to the Mandelbrot set.
```cuda-c
__global__ void mandelbrot_kernel(int *data, int width, int height, float x_min, float x_max, float y_min,
float y_max, int max_iters) {
    int index = blockIdx.x * blockDim.x + threadIdx.x;
    if (index >= width)
        return;

    int stride = blockDim.x * gridDim.x;

    for (int j = threadIdx.y; j < height; j += blockDim.y) {
        double real = x_min + (double)index / width * (x_max - x_min);
        double imag = y_min + (double)j / height * (y_max - y_min);

        int i;
        double zx = 0, zy = 0;
        double temp;

        for (i = 0; i < max_iters; i++) {
            temp = zx*zx - zy*zy + real;
            zy = 2.0 * zx * zy + imag;
            zx = temp;
            if ((zx*zx + zy*zy) > 4)
                break;
        }

        data[j*width + index] = i == max_iters ? 0 : 255 * (double)i / max_iters;
    }
}

4. Host Code:
   Write the host code that launches the kernel and manages memory allocation, copying data between CPU and
   GPU, etc.

int main() {
    int width = 1920; // image width
    int height = 1080; // image height

    int *data;
    cudaMallocManaged(&data, width * height * sizeof(int));

    dim3 threadsPerBlock(32, 32);
    dim3 numBlocks(width / threadsPerBlock.x + 1, height / threadsPerBlock.y + 1);

    mandelbrot_kernel<<<numBlocks, threadsPerBlock>>>(data, width, height,
                                                      -2.0f, 1.0f, // x_min and x_max
                                                      -1.5f, 1.5f, // y_min and y_max
                                                      256); // max_iters

    // Save the image data to a file or display it.
    cudaDeviceSynchronize();

    // Free memory
    cudaFree(data);

    return 0;
}

Make sure you adjust the threadsPerBlock dimensions based on your specific GPU architecture and
capabilities. The number of threads per block should be optimized for your specific GPUs (RTX 3050, RTX
4090) to fully utilize all cores.

This is a simple implementation that can be further enhanced with more sophisticated color schemes or by
adding additional complexity such as the Julia set. It's also important to handle errors when calling CUDA
functions and manage memory carefully for larger images or more complex scenes.

total duration: 50.4186706s
load duration: 19.0934ms
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eval duration: 49.365s
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can you expand on the use of " dim3 threadsPerBlock(32, 32);" 32x32 as opposed to 16x16 and whether this wou
... ld be specific to GPU version - for example my RTX-3500 and RTX-4090 are both ada but I also would like to run
... the cuda code on my ampere RTX-A4500 and RTX-A6000
Choosing the optimal block size (i.e., threadsPerBlock dimensions) in CUDA can significantly impact
performance. The choice of block size is influenced by several factors including:

1. Warp Size: NVIDIA GPUs execute threads in groups called warps. For Pascal, Volta, Turing, and Ampere
   architectures, a warp contains 32 threads, while for Ada (RTX 40 series), the warp size remains the same at
   32 threads. Aligning your block size with multiples of this warp size can lead to better performance because
   it minimizes divergence among threads within a warp.

2. Register Usage: The number of registers used per thread affects the occupancy of the multiprocessors
   on the GPU, which in turn impacts how many blocks are concurrently running and thus the overall throughput.
   A block with too many registers per thread can limit the amount of work that can be executed simultaneously.

3. Memory Coalescing: CUDA prefers accessing global memory through coalesced accesses to reduce bank
   conflicts and improve bandwidth utilization. Block sizes should facilitate this type of access, especially
   for shared memory operations where threads in a warp need to load/store data from/to consecutive addresses
   concurrently.

4. Hardware Limitations: Different GPU architectures have varying capabilities regarding block
   dimensions (max number of threads per block, max block dimension size) and grid dimensions (max number of
   blocks). For example, Ada GPUs like the RTX 30/40 series support up to a maximum of 1024 threads per block
