Hardware Acceleration & GPU Programming

Modern Mobile GPU Architecture

GPU vs CPU Performance Characteristics

Performance Comparison:
┌─────────────────┬─────────────┬─────────────┐
│ Aspect          │ CPU         │ GPU         │
├─────────────────┼─────────────┼─────────────┤
│ Cores           │ 4-12        │ 1000-3000   │
│ Clock Speed     │ 2-3 GHz     │ 400-800 MHz │
│ Memory          │ Large Cache │ High BW     │
│ Parallelism     │ Task        │ Data        │
│ Power Efficiency│ Variable    │ High        │
└─────────────────┴─────────────┴─────────────┘

Android Hardware Acceleration

RenderScript for Parallel Processing

// rs_blur_kernel.rs
#pragma version(1)
#pragma rs java_package_name(com.example.graphics)

rs_allocation input;
rs_allocation output;
int width;
int height;
float radius;

void RS_KERNEL blur_kernel(uint32_t x, uint32_t y) {
    float4 sum = 0;
    int count = 0;
    
    int radiusInt = (int)radius;
    
    for (int dy = -radiusInt; dy <= radiusInt; dy++) {
        for (int dx = -radiusInt; dx <= radiusInt; dx++) {
            int nx = x + dx;
            int ny = y + dy;
            
            if (nx >= 0 && nx < width && ny >= 0 && ny < height) {
                float4 pixel = rsUnpackColor8888(
                    rsGetElementAt_uint32_t(input, nx, ny)
                );
                sum += pixel;
                count++;
            }
        }
    }
    
    sum /= count;
    rsSetElementAt_uint32_t(output, rsPackColorTo8888(sum), x, y);
}

// Kotlin RenderScript Integration
class GPUImageProcessor(private val context: Context) {
    private val renderScript = RenderScript.create(context)
    private lateinit var blurScript: ScriptC_rs_blur_kernel
    
    init {
        blurScript = ScriptC_rs_blur_kernel(renderScript)
    }
    
    fun applyGaussianBlur(bitmap: Bitmap, radius: Float): Bitmap {
        val inputAllocation = Allocation.createFromBitmap(renderScript, bitmap)
        val outputAllocation = Allocation.createTyped(renderScript, inputAllocation.type)
        
        // Set script parameters
        blurScript._input = inputAllocation
        blurScript._output = outputAllocation
        blurScript._width = bitmap.width
        blurScript._height = bitmap.height
        blurScript._radius = radius
        
        // Launch kernel
        blurScript.forEach_blur_kernel(outputAllocation)
        
        // Copy result back
        val outputBitmap = Bitmap.createBitmap(
            bitmap.width, 
            bitmap.height, 
            Bitmap.Config.ARGB_8888
        )
        outputAllocation.copyTo(outputBitmap)
        
        // Cleanup
        inputAllocation.destroy()
        outputAllocation.destroy()
        
        return outputBitmap
    }
    
    fun cleanup() {
        blurScript?.destroy()
        renderScript.destroy()
    }
}

Vulkan API Implementation

class VulkanRenderer {
    private var instance: Long = 0
    private var device: Long = 0
    private var commandPool: Long = 0
    private var queue: Long = 0
    
    external fun createVulkanInstance(): Long
    external fun createDevice(instance: Long): Long
    external fun createCommandPool(device: Long): Long
    external fun submitCommands(commandPool: Long, commands: ByteArray)
    
    companion object {
        init {
            System.loadLibrary("vulkan-renderer")
        }
    }
    
    fun initialize(): Boolean {
        return try {
            instance = createVulkanInstance()
            device = createDevice(instance)
            commandPool = createCommandPool(device)
            true
        } catch (e: Exception) {
            Log.e("Vulkan", "Initialization failed", e)
            false
        }
    }
    
    fun renderFrame(vertices: FloatArray, indices: IntArray) {
        val commandBuffer = buildCommandBuffer(vertices, indices)
        submitCommands(commandPool, commandBuffer)
    }
    
    private fun buildCommandBuffer(vertices: FloatArray, indices: IntArray): ByteArray {
        // Build Vulkan command buffer
        // This would typically be done in native code
        return byteArrayOf()
    }
}

// Native Vulkan Implementation (vulkan-renderer.cpp)
#include <jni.h>
#include <vulkan/vulkan.h>
#include <android/log.h>

