Look at the following code:

# o1 mini - Segmented Sieve of Eratosthenes GPU acceleration
import numpy as np
import time
import pyopencl as cl
import math

def popcount32(arr, bits_to_count):
    """
    Perform a population count (number of set bits) on a NumPy array of uint32,
    considering only up to bits_to_count bits.

    Parameters:
        arr (np.ndarray): NumPy array with dtype=np.uint32.
        bits_to_count (int): Total number of bits to count.

    Returns:
        int: Total number of set bits.
    """
    # Copy the array to avoid modifying the original
    x = arr.copy()

    # Step 1: Subtract adjacent bits
    x = x - ((x >> 1) & 0x55555555)

    # Step 2: Sum pairs of bits
    x = (x & 0x33333333) + ((x >> 2) & 0x33333333)

    # Step 3: Sum nibbles (4 bits)
    x = (x + (x >> 4)) & 0x0F0F0F0F

    # Step 4: Multiply by a magic number and extract byte counts
    x = x * 0x01010101
    counts = (x >> 24) & 0xFF

    # Initial total count
    total = counts.sum()

    # Handle remaining bits if bits_to_count is not a multiple of 32
    remainder = bits_to_count % 32
    if remainder != 0:
        # Number of full 32-bit words
        full_words = bits_to_count // 32
        # Extract the last relevant word
        if full_words < len(arr):
            last_word = arr[full_words]
        else:
            last_word = 0
        # Create a mask for the relevant bits
        mask = (1 << remainder) - 1
        # Apply the mask
        last_word_masked = last_word & mask
        # Recompute popcount for the masked last word
        x_last = last_word_masked
        x_last = x_last - ((x_last >> 1) & 0x55555555)
        x_last = (x_last & 0x33333333) + ((x_last >> 2) & 0x33333333)
        x_last = (x_last + (x_last >> 4)) & 0x0F0F0F0F
        x_last = x_last * 0x01010101
        count_last = (x_last >> 24) & 0xFF

        # Adjust the total count
        if full_words > 0:
            total = counts[:full_words].sum()
        else:
            total = 0
        total += count_last

    return int(total)

def sieve_simple(limit):
    """
    Perform a simple sieve of Eratosthenes up to 'limit' using half the memory by considering only odd numbers.
    Returns an array of prime numbers up to 'limit'.
    Includes the prime number 2.

    Parameters:
        limit (int): The upper limit to sieve primes up to.

    Returns:
        np.ndarray: Array of prime numbers up to 'limit'.
    """
    if limit < 2:
        return np.array([], dtype=np.uint32)
    
    sieve_size = (limit // 2) + 1  # Only odd numbers
    sieve = np.ones(sieve_size, dtype=np.uint32)
    sieve[0] = 0  # 1 is not prime

    sqrt_limit = int(math.isqrt(limit))
    for num in range(3, sqrt_limit + 1, 2):
        if sieve[num // 2]:
            start_index = (num * num) // 2
            sieve[start_index : sieve_size : num] = 0

    primes = np.nonzero(sieve)[0] * 2 + 1
    primes = primes[primes <= limit]

    # Include prime number 2
    if limit >= 2:
        primes = np.insert(primes, 0, 2)

    return primes

def count_primes_with_gpu(n):
    """
    Count the number of primes up to 'n' using the Sieve of Eratosthenes
    with GPU acceleration and advanced optimizations.

    Parameters:
        n (int): The upper limit to count primes up to.

    Returns:
        int: Number of primes up to 'n'.
    """
    # Step 1: Find small primes up to sqrt(n)
    sqrt_n = int(math.isqrt(n))
    small_primes = sieve_simple(sqrt_n)
    print(f"Number of small primes up to sqrt(n)={sqrt_n}: {len(small_primes)}")

    # Step 2: Initialize OpenCL
    platforms = cl.get_platforms()
    if not platforms:
        raise RuntimeError("No OpenCL platforms found.")