   while Ampere supports up to 1024 as well, but older architectures might have lower limits.

For your case with Ada and Ampere architecture GPUs (RTX 3500 - assuming it's RTX 30xx series or newer
model, RTX 4090, A4500, and A6000), you can generally use threadsPerBlock(32, 32) as a good starting point
because they all have the same warp size of 32 threads. This block configuration ensures that each block is
perfectly aligned with one warp in both dimensions (X and Y). However, to optimize for performance across
multiple GPU architectures:

1. Profile Performance: Use NVIDIA’s Nsight Systems or other profiling tools to measure the impact of
   different block sizes on your specific application. The optimal block size can vary depending on memory
   access patterns and computational workload.

2. Consider Occupancy: Aim for a high occupancy but also consider that more threads per block might not
   always lead to better performance if they consume too many resources or if there's no real benefit from
   additional parallelism.

3. Adapt Based on Device Query Information: Use the cudaDeviceGetAttribute function to query device
   attributes such as the maximum number of registers per block and other hardware limits, then use this
   information to adapt your kernel parameters dynamically at runtime.

For instance, you might find that using a smaller 16x16 configuration is better for some GPUs due to more
efficient memory usage or because it better fits within register limitations. Conversely, larger
configurations like 32x32 might be optimal on high-end GPUs with ample resources and when coalesced memory
access isn't the limiting factor.

In summary, while a block size of 32x32 is generally suitable for Ada and Ampere architectures due to the
warp size being 32 threads, you should profile your application's performance using different configurations
to determine the optimal settings. Keep in mind that what works well on one GPU might not be ideal for
another model or architecture, so it’s important to test and adapt accordingly.

Here is an example of how you could write more dynamic CUDA code with support for varying hardware:

// Query device attributes at runtime
int maxThreadsPerBlock;
cudaDeviceGetAttribute(&maxThreadsPerBlock, cudaDevAttrMaxThreadsPerBlock, 0);
int threadsX = std::min(32, maxThreadsPerBlock);
int threadsY = std::min(32, maxThreadsPerBlock / threadsX);

dim3 threadsPerBlock(threadsX, threadsY);

In this snippet, you set the block dimensions based on querying the maximum number of threads per block
supported by the GPU. This approach can help in maintaining compatibility across different devices while
aiming for high performance.

total duration: 50.5002798s
load duration: 18.5515ms
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eval duration: 49.945s
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$ nvidia-smi
Sat Jan 25 23:58:18 2025
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 561.17 Driver Version: 561.17 CUDA Version: 12.6 |
|-----------------------------------------+------------------------+----------------------+
| GPU Name Driver-Model | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA GeForce RTX 4090 WDDM | 00000000:01:00.0 Off | Off |
| 30% 39C P2 195W / 480W | 20498MiB / 24564MiB | 49% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
| 1 NVIDIA GeForce RTX 4090 WDDM | 00000000:02:00.0 On | Off |
| 0% 49C P2 201W / 480W | 21334MiB / 24564MiB | 45% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| 0 N/A N/A 11828 C ...uda_v12_avx\ollama_llama_server.exe N/A |
| 1 N/A N/A 2508 C+G ...5n1h2txyewy\ShellExperienceHost.exe N/A |

[obriensystems]

OpenAI o1 pro $200/m comparison from 20240120

prompt
Can we go over creating a mandelbrot image using cuda c. I would like to fully utilize all the cores of the GPU. I am using specific GPUs from NVidia including the RTX-3500 Ada mobile with 5120 cores and the RTX-4090 with 16384 cores.

thinking 4m 0s

Utilizing the full GPU potential
I’m exploring how to optimize a CUDA C program to leverage numerous GPU cores, like the RTX-3500 Ada and RTX-4090, for generating images such as Mandelbrot sets.
Crafting the iteration function
I’m detailing the creation of a function to iterate Mandelbrot set calculations for each pixel, scaling coordinates, applying color schemes based on iteration counts, and defining the iteration formula and limits.