#define LOG_TAG "VulkanRenderer"
#define LOGI(...) __android_log_print(ANDROID_LOG_INFO, LOG_TAG, __VA_ARGS__)
#define LOGE(...) __android_log_print(ANDROID_LOG_ERROR, LOG_TAG, __VA_ARGS__)

extern "C" {

JNIEXPORT jlong JNICALL
Java_com_example_VulkanRenderer_createVulkanInstance(JNIEnv *env, jobject thiz) {
    VkApplicationInfo appInfo{};
    appInfo.sType = VK_STRUCTURE_TYPE_APPLICATION_INFO;
    appInfo.pApplicationName = "Mobile Graphics App";
    appInfo.applicationVersion = VK_MAKE_VERSION(1, 0, 0);
    appInfo.pEngineName = "Custom Engine";
    appInfo.engineVersion = VK_MAKE_VERSION(1, 0, 0);
    appInfo.apiVersion = VK_API_VERSION_1_0;

    VkInstanceCreateInfo createInfo{};
    createInfo.sType = VK_STRUCTURE_TYPE_INSTANCE_CREATE_INFO;
    createInfo.pApplicationInfo = &appInfo;

    VkInstance instance;
    VkResult result = vkCreateInstance(&createInfo, nullptr, &instance);
    
    if (result != VK_SUCCESS) {
        LOGE("Failed to create Vulkan instance: %d", result);
        return 0;
    }
    
    LOGI("Vulkan instance created successfully");
    return reinterpret_cast<jlong>(instance);
}

JNIEXPORT jlong JNICALL
Java_com_example_VulkanRenderer_createDevice(JNIEnv *env, jobject thiz, jlong instancePtr) {
    VkInstance instance = reinterpret_cast<VkInstance>(instancePtr);
    
    // Enumerate physical devices
    uint32_t deviceCount = 0;
    vkEnumeratePhysicalDevices(instance, &deviceCount, nullptr);
    
    if (deviceCount == 0) {
        LOGE("No Vulkan-compatible devices found");
        return 0;
    }
    
    std::vector<VkPhysicalDevice> devices(deviceCount);
    vkEnumeratePhysicalDevices(instance, &deviceCount, devices.data());
    
    // Select first suitable device (simplified)
    VkPhysicalDevice physicalDevice = devices[0];
    
    // Create logical device
    float queuePriority = 1.0f;
    VkDeviceQueueCreateInfo queueCreateInfo{};
    queueCreateInfo.sType = VK_STRUCTURE_TYPE_DEVICE_QUEUE_CREATE_INFO;
    queueCreateInfo.queueFamilyIndex = 0; // Simplified
    queueCreateInfo.queueCount = 1;
    queueCreateInfo.pQueuePriorities = &queuePriority;
    
    VkDeviceCreateInfo createInfo{};
    createInfo.sType = VK_STRUCTURE_TYPE_DEVICE_CREATE_INFO;
    createInfo.queueCreateInfoCount = 1;
    createInfo.pQueueCreateInfos = &queueCreateInfo;
    
    VkDevice device;
    VkResult result = vkCreateDevice(physicalDevice, &createInfo, nullptr, &device);
    
    if (result != VK_SUCCESS) {
        LOGE("Failed to create Vulkan device: %d", result);
        return 0;
    }
    
    LOGI("Vulkan device created successfully");
    return reinterpret_cast<jlong>(device);
}

}

iOS Metal Framework Integration

Metal Performance Shaders (MPS)

import Metal
import MetalPerformanceShaders

class MetalImageProcessor {
    private let device: MTLDevice
    private let commandQueue: MTLCommandQueue
    private let library: MTLLibrary
    
    init() {
        guard let device = MTLCreateSystemDefaultDevice(),
              let commandQueue = device.makeCommandQueue(),
              let library = device.makeDefaultLibrary() else {
            fatalError("Metal not supported on this device")
        }
        
        self.device = device
        self.commandQueue = commandQueue
        self.library = library
    }
    
    func applyGaussianBlur(to texture: MTLTexture, radius: Float) -> MTLTexture? {
        guard let commandBuffer = commandQueue.makeCommandBuffer() else {
            return nil
        }
        
        // Create output texture
        let descriptor = MTLTextureDescriptor.texture2DDescriptor(
            pixelFormat: texture.pixelFormat,
            width: texture.width,
            height: texture.height,
            mipmapped: false
        )
        descriptor.usage = [.shaderRead, .shaderWrite]
        
        guard let outputTexture = device.makeTexture(descriptor: descriptor) else {
            return nil
        }
        