    # Select the first platform with a GPU device
    device = None
    for platform in platforms:
        devices = platform.get_devices(device_type=cl.device_type.GPU)
        if devices:
            device = devices[0]
            break

    if device is None:
        raise RuntimeError("No GPU device found on any platform.")

    ctx = cl.Context([device])
    # Create a command queue with profiling enabled
    queue = cl.CommandQueue(ctx, properties=cl.command_queue_properties.PROFILING_ENABLE)

    # Step 3: Define the OpenCL kernel with atomic operations
    kernel_code = """
    __kernel void sieve_of_eratosthenes(__global unsigned int *segment,
                                        __global const unsigned int *small_primes,
                                        const unsigned int num_small_primes,
                                        const unsigned int segment_start,
                                        const unsigned int segment_size,
                                        const unsigned int max_n)
    {
        int idx = get_global_id(0);
        if (idx >= num_small_primes) return;

        unsigned int p = small_primes[idx];

        // Find the first multiple of p within the segment
        unsigned int start = (p * p > segment_start) ? p * p : segment_start + ((p - (segment_start % p)) % p);
        
        // Ensure start is odd
        if (start % 2 == 0)
            start += p;

        // Iterate over multiples of p within the segment
        for(unsigned int multiple = start; multiple < segment_start + segment_size && multiple <= max_n; multiple += p * 2)
        {
            if (multiple < segment_start)
                continue;
            unsigned int index = (multiple - segment_start) / 2;
            if(index >= (segment_size + 1) / 2)
                break;
            
            // Calculate word and bit indices
            unsigned int word_idx = index / 32;
            unsigned int bit_idx = index % 32;
            unsigned int mask = ~(1u << bit_idx);
            
            // Atomic AND operation to clear the bit
            atomic_and(&segment[word_idx], mask);
        }
    }
    """

    try:
        program = cl.Program(ctx, kernel_code).build()
    except cl.RuntimeError as e:
        # Retrieve and print the build log directly from the program
        try:
            build_log = program.get_build_info(device, cl.program_build_info.LOG)
        except:
            build_log = "No build log available."
        print("Build log:")
        print(build_log)
        raise

    sieve_kernel = program.sieve_of_eratosthenes

    # Step 4: Segmented Sieve Parameters
    segment_size = 10_000_000  # 10 million numbers per segment
    remaining_numbers = n - sqrt_n
    num_segments = (remaining_numbers + segment_size -1) // segment_size
    print(f"Total segments to process: {num_segments}")

    # Step 5: Allocate Odd Small Primes Buffer (exclude 2)
    # Exclude 2 to optimize since even numbers are already excluded in sieve segments
    odd_small_primes = small_primes[small_primes != 2]
    print(f"Number of odd small primes (excluding 2): {len(odd_small_primes)}")
    small_primes_np = np.array(odd_small_primes, dtype=np.uint32)
    small_primes_buffer = cl.Buffer(ctx, cl.mem_flags.READ_ONLY | cl.mem_flags.COPY_HOST_PTR, hostbuf=small_primes_np)

    # Step 6: Initialize Total Prime Count (includes all small primes, including 2)
    total_primes = len(small_primes)  # 'small_primes' includes 2

    # Step 7: Process Each Segment
    for seg_idx in range(num_segments):
        low = sqrt_n + 1 + seg_idx * segment_size  # Start strictly above sqrt_n
        high = min(low + segment_size, n + 1)

        # Ensure low is odd and >=3
        if low < 3:
            low = 3
        if low % 2 == 0:
            low += 1
        current_segment_size = high - low
        sieve_bits = (current_segment_size + 1) // 2  # Only odd numbers
        sieve_words = (sieve_bits + 31) // 32  # Ceiling division for 32-bit words

        # Initialize the segment sieve: all bits set to 1 (each bit represents an odd number)
        sieve = np.full(sieve_words, 0xFFFFFFFF, dtype=np.uint32)

        # Create buffer for the segment sieve
        sieve_buffer = cl.Buffer(ctx, cl.mem_flags.READ_WRITE | cl.mem_flags.COPY_HOST_PTR, hostbuf=sieve)

        # Set global work size
        global_work_size = len(odd_small_primes)

        # Set kernel arguments
        sieve_kernel.set_args(
            sieve_buffer,
            small_primes_buffer,
            np.uint32(len(odd_small_primes)),  # arg#3: num_small_primes
            np.uint32(low),                    # arg#4: segment_start
            np.uint32(current_segment_size),   # arg#5: segment_size
            np.uint32(n)                       # arg#6: max_n
        )