Breaking down GPU tasks
I’m setting up a sample CUDA program, optimizing GPU resources with 2D block configurations, and preparing data for storage in a .ppm file.
Optimizing GPU usage
I’m focusing on parallel iteration computations, leveraging GPU cores efficiently, and exploring various optimization techniques and precision options to enhance fractal generation.

details
Below is a step-by-step guide and sample CUDA C/C++ code for generating a Mandelbrot set image on an NVIDIA GPU such as the RTX 3500 Ada mobile (5120 cores) or the RTX 4090 (16384 cores). This example demonstrates:
1. Defining a simple Mandelbrot function in CUDA
2. Using a 2D grid of blocks and threads so that each thread computes the color of one pixel.
3. Transferring the final image back to the CPU and writing it out (in this example, as a .ppm file).

You can adjust image resolution, iteration limits, and color-mapping techniques to suit your needs. The code below uses a straightforward approach that should scale well with large numbers of GPU cores.

Overview of the Approach
1. Mandelbrot Mathematics
• For each point (x, y) in your image, map it to a point c in the complex plane.
• Start with z = 0 . Repeatedly compute z \leftarrow z^2 + c .
• If |z| exceeds 2 before a certain iteration limit, we say c is outside the set (the pixel is colored accordingly).
• If we reach the iteration limit without escaping, we say c is likely inside the set (also colored accordingly).
2. CUDA Parallelization
• We launch a 2D grid of thread blocks, where each thread block is typically 16x16 or 32x32 threads.
• Each thread handles one pixel (or a small tile of pixels), computing the iteration count for its assigned pixel.
• Because there are many thousands of pixels, we can spawn tens or hundreds of thousands of threads, fully loading the GPU.
3. Transferring Image Data
• We allocate memory on the device (GPU) for the output image (e.g., an array of uchar4 or float4 or simply a grayscale buffer).
• After the kernel finishes, we copy results from the GPU back to the CPU.
• Finally, we write the image to disk (here we show an example using the PPM format, which is easy to write).

Example Code

Filename: mandelbrot.cu
Compile using:
nvcc mandelbrot.cu -o mandelbrot

#include <stdio.h>
#include <stdlib.h>
#include <cuda_runtime.h>

// Image dimensions
#define WIDTH  1920
#define HEIGHT 1080

// Maximum iterations for Mandelbrot
#define MAX_ITER 500

// Complex plane boundaries (you can adjust these)
#define X_MIN -2.0f
#define X_MAX  1.0f
#define Y_MIN -1.2f
#define Y_MAX  1.2f

CUDA Kernel

Each thread will:
1. Determine its (x, y) pixel coordinate from block and thread indices.
2. Convert (x, y) to the corresponding point in the complex plane.
3. Iterate the Mandelbrot formula up to MAX_ITER, or until escape.
4. Store the iteration result in the output array.

__global__ void mandelbrotKernel(unsigned char* output, int width, int height,
                                 float xMin, float xMax, float yMin, float yMax,
                                 int maxIter)
{
    // Compute the global x and y pixel indices
    int px = blockIdx.x * blockDim.x + threadIdx.x;
    int py = blockIdx.y * blockDim.y + threadIdx.y;

    // Check bounds
    if (px >= width || py >= height) return;

    // Map pixel (px, py) to the complex plane: (real, imag)
    float dx = (xMax - xMin) / (float)width;
    float dy = (yMax - yMin) / (float)height;
    float x0 = xMin + px * dx;
    float y0 = yMin + py * dy;

    // Mandelbrot iteration
    float x = 0.0f, y = 0.0f;
    int iter = 0;
    while ((x*x + y*y <= 4.0f) && (iter < maxIter)) {
        float xTemp = x*x - y*y + x0;
        y = 2.0f * x * y + y0;
        x = xTemp;
        iter++;
    }

    // Convert iteration count to a grayscale (or color) value
    // Here we do a simple grayscale, but you can do more advanced coloring
    unsigned char color = (unsigned char)(255.0f * (float)iter / (float)maxIter);

    // Flatten 2D -> 1D index in row-major format
    int index = py * width + px;

    // Store the grayscale value
    output[index] = color;
}

Host Code

int main()
{
    // Image buffer (host)
    size_t imageSize = WIDTH * HEIGHT * sizeof(unsigned char);