        // Apply Gaussian blur using MPS
        let gaussianBlur = MPSImageGaussianBlur(device: device, sigma: radius)
        gaussianBlur.encode(
            commandBuffer: commandBuffer,
            sourceTexture: texture,
            destinationTexture: outputTexture
        )
        
        commandBuffer.commit()
        commandBuffer.waitUntilCompleted()
        
        return outputTexture
    }
    
    func performMatrixMultiplication(matrixA: [Float], matrixB: [Float], 
                                   rowsA: Int, colsA: Int, colsB: Int) -> [Float]? {
        guard let commandBuffer = commandQueue.makeCommandBuffer() else {
            return nil
        }
        
        // Create buffers
        guard let bufferA = device.makeBuffer(bytes: matrixA, 
                                            length: matrixA.count * MemoryLayout<Float>.size,
                                            options: []),
              let bufferB = device.makeBuffer(bytes: matrixB,
                                            length: matrixB.count * MemoryLayout<Float>.size,
                                            options: []),
              let resultBuffer = device.makeBuffer(length: rowsA * colsB * MemoryLayout<Float>.size,
                                                 options: []) else {
            return nil
        }
        
        // Use MPS matrix multiplication
        let matrixMult = MPSMatrixMultiplication(
            device: device,
            transposeLeft: false,
            transposeRight: false,
            resultRows: rowsA,
            resultColumns: colsB,
            interiorColumns: colsA,
            alpha: 1.0,
            beta: 0.0
        )
        
        let descA = MPSMatrixDescriptor(rows: rowsA, columns: colsA, 
                                      rowBytes: colsA * MemoryLayout<Float>.size,
                                      dataType: .float32)
        let descB = MPSMatrixDescriptor(rows: colsA, columns: colsB,
                                      rowBytes: colsB * MemoryLayout<Float>.size,
                                      dataType: .float32)
        let descResult = MPSMatrixDescriptor(rows: rowsA, columns: colsB,
                                           rowBytes: colsB * MemoryLayout<Float>.size,
                                           dataType: .float32)
        
        let mpsMatrixA = MPSMatrix(buffer: bufferA, descriptor: descA)
        let mpsMatrixB = MPSMatrix(buffer: bufferB, descriptor: descB)
        let mpsResult = MPSMatrix(buffer: resultBuffer, descriptor: descResult)
        
        matrixMult.encode(commandBuffer: commandBuffer,
                         leftMatrix: mpsMatrixA,
                         rightMatrix: mpsMatrixB,
                         resultMatrix: mpsResult)
        
        commandBuffer.commit()
        commandBuffer.waitUntilCompleted()
        
        // Extract result
        let resultPointer = resultBuffer.contents().bindMemory(to: Float.self, capacity: rowsA * colsB)
        return Array(UnsafeBufferPointer(start: resultPointer, count: rowsA * colsB))
    }
}

Custom Metal Compute Shaders

// Shaders.metal
#include <metal_stdlib>
using namespace metal;

// Parallel reduction for sum calculation
kernel void parallel_sum(device const float* input [[buffer(0)]],
                        device float* output [[buffer(1)]],
                        constant uint& count [[buffer(2)]],
                        uint gid [[thread_position_in_grid]],
                        uint threads_per_group [[threads_per_threadgroup]],
                        uint group_id [[threadgroup_position_in_grid]]) {
    
    threadgroup float shared_memory[256];
    
    uint tid = gid;
    uint local_id = tid % threads_per_group;
    
    // Load data into shared memory
    shared_memory[local_id] = (tid < count) ? input[tid] : 0.0f;
    threadgroup_barrier(mem_flags::mem_threadgroup);
    
    // Parallel reduction
    for (uint stride = threads_per_group / 2; stride > 0; stride >>= 1) {
        if (local_id < stride) {
            shared_memory[local_id] += shared_memory[local_id + stride];
        }
        threadgroup_barrier(mem_flags::mem_threadgroup);
    }
    
    // Write result
    if (local_id == 0) {
        output[group_id] = shared_memory[0];
    }
}