        # Enqueue the kernel
        try:
            event = cl.enqueue_nd_range_kernel(
                queue,
                sieve_kernel,
                (global_work_size,),
                None  # Let OpenCL decide the local work size
            )
        except cl.RuntimeError as e:
            print(f"Error enqueueing kernel on segment {seg_idx}: {e}")
            build_log = program.get_build_info(device, cl.program_build_info.LOG)
            print("Build log:")
            print(build_log)
            raise

        # Read back the sieve data after kernel execution
        sieve_host = np.empty_like(sieve)
        try:
            cl.enqueue_copy(queue, sieve_host, sieve_buffer, is_blocking=True, wait_for=[event])
        except cl.RuntimeError as e:
            print(f"Error copying data from GPU on segment {seg_idx}: {e}")
            raise

        # Calculate the maximum number in this segment
        max_num = high - 1  # since range is [low, high)
        max_num = min(max_num, n)

        # Calculate the number of bits to count
        bits_to_count = (max_num - low) // 2 + 1  # Only odd numbers

        # Ensure bits_to_count does not exceed sieve_bits
        bits_to_count = min(bits_to_count, sieve_bits)

        # Count the number of set bits (primes) in the segment using popcount32
        primes_in_segment = popcount32(sieve_host, bits_to_count)

        # Add to the total prime count
        total_primes += primes_in_segment

        # Optional: Progress update
        progress = (seg_idx + 1) / num_segments * 100
        print(f"Processed segment [{low}, {high}): {primes_in_segment} primes found. Progress: {progress:.2f}%")

    return total_primes

# Example Usage
if __name__ == "__main__":
    test_values = [100, 1000, 10000, 10000000]  # Add more values as needed

    for n in test_values:
        print("\n" + "-"*50)
        print(f"Counting primes up to {n}")
        start_time = time.time()
        prime_count = count_primes_with_gpu(n)
        end_time = time.time()
        print(f"Total number of primes up to {n}: {prime_count}")
        print(f"Execution time: {end_time - start_time:.2f} seconds")

I am not getting the expected performance boost from gpu acceleration.
Please optimize the code to improve gpu utilization and memory access patterns.

Here are my gpu specs:

APU - AMD Radeon (TM) Graphics - Primary/Integrated
VRAM - 512 MB - DDR4 1333 MHz
Graphics - AMD Radeon (TM) Graphics
Graphics Manufacturer - Powered by AMD
Usable Memory Size - 512 MB
Core Clock - 1800 MHz
Memory Type - DDR4
Memory Clock - 1333 MHz
Memory Bit Rate - 2.67 Gbps
Total Memory Bandwidth - 21 GByte/s
BIOS Part Number - 113-BARCELO-004
BIOS Version - 017.010.000.031
BIOS Date - 2022/02/23 06:22
Device ID - 15E7
Revision ID - C2
Vendor ID - 1002
SubSystem ID - 8999
SubSystem Vendor ID - 103C
Bus Type - PCI
Current Bus Settings - PCI
Driver Version - 23.19.10.07-240520a-403378C-HP
AMD Windows Driver Version - 31.0.21910.7001
Direct3D API Version - 12.1
Vulkan™ API Version - 1.3.260
OpenCL™ API Version - 2.0
OpenGL® API Version - 4.6
Direct3D® Driver Version - 9.14.10.01526
Vulkan™ Driver Version - 2.0.279
OpenCL® Driver Version - 31.0.21910.7001
OpenGL® Driver Version - 24.05.230729_569461f
2D Driver Version - 8.1.1.1634
2D Driver File Path - /REGISTRY/MACHINE/SYSTEM/CurrentControlSet/Control/Class/{4d36e968-e325-11ce-bfc1-08002be10318}/0000
UI Version - 2024.0220.1122.1996
AMD Audio Driver Version - Not Available
Driver Provider - Advanced Micro Devices, Inc.
Windows Edition - Windows 11 CoreSingleLanguage (64 bit)
Windows Version - 23H2