    unsigned char* h_image = (unsigned char*)malloc(imageSize);

    // Device buffer
    unsigned char *d_image;
    cudaMalloc((void**)&d_image, imageSize);

    // Configure a 2D block and 2D grid
    dim3 blockSize(16, 16);
    dim3 gridSize((WIDTH + blockSize.x - 1) / blockSize.x,
                  (HEIGHT + blockSize.y - 1) / blockSize.y);

    // Launch the kernel
    mandelbrotKernel<<<gridSize, blockSize>>>(d_image, WIDTH, HEIGHT,
                                              X_MIN, X_MAX, Y_MIN, Y_MAX,
                                              MAX_ITER);

    // Synchronize to check for any kernel errors
    cudaError_t err = cudaDeviceSynchronize();
    if (err != cudaSuccess) {
        fprintf(stderr, "CUDA Error: %s\n", cudaGetErrorString(err));
        return -1;
    }

    // Copy result back to host
    cudaMemcpy(h_image, d_image, imageSize, cudaMemcpyDeviceToHost);

    // Free device memory
    cudaFree(d_image);

    // Write PPM image (grayscale, P5 format)
    FILE *fp = fopen("mandelbrot.ppm", "wb");
    if (!fp) {
        fprintf(stderr, "Failed to open file for writing.\n");
        return -1;
    }
    fprintf(fp, "P5\n%d %d\n255\n", WIDTH, HEIGHT);
    fwrite(h_image, 1, imageSize, fp);
    fclose(fp);

    // Clean up host memory
    free(h_image);

    printf("Mandelbrot image saved to mandelbrot.ppm\n");
    return 0;
}

Explanation
1. blockSize(16, 16) and gridSize((WIDTH+15)/16, (HEIGHT+15)/16)
• Each block is 16x16 = 256 threads.
• The grid covers the entire 2D image.
• Even on GPUs with thousands of cores, having large numbers of threads helps

[obriensystems]

qwen2-72b-instruct-fp16 on RTX-4090 dual 24+24=48G and 192GB cpu ram

145g model runs in 102G cpu ram and 20, 22G VRAM - expected to be very slow as we are working across the PCIe x8 shared bus

2 tokens / sec

ael@13900b MINGW64 ~
$ nvidia-smi
Sun Jan 26 00:43:01 2025
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 561.17                 Driver Version: 561.17         CUDA Version: 12.6     |
|-----------------------------------------+------------------------+----------------------+
| GPU  Name                  Driver-Model | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA GeForce RTX 4090      WDDM  |   00000000:01:00.0 Off |                  Off |
|  0%   49C    P3             51W /  480W |   21600MiB /  24564MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+
|   1  NVIDIA GeForce RTX 4090      WDDM  |   00000000:02:00.0  On |                  Off |
|  0%   51C    P3             50W /  480W |   20103MiB /  24564MiB |     22%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|    0   N/A  N/A      7380      C   ...uda_v12_avx\ollama_llama_server.exe      N/A      |


PS C:\Users\michael> ollama run qwen2:72b-instruct-fp16 --verbose
pulling manifest
pulling de0a69bb9537... 100% ▕██████████████████████████████████████████████████▏ 145 GB
pulling 77c91b422cc9... 100% ▕██████████████████████████████████████████████████▏ 1.4 KB
pulling 6e521a32ba66... 100% ▕██████████████████████████████████████████████████▏ 6.9 KB
pulling f02dd72bb242... 100% ▕██████████████████████████████████████████████████▏   59 B
pulling 75357d685f23... 100% ▕██████████████████████████████████████████████████▏   28 B
pulling 95b0c40eb960... 100% ▕██████████████████████████████████████████████████▏  485 B
verifying sha256 digest
writing manifest
success
>>> Can we go over creating a mandelbrot image using cuda c. I would like to fully utilize all the cores of the GP
... U. I am using specific GPUs from NVidia including the RTX-3500 Ada mobile with 5120 cores and the RTX-4090 wit
... h 16384 cores.
Creating a Mandelbrot set image using CUDA C can significantly speed up rendering times by leveraging the
massive parallel processing power of GPUs, like those you mentioned (RTX 3050 Ada mobile and RTX 4090).
Here's how to create such an application:

### 1. Set Up Your Project
Ensure you have NVIDIA CUDA Toolkit installed on your system, which includes the necessary compilers and
libraries for developing GPU-accelerated applications.

### 2. Create a Cuda Kernel Function
A kernel function is executed by many threads in parallel on the GPU. In this case, each thread will
calculate one or more pixels of the Mandelbrot set image based on its grid position.