// Image convolution kernel
kernel void convolution_2d(texture2d<float, access::read> input_texture [[texture(0)]],
                          texture2d<float, access::write> output_texture [[texture(1)]],
                          constant float* kernel [[buffer(0)]],
                          constant int& kernel_size [[buffer(1)]],
                          uint2 gid [[thread_position_in_grid]]) {
    
    if (gid.x >= output_texture.get_width() || gid.y >= output_texture.get_height()) {
        return;
    }
    
    float4 sum = float4(0.0);
    int half_kernel = kernel_size / 2;
    
    for (int dy = -half_kernel; dy <= half_kernel; dy++) {
        for (int dx = -half_kernel; dx <= half_kernel; dx++) {
            uint2 coord = uint2(int(gid.x) + dx, int(gid.y) + dy);
            
            if (coord.x < input_texture.get_width() && coord.y < input_texture.get_height()) {
                float4 pixel = input_texture.read(coord);
                float weight = kernel[(dy + half_kernel) * kernel_size + (dx + half_kernel)];
                sum += pixel * weight;
            }
        }
    }
    
    output_texture.write(sum, gid);
}

// Metal Compute Pipeline
class MetalComputeEngine {
    private let device: MTLDevice
    private let commandQueue: MTLCommandQueue
    private let library: MTLLibrary
    
    init() {
        guard let device = MTLCreateSystemDefaultDevice(),
              let commandQueue = device.makeCommandQueue(),
              let library = device.makeDefaultLibrary() else {
            fatalError("Metal initialization failed")
        }
        
        self.device = device
        self.commandQueue = commandQueue
        self.library = library
    }
    
    func parallelSum(data: [Float]) -> Float {
        guard let function = library.makeFunction(name: "parallel_sum"),
              let pipeline = try? device.makeComputePipelineState(function: function),
              let commandBuffer = commandQueue.makeCommandBuffer(),
              let encoder = commandBuffer.makeComputeCommandEncoder() else {
            return 0.0
        }
        
        // Create buffers
        let inputBuffer = device.makeBuffer(bytes: data, 
                                          length: data.count * MemoryLayout<Float>.size,
                                          options: [])!
        
        let outputCount = (data.count + 255) / 256  // Number of thread groups
        let outputBuffer = device.makeBuffer(length: outputCount * MemoryLayout<Float>.size,
                                           options: [])!
        
        var count = UInt32(data.count)
        let countBuffer = device.makeBuffer(bytes: &count,
                                          length: MemoryLayout<UInt32>.size,
                                          options: [])!
        
        // Configure compute encoder
        encoder.setComputePipelineState(pipeline)
        encoder.setBuffer(inputBuffer, offset: 0, index: 0)
        encoder.setBuffer(outputBuffer, offset: 0, index: 1)
        encoder.setBuffer(countBuffer, offset: 0, index: 2)
        
        // Dispatch threads
        let threadsPerGroup = MTLSize(width: 256, height: 1, depth: 1)
        let numGroups = MTLSize(width: outputCount, height: 1, depth: 1)
        
        encoder.dispatchThreadgroups(numGroups, threadsPerThreadgroup: threadsPerGroup)
        encoder.endEncoding()
        
        commandBuffer.commit()
        commandBuffer.waitUntilCompleted()
        
        // Sum partial results on CPU (could be further optimized)
        let resultPointer = outputBuffer.contents().bindMemory(to: Float.self, capacity: outputCount)
        var totalSum: Float = 0.0
        for i in 0..<outputCount {
            totalSum += resultPointer[i]
        }
        
        return totalSum
    }
}

Performance Monitoring & Optimization

GPU Memory Management

// Android GPU Memory Tracking
class GPUMemoryManager {
    private val memoryUsage = mutableMapOf<String, Long>()
    private val activityManager = context.getSystemService(Context.ACTIVITY_SERVICE) as ActivityManager
    
    fun trackTextureMemory(name: String, width: Int, height: Int, bytesPerPixel: Int) {
        val memorySize = (width * height * bytesPerPixel).toLong()
        memoryUsage[name] = memorySize
        
        logMemoryUsage()
    }
    
    fun releaseTexture(name: String) {
        memoryUsage.remove(name)
        logMemoryUsage()
    }
    
    private fun logMemoryUsage() {
        val totalGPUMemory = memoryUsage.values.sum()
        val memInfo = ActivityManager.MemoryInfo()
        activityManager.getMemoryInfo(memInfo)
        
        Log.d("GPU Memory", "Total GPU texture memory: ${totalGPUMemory / (1024 * 1024)} MB")
        Log.d("GPU Memory", "Available system memory: ${memInfo.availMem / (1024 * 1024)} MB")
        
        if (totalGPUMemory > memInfo.availMem * 0.1) { // 10% of available memory
            Log.w("GPU Memory", "High GPU memory usage detected - consider texture compression")
        }
    }
    
    fun optimizeTextureFormats(): Map<String, String> {
        return mapOf(
            "RGBA8888" to "Use for high-quality images with transparency",
            "RGB565" to "Use for opaque images to save 50% memory",
            "ETC2" to "Use compressed format for Android (OpenGL ES 3.0+)",
            "ASTC" to "Use for high-end devices with ASTC support",
            "S3TC/DXT" to "Use for desktop/high-end mobile GPUs"
        )
    }
}

GPU Bellek Yönetimi ve Optimizasyon

Bellek Yönetimi Stratejileri

// Android GPU Bellek Yönetimi
class GPUBellekYoneticisi {
    private val bellekKullanim = mutableMapOf<String, Long>()
    private val aktiviteYoneticisi = context.getSystemService(Context.ACTIVITY_SERVICE) as ActivityManager
    
    fun dokuBellekTakibi(isim: String, genislik: Int, yukseklik: Int, pikselBasinaByte: Int) {
        val bellekBoyutu = (genislik * yukseklik * pikselBasinaByte).toLong()
        bellekKullanim[isim] = bellekBoyutu
        
        bellekKullaniminiLogla()
    }
    
    fun dokuyuSerbestBirak(isim: String) {
        bellekKullanim.remove(isim)
        bellekKullaniminiLogla()
    }
    
    private fun bellekKullaniminiLogla() {
        val toplamGPUBellek = bellekKullanim.values.sum()
        val bellekBilgisi = ActivityManager.MemoryInfo()
        aktiviteYoneticisi.getMemoryInfo(bellekBilgisi)
        
        Log.d("GPU Bellek", "Toplam GPU doku belleği: ${toplamGPUBellek / (1024 * 1024)} MB")
        Log.d("GPU Bellek", "Kullanılabilir sistem belleği: ${bellekBilgisi.availMem / (1024 * 1024)} MB")
        
        if (toplamGPUBellek > bellekBilgisi.availMem * 0.1) { // Kullanılabilir belleğin %10'u
            Log.w("GPU Bellek", "Yüksek GPU bellek kullanımı tespit edildi - doku sıkıştırma düşünülebilir")
        }
    }
    
    fun dokuFormatlariniOptimizeEt(): Map<String, String> {
        return mapOf(
            "RGBA8888" to "Şeffaflık içeren yüksek kaliteli görüntüler için kullan",
            "RGB565" to "Şeffaf olmayan görüntüler için %50 bellek tasarrufu sağlar",
            "ETC2" to "Android için sıkıştırılmış format (OpenGL ES 3.0+)",
            "ASTC" to "ASTC destekli üst düzey cihazlar için kullan",
            "S3TC/DXT" to "Masaüstü/üst düzey mobil GPU'lar için kullan"
        )
    }
}

Asenkron GPU İşlemleri

// Android - Asenkron GPU işleme
class AsenkronGPUIsleyici {
    private val glExecutor = Executors.newSingleThreadExecutor()
    private val hesaplamaExecutor = Executors.newFixedThreadPool(2)
    
    fun asenkronIsle(
        girdiVerisi: FloatArray,
        geriCagri: (FloatArray) -> Unit
    ) {
        hesaplamaExecutor.submit {
            try {
                val sonuc = GPUdaIsle(girdiVerisi)
                
                // Sonucu ana thread'e döndür
                Handler(Looper.getMainLooper()).post {
                    geriCagri(sonuc)
                }
            } catch (e: Exception) {
                Log.e("AsenkronGPU", "İşleme başarısız", e)
            }
        }
    }
    
    private fun GPUdaIsle(veri: FloatArray): FloatArray {
        // GPU işleme implementasyonu
        return veri.map { it * 2.0f }.toFloatArray()
    }
    
    fun topluIsle(
        topluVeriler: List<FloatArray>,
        ilerlemeGeriCagri: (Int, Int) -> Unit,
        tamamlanmaGeriCagri: (List<FloatArray>) -> Unit
    ) {
        val sonuclar = mutableListOf<FloatArray>()
        val toplamToplu = topluVeriler.size
        
        topluVeriler.forEachIndexed { index, toplu ->
            hesaplamaExecutor.submit {
                val sonuc = GPUdaIsle(toplu)
                
                synchronized(sonuclar) {
                    sonuclar.add(sonuc)
                    