```cuda-cpp
__global__ void mandelbrotKernel(float *d_data, int width, int height) {
    // Each block processes a portion of the rows in the image.
    int row = blockIdx.y * blockDim.y + threadIdx.y;

    if (row < height) {
        for (int col = blockIdx.x * blockDim.x + threadIdx.x; col < width; col += gridDim.x * blockDim.x) {
            float x0 = mapToReal(row, -2.1f, 0.6f, 0, height);
            float y0 = mapToImag(col, -1.3f, 1.3f, 0, width);

            int index = row * width + col;
            d_data[index] = calculateMandelbrot(x0, y0);
        }
    }
}

float mapToReal(int coord, float realMin, float realMax, int coordMin, int coordMax) {
    return (realMax - realMin) / (coordMax - coordMin) * (coord - coordMin) + realMin;
}

float mapToImag(int coord, float imagMin, float imagMax, int coordMin, int coordMax) {
    return (imagMax - imagMin) / (coordMax - coordMin) * (coord - coordMin) + imagMin;
}

int calculateMandelbrot(float x0, float y0) {
    complex<float> z = 0.0f;
    complex<float> c(x0, y0);
    int i;

    for (i = 0; i < MAX_ITERATIONS; ++i)
        if (abs(z) > 2.0f)
            break;
        else
            z = z*z + c;

    return i == MAX_ITERATIONS ? 0 : 255 * i / MAX_ITERATIONS;
}

3. Launch the Kernel Function from Host Code

In your host code, allocate memory for the image data on both CPU and GPU, copy initial values to the device
(if necessary), launch the kernel with appropriate grid and block dimensions, then copy results back to CPU.

#include <cuda_runtime.h>
#include <complex>

// Define constants
#define MAX_ITERATIONS 1024

int main() {
    int width = 800;
    int height = 600;

    // Allocate memory on the host for the image data.
    float *h_data = new float[width*height];

    // Allocate memory on the device
    float *d_data;
    cudaMalloc((void**)&d_data, sizeof(float) * width*height);

    // Launch kernel function with grid and block dimensions.
    dim3 blockDim(16, 16);        // Blocks have 16x16 threads each
    dim3 gridDim(width/16+1, height/16+1); // Grid size based on image dimensions

    mandelbrotKernel<<<gridDim, blockDim>>>(d_data, width, height);

    // Copy result data from device to host memory.
    cudaMemcpy(h_data, d_data, sizeof(float) * width*height, cudaMemcpyDeviceToHost);

    // Free allocated memory
    cudaFree(d_data);
    delete[] h_data;

    return 0;
}

4. Optimize for Your Specific GPU

Optimizing CUDA code can involve tweaking grid and block dimensions, managing shared memory usage, and
handling warp divergence (ensuring threads within a warp follow the same execution path). For high-core
count GPUs like the RTX 3050 Ada mobile and RTX 4090, consider increasing thread blocks to fully utilize
their capabilities while ensuring optimal use of resources.

5. Visualize the Results

You can visualize the results using libraries such as OpenCV or by writing out the pixel values to a PPM
image file format for viewing in standard image editors.

This provides a basic framework for rendering Mandelbrot set images with CUDA C on NVIDIA GPUs, leveraging
their parallel processing power for faster computation. Adjust the code based on your specific requirements
and GPU architecture for optimal performance.

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CPU temp reaches 98c with liquid cooling at 43c at 45% utilization, 5.4GHz on stock 13900K

<img width="2049" alt="Image" src="https://github.com/user-attachments/assets/5da753c1-43b7-443d-a500-dadae2955a9b" />