                    Handler(Looper.getMainLooper()).post {
                        ilerlemeGeriCagri(index + 1, toplamToplu)
                        
                        if (sonuclar.size == toplamToplu) {
                            tamamlanmaGeriCagri(sonuclar.toList())
                        }
                    }
                }
            }
        }
    }
}

Donanım Tespiti ve Yedekleme Stratejileri

Yetenek Tespiti

// Çapraz platform donanım yetenek tespiti
interface DonanimYetenekleri {
  GPUvar: boolean;
  NPUvar: boolean;
  DSPvar: boolean;
  gpuBellek: number;
  hesaplamaBirimleri: number;
  desteklenenFormatlar: string[];
}

class DonanimTespitci {
  static async yetenekleriAl(): Promise<DonanimYetenekleri> {
    if (Platform.OS === 'ios') {
      return await this.iOSYetenekleriniAl();
    } else {
      return await this.androidYetenekleriniAl();
    }
  }
  
  private static async iOSYetenekleriniAl(): Promise<DonanimYetenekleri> {
    const yetenekler = await NativeModules.DonanimTespitci.iOSYetenekleriniAl();
    
    return {
      GPUvar: yetenekler.GPUvar,
      NPUvar: yetenekler.neuralEngineVar,
      DSPvar: yetenekler.imageSignalProcessorVar,
      gpuBellek: yetenekler.gpuBellek,
      hesaplamaBirimleri: yetenekler.gpuCekirdekleri,
      desteklenenFormatlar: yetenekler.desteklenenPikselFormatlari
    };
  }
  
  private static async androidYetenekleriniAl(): Promise<DonanimYetenekleri> {
    const yetenekler = await NativeModules.DonanimTespitci.androidYetenekleriniAl();
    
    return {
      GPUvar: yetenekler.GPUvar,
      NPUvar: yetenekler.NNAPIvar,
      DSPvar: yetenekler.hexagonDSPvar,
      gpuBellek: yetenekler.gpuBellek,
      hesaplamaBirimleri: yetenekler.gpuCekirdekleri,
      desteklenenFormatlar: yetenekler.desteklenenFormatlar
    };
  }
  
  static optimalIsleyiciSec(
    yetenekler: DonanimYetenekleri,
    gorevTipi: 'goruntu' | 'ml' | 'hesaplama'
  ): IsleyiciTipi {
    switch (gorevTipi) {
      case 'goruntu':
        return yetenekler.GPUvar ? 'gpu' : 'cpu';
      case 'ml':
        return yetenekler.NPUvar ? 'npu' : 
               yetenekler.GPUvar ? 'gpu' : 'cpu';
      case 'hesaplama':
        return yetenekler.DSPvar ? 'dsp' :
               yetenekler.GPUvar ? 'gpu' : 'cpu';
      default:
        return 'cpu';
    }
  }
}

type IsleyiciTipi = 'cpu' | 'gpu' | 'npu' | 'dsp';

En İyi Uygulamalar

Donanım Hızlandırma Kılavuzu

1. Doğru hızlandırıcıyı seçin

   • GPU: Paralel grafik ve hesaplama işlemleri
   • NPU: Makine öğrenimi çıkarımı
   • DSP: Sinyal işleme ve ses

2. Bellek optimizasyonu

   • GPU tamponlarını havuzda tutun
   • CPU-GPU aktarımlarını minimize edin
   • Uygun bellek tiplerini kullanın

3. Asenkron işleme

   • Ana thread'i bloklamayın
   • Uygun hata yönetimi uygulayın
   • İlerleme geri bildirimi sağlayın

4. Yedekleme stratejileri

   • Donanım yeteneklerini tespit edin
   • CPU yedeklemeleri uygulayın
   • Zarif düşüş sağlayın

5. Güç verimliliği

   • Termal durumu izleyin
   • Performans ve pil ömrü dengesini koruyun
   • Uyarlanabilir kalite ayarları kullanın

Donanım hızlandırma, performans kritik mobil uygulamalar için gereklidir. Doğru uygulama, farklı cihaz yetenekleri arasında enerji verimliliğini korurken önemli performans iyileştirmeleri sağlayabilir.